Dr Payam Barnaghi


Professor of Machine Intelligence

Biography

Biography

I am Professor of Machine Intelligence at the Department of Electronic and Electrical Engineering and a member of the Institute for Communication Systems (ICS) and the Centre for Mathematical and Computational Biology (CMCB) at the University of Surrey.  I am a senior member of IEEE and a Fellow of the Higher Education Academy. I am technical lead of the Department of Health/NHS TIHM for Dementia and I was co-ordinator and Principal Investigator of the EU FP7 CityPulse project on smart cities and large-scale data analytics. I am an associate editor of the IEEE Transactions on Big Data and IEEE Internet of Things Journal, vice-chair of the IEEE SIG on Big Data Intelligent Networking, a member of the World Wide Web Consortium (W3C) Advisory Committee and the EPSRC Peer Review College. I received the FEPS Teacher of the Year Award (2017) at the Faculty of Engineering and Physical Sciences and an IEEE Outstanding Leadership Award in 2017.

Research interests

My main research goal is to develop intelligent information communication, discovery and retrieval methods for cyber-physical and social systems. I work on machine learning, Internet of Things (IoT), semantic web, service computing, adaptive algorithms, data-centric networking, big data, stream processing and information search and retrieval to solve problems and develop new technologies for the future Internet/Web and healthcare systems (Read more about our research).

Research collaborations

Project involvement

Teaching

  • Spring 2013
    • Mobile Applications and Web Services (this module will be delivered jointly with Prof. Klaus Moessner)
      • My lectures will be from week 5 to week 10 (additional and material will be available on SurreyLearn)- only the lecture notes will be made available here
      • Assignment (pdf handout)
      • Lecture notes:
        • Introduction (Part II)
        • Mobile Applications and Web Services - Part II (handout)
        • Introduction to the Semantic Web and metadata frameworks (handout)
    • Laboratories, Design & Professional Studies (LDPS) - Module description

      • LDPS- Technical Design Assignment, Band-Pass Filter and PCB Design (slides)
      • Assignment (submission date: Monday 18th February 2013)

Affiliations

Invited Talks and lectures

Keynote/Plenary talks

Invited Talks

Guest Lectures

My publications

Publications

Villalonga P, Bauer M, Huang V, Bernat J, Barnaghi P (2010) Modeling of Sensor Data and Context for the Real World Internet, Proceedings of 8th IEEE PERCOM Workshops IEEE
The Internet is expanding to reach the real world, integrating the physical world into the digital world in what is called the Real World Internet (RWI). Sensor and actuator networks deployed all over the Internet will play the role of collecting sensor data and context information from the physical world and integrating it into the future RWI. In this paper we present the SENSEI architecture approach for the RWI; a layered architecture composed of one or several context frameworks on top of a sensor framework, which allows the collection of sensor data as well as context information from the real world. We focus our discussion on how the modeling of information is done for different levels (sensor and context data), present a multi-layered information model, its representation and the mapping between its layers.
Kurian JC, Barnaghi PM, Hartley MI (2006) Semantics-based Dynamic Hypermedia Adaptation using the Hidden Markov Model., SEMPS 228 CEUR-WS.org
Wei W, Barnaghi P, Bargiela A (2011) Rational Research model for ranking semantic entities, Information Sciences
Presser M, Barnaghi P, Eurich M, Villalonga C (2008) The SENSEI project: integrating the physical world with the digital world of the network of the future - [global communications newsletter], pp. 1-4 IEEE
The Internet extends its reach to the real world through innovations collectively termed the Internet of Things (IoT). The IoT aims at integrating technologies such as radio frequency identification, wireless sensor and actuator networks (WSANs), and networked embedded devices. Recent ideas envision the Internet as an all encompassing infrastructure that connects the physical into the digital world: the real world Internet (RWI). The European project SENSEI plays a leading role within the current efforts to create an underlying architecture and services for the future Internet and to realize the vision of the RWI.
Barnaghi PM, Kareem SA (2005) Ontology-based multimedia presentation generation, IEEE Region 10 Annual International Conference, Proceedings/TENCON pp. 1-5 IEEE
Multimedia data are illusory entities for the machines. Their contents include interpretable data as well as binary representations. Understanding and accessing the content-driven information for multimedia objects allow us to design an efficient multimedia querying and retrieval system. In this paper, we propose a framework to represent the multimedia information and object roles in order to generate automatic multimedia presentations. The proposed architecture attempts to represent the semantic information and the relations amongst the multimedia objects in a disclosure domain. Thus, the system is domain dependent. The represented data associates with the presentation mechanisms to create an integrated presentation generation system. A multi-layer design defines the various levels of abstraction for the proposed framework.
Ganz F, Barnaghi P, Carrez F (2014) Automated Semantic Knowledge Acquisition From Sensor Data, IEEE Systems Journal
The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k-means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.
Barnaghi P, Moessner K, Presser M, Meissner S (2009) Preface, Lecture Notes in Computer Science 5741 pp. v-vi Springer
Barnaghi P, Wang W, Dong L, Wang C (2013) A Linked-data Model for Semantic Sensor Streams, Proceedings of 2013 IEEE Internet of Things (iThings) pp. 468-475 IEEE
This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes.
Wei W, Barnaghi P, Bargiela A (2011) Rational Research model for ranking semantic entities, Information Sciences 181 (13) pp. 2823-2840
Wang W, Barnaghi PM, Bargiela A (2007) Semantic-enhanced information search and retrieval, Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology pp. 218-223
Information Retrieval (IR) techniques have been extensively studied since late 1940s and achieved great success evidenced particularly by popular online search engines. However, various classical information retrieval models also have witnessed criticism for emphasizing computation with occurrence of words while ignoring semantics (i.e. meaning of words, search context and etc). Research of the Semantic Web in recent years has provided an opportunity to migrate from mere word-computing to semantic-enhanced information search and retrieval. In this paper, we describe a methodology by combing the Semantic Web technologies, information extraction and social network analysis techniques to elicit semantics from available data in order to develop a semantic-enhanced information search and retrieval system. © 2007 IEEE.
Barnaghi P, Abangar H, Tafazolli R, Moessner K, Nnaemego A, Balaskandan K (2010) A Service Oriented Middleware Architecture for Wireless Sensor Networks, Conference Proceedings of Future Network and MobileSummit 2010
Anantharam P, Sheth A, Barnaghi P (2013) Data processing and semantics for advanced internet of things (IoT) applications: Modeling, annotation, integration, and perception, ACM International Conference Proceeding Series
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying largescale IoT data, and AI applications for automated knowledge extraction from real world data. Copyright © 2013 ACM.
Barnaghi P, Moessner K, Presser M (2009) Smart Sensing and Context, Springer-Verlag New York Inc
This volume constitutes the revised papers of the 4th European Conference on Smart Sensing and Context, Euro SSC 2009, held in Guilford, UK, in September 2009.
Wei W, Barnaghi P (2009) Semantic Annotation and Reasoning for Sensor Data, Lecture Notes in Computer Science: Smart Sensing and Context 5741 pp. 66-76 Springer
Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web technologies to design future service and applications for the Web. In this paper we focus on the current developments and discussions on designing Semantic Sensor Web, particularly, we advocate the idea of semantic annotation with the existing authoritative data published on the semantic Web. Through illustrative examples, we demonstrate how rule-based reasoning can be performed over the sensor observation and measurement data and linked data to derive additional or approximate knowledge. Furthermore, we discuss the association between sensor data, the semantic Web, and the social Web which enable construction of context-aware applications and services, and contribute to construction of a networked knowledge framework.
Ganz F, Enshaeifar S, Puschmann D, Ahrabian A, Elsaleh T (2015) The Knowledge Acquisition Toolkit ? KAT (Extracting knowledge from sensor data), ICS/University of Surrey
Knowledge Acquisition Toolkit (KAT) is an open-source software that includes methods to process numerical sensory data. KAT is able to extract and represent human understandable and/or machine interpretable information from raw data.

KAT includes a collection of algorithms for each step of the Internet of Things (IoT) data processing workflow ranging from data and signal pre-processing algorithms such as Frequency Filters, dimensionality reduction techniques such as Wavelet, FFT, SAX, and Feature Extraction and Abstraction and Inference methods such as Clustering. Figure 1 shows the steps of the process chain for processing cyber-physical data on the Web. KAT can be used to design and evaluate algorithms for sensor data that aim to extract and find new insights from the data.

Ganz F, Barnaghi P, Carrez F (2014) Multi-resolution data communication in wireless sensor networks, 2014 IEEE World Forum on Internet of Things, WF-IoT 2014 pp. 571-574
There is an increasing trend in using data collected by sensor devices to enable better understanding of the physical world for humans and support the creation of pervasive environments for a wide range of applications in different domains such as smart cities, and intelligent transportation. However, the deluge of data created and communicated and the low-processing capabilities of the used sensor devices lead to bottlenecks in the processing and interpreting of the data. We introduce a data reduction approach that submits high-granular data in times of high activity in the sensor readings and low-granular data in times of low activity. We consider and discuss different methods to measure activity in the data and modify the symbolic aggregate approximation algorithm that uses a fixed window length to adapt the length according to the data activity for ultimately less data communication between sensor node and sink/gateway. We evaluate our approach over real-world data sets and show that reduction of data size while maintaining the features of the data can be achieved. © 2014 IEEE.
De S, Barnaghi P, Bauer M, Meissner S (2011) Service modelling for the Internet of Things, 2011 Federated Conference on Computer Science and Information Systems, FedCSIS 2011 pp. 949-955
The Internet of Things envisions a multitude of heterogeneous objects and interactions with the physical environment. The functionalities provided by these objects can be termed as 'real-world services' as they provide a near real-time state of the physical world. A structured, machine-processible approach to provision such real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the digital world. This paper presents a semantic modeling approach for different components in an IoT framework. It is also discussed how the model can be integrated into the IoT framework by using automated association mechanisms with physical entities and how the data can be discovered using semantic search and reasoning mechanisms. © 2011 Polish Info Processing Soc.
Serrano M, H.N.M. Quoc, Hauswirth M, Wang W, Barnaghi P, Cousin P (2013) Open Services for IoT Cloud Applications in the Future Internet, World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2013 IEEE 14th International Symposium and Workshops on a pp. 1-6
Sheth AP, Barnaghi PM, Strohmaier M, Jain R, Staab S (2013) Physical-Cyber-Social Computing (Dagstuhl Reports 13402)., 9 pp. 245-263
Ganz F, Barnaghi P, Carrez F, Moessner K (2011) Context-aware management for sensor networks, Proceedings of the 5th International Conference on Communication System Software and Middleware pp. 6:1-6:6 ACM
The wide field of wireless sensor networks requires that hun-
dreds or even thousands of sensor nodes have to be main-
tained and configured. With the upcoming initatives such
as Smart Home and Internet of Things, we need new mecha-
nism to discover and manage this amount of sensors. In this
paper, we describe a middleware architecture that uses con-
text information of sensors to supply a plug-and-play gate-
way and resource management framework for heterogeneous
sensor networks. Our main goals are to minimise the effort
for network engineers to configure and maintain the network
and supply a unified interface to access the underlying het-
erogeneous network. Based on the context information such
as battery status, routing information, location and radio
signal strength the gateway will configure and maintain the
sensor network. The sensors are associated to nearby base
stations using an approach that is adapted from the 802.11
WLAN association and negotiation mechanism to provide
registration and connectivity services for the underlying sen-
sor devices. This abstracted connection layer can be used to
integrate the underlying sensor networks into high-level ser-
vices and applications such as IP-based networks and Web
services.
Barnaghi P, Liu X, Moessner K, Liao J (2010) Using Concept and Structure Similarities for Ontology Integration, CEUR Workshop Proceedings: Proceedings of the 5th International Workshop on Ontology Matching 689
We propose a method to align different ontologies in similar
domains and then define correspondence between concepts in two
different ontologies using the SKOS model.
Anantharam P, Barnaghi P, Sheth AP (2015) Extracting City Traffic Events from Social Streams, ACM Transactions on Intelligent Systems and Technology n n
(2014) Proceedings of the Fifth Workshop on Semantics for Smarter Cities a Workshop at the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, October 19, 2014., S4SC@ISWC 1280 CEUR-WS.org
Barnaghi PM, Kareem SA (2007) A context-aware ranking method for the complex relationships on the semantic web, Proceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology pp. 129-134
In this paper we propose a method to analyse contextual perspectives in information search and retrieval process in the Semantic Web framework. We start with definitions of context in different computer science applications. We discuss our specific purpose of context-aware information representation and then describe utilising the context confidence factor for ranking the relationships. A confidence factor specifies relevancy degree of an entity to a context in a particular domain. We demonstrate how the uncertainty and Bayesian rule are taken in to account to compute the confidence factor for the results of a query. The main goal is providing a context-aware ranking method which defines degree of relevancy between resources in a set of related information to a particular query. © 2007 IEEE.
barnaghi P, Bermudez M, Toenjes R (2015) Challenges for Quality of Data in Smart Cities, ACM Journal of Data and Information Quality Association for Computing Machinery
Wei W, Barnaghi PM, Bargiela A (2008) Knowledge Acquisition for Semantic Search Systems, Proceedings of International Symposium on Information Technology 2008 pp. 1157-1162 IEEE
Semantic search extends the scope of conventional information
search and retrieval paradigms from documentoriented
and to entity and knowledge-centric search and retrieval.
By attempting to provide direct and intuitive answers
such systems alleviate information overload problem
and reduce information seekers? cognitive overhead.
Ontologies and knowledge bases are fundamental cornerstones
in semantic search systems based on which sophisticated
search mechanisms and efficient search services
are designed. Nevertheless, acquisition of quality knowledge
from heterogeneous sources on the Web is never a
trivial task. Transformation of data in existing databases
seems a promising bootstrapping approach, while information
providers may refuse to do so because of intellectual
property issues. In this article we discuss issues related to
knowledge acquisition for semantic search systems. In particular,
we discuss ontology learning from unstructured text
corpus, which is an automatic knowledge acquisition process
using different techniques.
Barnaghi P, Ganz F, Henson C, Sheth A (2012) Computing Perception from Sensor Data,
This paper describes a framework for perception
creation from sensor data. We propose using data abstraction
techniques, in particular Symbolic Aggregate Approximation
(SAX), to analyse and create patterns from sensor data. The
created patterns are then linked to semantic descriptions that
define thematic, spatial and temporal features, providing highly
granular abstract representation of the raw sensor data. This
helps to reduce the size of the data that needs to be
communicated from the sensor nodes to the gateways or highlevel
processing components. We then discuss a method that uses
abstract patterns created by SAX method and occurrences of
different observations in a knowledge-based model to create
perceptions from sensor data.
Kolozali S, Puschmann D, Bermudez-Edo M, Barnaghi P (2016) On the Effect of Adaptive and Non-Adaptive Analysis of Time-Series Sensory Data, IEEE Internet of Things 99
With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.
Cassar G, Barnaghi P, Moessner K (2013) Probabilistic Matchmaking Methods for Automated Service Discovery, IEEE Transactions on Service Computing
Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values.
Liu X, Barnaghi P, Cheng B, Wan L, Yang Y (2015) OMI-DL: An Ontology Matching Framework, Services Computing, IEEE Transactions on PP 99 pp. 1-1-1-1
Barnaghi P, Abdul Kareem S (2007) Relation Robustness Evaluation for the Semantic Associations, Electronic Journal of Knowledge Management 5 (3) pp. 265-272
The search tools and information retrieval systems on the contemporary Web use keywords, lexical analysis, popularity, and statistical methods to find and prioritise relevant data to a specific query. In recent years, Semantic web has introduced new approaches to specify Web data using machine-interpretable structures. This has led to the establishment of new frameworks for search engines and information systems based on discovering complex and meaningful relationships between information resources. In this paper we discuss a semantic supported information search and retrieval system to answer users? information queries. The paper focuses on knowledge discovery aspects of the system and in particular analysis of semantic associations. The information resources are multimedia data, which could be retrieved from heterogeneous resources. The main goal is to provide a hypermedia presentation, which narratively conveys relevant information to the queried term. The structure describes the related entities to the queried topic and a ranking mechanism assigns weights to the entities. The assigned weights express the degree of relevancy of each related entity in the presentation structure.
Barnaghi P, Sheth A, Singh V, Hauswirth M (2015) Physical-Cyber-Social Computing: Looking Back, Looking Forward, IEEE INTERNET COMPUTING 19 (3) pp. 7-11 IEEE COMPUTER SOC
Puschmann D, Barnaghi P, Tafazolli R (2016) Adaptive Clustering for Dynamic IoT Data Streams, IEEE Internet of Things IEEE
The emergence of the Internet of Things (IoT) has
led to the production of huge volumes of real-world streaming
data. We need effective techniques to process IoT data streams
and to gain insights and actionable information from realworld
observations and measurements. Most existing approaches
are application or domain dependent. We propose a method
which determines how many different clusters can be found
in a stream based on the data distribution. After selecting the
number of clusters, we use an online clustering mechanism
to cluster the incoming data from the streams. Our approach
remains adaptive to drifts by adjusting itself as the data changes.
We benchmark our approach against state-of-the-art stream
clustering algorithms on data streams with data drift. We show
how our method can be applied in a use case scenario involving
near real-time traffic data. Our results allow to cluster, label and
interpret IoT data streams dynamically according to the data
distribution. This enables to adaptively process large volumes of
dynamic data online based on the current situation. We show
how our method adapts itself to the changes. We demonstrate
how the number of clusters in a real-world data stream can be
determined by analysing the data distributions.
Barnaghi P, Meissner S, Presser M, Moessner K (2009) Sense and sens? ability: Semantic data modelling for sensor networks, Conference Proceedings of ICT Mobile Summit 2009
Wei W, Barnaghi PM (2007) Semantic support for medical image search and retrieval, Biomedical Engineering 2007: Proceedings of the 5th IASTED International Conference on Biomedical Engineering pp. 315-319 ACTA Press
The need for annotating digital image data is recognised in a variety of different medical information systems, covering both professional and educational usage of medical imaging. Due to the high recall and low precision attribute of keyword-based search, multimedia information search and retrieval based on textual descriptions is not always an efficient and sufficient solution, particularly for specific applications such as the medical diagnosis information systems. On the other hand, using image processing techniques to provide search on the content specific data for multimedia information is not a trivial task. In this paper we use the semantic web technologies in medical image search and retrieval process for a medical imaging information system. We employ an ontology-based knowledge representation and semantic annotation for medical image data. The proposed system defines data representation structures which are given well-defined meanings. The meanings are machine-accessible contents which could be interpreted by the software agents to find and retrieve the information based on the standard vocabularies and meaningful relationships between the data items.
Ganz F, Puschmann D, Barnaghi P, Carrez F (2015) A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things, Internet of Things Journal, IEEE PP (99)
Barnaghi P (2015) Digital Technology Adoption in the Smart Built Environment, IET
Ganz F, Barnaghi P, Li R, Harai H (2012) A resource mobility scheme for service-continuity in the Internet of Things, Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012 pp. 261-264
In the Internet of Things (IoT) a large number of devices enable data communication and interaction between physical objects and the cyber world. An important feature of IoT is the possibility of having mobile objects equipped with sensing devices. In service-enabled IoT platforms, where data and interacting are provisioned as services, access and utilisation of these services are affected by the mobility of the resources that provide the data and services. In a reliable and dependable environment, service continuity is supported in scenarios where the IoT resources are mobile or can become unavailable due to handover delays, network disconnection or power outage. In this paper, we propose a resource mobility scheme with two operating modes - caching and tunnelling. We use these methods to enable applications to access the sensory data when the resources become temporarily unavailable. We have implemented a prototype for the proposed scheme in a mobile scenario. The evaluation results show a reduction of service loss in mobility scenarios by 30%. © 2012 IEEE.
De S, Elsaleh T, Barnaghi P, Meissner S (2012) An Internet of Things Platform for Real-World and Digital Objects, Scalable Computing: Practice and Experience 13 (1) pp. 45-57 West University of Timisoara
The vision of the Internet of Things (IoT) relies on the provisioning of real-world services, which are provided
by smart objects that are directly related to the physical world. A structured, machine-processible approach to provision such
real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the
digital world. The incorporation of observation and measurement data obtained from the physical objects with the Web data, using
information processing and knowledge engineering methods, enables the construction of ?intelligent and interconnected things?.
The current research mostly focuses on the communication and networking aspects between the devices that are used for sensing
amd measurement of the real world objects. There is, however, relatively less effort concentrated on creating dynamic infrastructures
to support integration of the data into the Web and provide unified access to such data on service and application levels. This
paper presents a semantic modelling and linked data approach to create an information framework for IoT. The paper describes
a platform to publish instances of the IoT related resources and entities and to link them to existing resources on the Web. The
developed platform supports publication of extensible and interoperable descriptions in the form of linked data.
Barnaghi P, Sheth A, Henson C (2013) From data to actionable knowledge: Big data challenges in the web of things, IEEE Intelligent Systems 28 (6)
Extending the current Internet and providing connection, communication, and internetworking between devices and physical objects, or 'things,' is a growing trend that's often referred to as the Internet of Things (IoT). Integrating real-world data into the Web, with its large repositories of data, and providing Web-based interactions between humans and IoT resources is what the Web of Things (WoT) stands for. Here, the guest editors describe the Big Data issues in the WoT, discuss the challenges of extracting actionable knowledge and insights from raw sensor data, and introduce the theme articles in this special issue. © 2013 IEEE.
Barnaghi P, Wang W, Kurian JC (2009) Semantic Association Analysis in Ontology-based Information Retrieval, XIII pp. 131-141 IGI Global
The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.
Laurent Lefort, Cory Henson, Kerry Taylor, Michael Compton, Oscar Corcho, Raúl García Castro, John Graybeal, Arthur Herzog, Krzysztof Janowicz, Holger Neuhaus, Andriy Nikolov, Kevin Page, Payan Barnaghi (2011) Semantic Sensor Network XG Final Report,
Bermudez M, kolozali S, Puschmann D, Ganz F, Barnaghi P (2014) A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing,
Lee JR, Barnaghi PM (2007) Semantic media in medical information systems, SMAP07 - Second International Workshop on Semantic Media Adaptation and Personalization pp. 27-31
The Content-based Image Retrieval (CBIR) methods have been applied to medical field to aid diagnosis and other medical processes for a relatively long time. This type of systems, with the growth of digital medical image databases, sometimes does not provide information tailored to the end user requirements. Typically there is a semantic gap between the low-level features extracted form the images and high-level concepts required by the user. This paper proposes an ontology-based search and retrieval as a supplementary method to associate high-level concepts and semantics to medical image data and exploit these semantic media for information search and retrieval. The main goal is to perform a search on a medical image repository and to retrieve relevant medical images and information to a particular case in order to assist diagnosis process. We demonstrate the functionality of the system in case of a mammography imaging database. The system uses an ontology-based search and retrieval for the medical data as a complementary solution to provide more efficient and insight access to the stored data. © 2007 IEEE.
Cassar G, Barnaghi P, Wang W, Moessner K (2012) A hybrid semantic matchmaker for IoT services, Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012 pp. 210-216 IEEE
The use of semantic Web technologies and service oriented computing paradigm in Internet of Things research has recently received significant attention to create a semantic service layer that supports virtualisation of and interaction among "Things". Using service-based solutions will produce a deluge of services that provide access to different data and capabilities exposed by different resources. The heterogeneity of the resources and their service attributes, and dynamicity of mobile environments require efficient solutions that can discover services and match them to the data and capability requirements of different users. Semantic service matchmaking process is the fundamental construct for providing higher level service-oriented functionalities such as service recommendation, composition, and provisioning in Internet of Things. However, scalability of the current approaches in dealing with large number of services and efficiency of logical inference mechanisms in processing huge number of heterogeneous service attributes and metadata are limited. We propose a hybrid semantic service matchmaking method that combines our previous work on probabilistic service matchmaking using latent semantic analysis with a weighted-link analysis based on logical signature matching. The hybrid method can overcome most cases of semantic synonymy in semantic service description which usually presents the biggest challenge for semantic service matchmakers. The results show that the proposed method performs better than existing solutions in terms of precision (P@n) and normalised discounted cumulative gain (NDCG) measurement values. © 2012 IEEE.
Barnaghi P, Moessner K, Presser M, Meissner S (2009) Smart Sensing and Context, Springer
This volume constitutes the revised papers of the 4th European Conference on Smart Sensing and Context, Euro SSC 2009, held in Guilford, UK, in September 2009.This volume consists of 16 full papers.
Wang W, Barnaghi P, Bargiela A (2008) Search with Meanings:An Overview of Semantic Search Systems, International Journal of Communications of SIWN 3 pp. 76-82
Research on semantic search aims to improve conventional
information search and retrieval methods, and facilitate
information acquisition, processing, storage and retrieval
on the semantic web. The past ten years have seen a number
of implemented semantic search systems and various proposed
frameworks. A comprehensive survey is needed to gain
an overall view of current research trends in this field. We
have investigated a number of pilot projects and corresponding
practical systems focusing on their objectives, methodologies
and most distinctive characteristics. In this paper, we report
our study and findings based on which a generalised semantic
search framework is formalised. Further, we describe issues
with regards to future research in this area.
(2013) Proceedings of the 6th International Workshop on Semantic Sensor Networks co-located with the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 22nd, 2013., SSN@ISWC 1063 CEUR-WS.org
Kurian JC, Barnaghi PM, Hartley MI (2006) User mediated hypermedia presentation generation on the semantic web framework, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4282 LNCS pp. 901-907
The art of authoring digital multimedia involves collecting and organizing different sorts of media items and transforming them into a coherent presentation. Existing authoring tools for multimedia presentations provide functional support for the authoring process that requires domain knowledge or presentation skills. The authoring process can be enhanced if the authors are supported with decision making, material collection, selection, and presentation composition in generating web presentations. We apply the semantic web technology to generate hypermedia presentations based on media resources retrieved from the web. A discourse model represents the discussion of a subject with a theme supported by a discourse structure that represents the arrangement of contents in a discourse. In this paper we define a discourse model (i.e. Neural Network Architecture) that specifies the knowledge of composing various discourse entities (e.g. Feed-Forward Neural Networks) which enables the building of discourse structures for various themes (e.g. Lecture Notes). © 2006 Springer-Verlag Berlin/Heidelberg.
Liong SS, Barnaghi PM (2007) Bluetooth network security: A new approach to secure scatternet formation, IEEE Region 10 Annual International Conference, Proceedings/TENCON 2007
In this paper, we study some of the most common formation protocols for scatternets such as BlueTrees, BlueNet, and BlueStars. The paper focuses on security mechanisms that are needed to provide secure communication among the nodes in the scatternet We propose a secure communication between two parties based on encryption mechanisms. In this approach secret keys are proposed for each pair. The focus of the suggested method is the scatternet communication security and in particular the secret key exchange. The paper describes a mechanism for the key agreement procedure through a secure scatternet formation protocol.
Barnaghi P, Cassar G, Moessner K (2010) Probabilistic Methods for Service Clustering, CEUR Workshop Proceedings: Proceedings of 4th International Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web 667
This paper focuses on service clustering and uses service descriptions
to construct probabilistic models for service clustering.We discuss
how service descriptions can be enriched with machine-interpretable
semantics and then we investigate how these service descriptions can be
grouped in clusters in order to make discovery, ranking, and recommendation
faster and more effective. We propose using Probabilistic Latent
Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) (i.e.
two machine learning techniques used in Information Retrieval) to learn
latent factors from the corpus of service descriptions and group services
according to their latent factors. By creating an intermediate layer of
latent factors between the services and their descriptions, the dimensionality
of the model is reduced and services can be searched and linked
together based on probabilistic methods in latent space. The model can
cluster any newly added service with a direct calculation without requiring
to re-calculate the latent variables or re-train the model.
Hoseinitabatabaei SA, barnaghi P, tafazolli R, wang C Method and apparatus for scalable data discovery in IoT systems,
This patent is based on our novel data discovery mechanism for large scale, highly distributed and heterogeneous data networks. Managing Big Data harvested from IoT environments is an example application
Barnaghi P, Wang W, Henson C, Tayolor K (2012) Semantics for the Internet of Things: early progress and back to the future, International Journal on Semantic Web and Information Systems 8 (1) IGI Global
Puiu D, Barnaghi P, Toenjes R, Kuemper D, Ali MI, Mileo A, Parreira JX, Fischer M, Kolozali S, Farajidavar N, Gao F, Iggena T, Pham T-L, Nechifor C-S, Puschmann D, Fernandes J (2016) CityPulse: Large Scale Data Analytics Framework for Smart Cities, IEEE ACCESS 4 pp. 1086-1108 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Cassar G, Barnaghi P, Moessner K (2012) A Distributed Hybrid Match- maker for IoT Services, International Transactions on Systems Science and Applications 8 pp. 113-128 SIWN Press
Tönjes R, Reetz ES, Moessner K, Barnaghi PM (2012) A test-driven approach for life cycle management of internet of things enabled services, 2012 Future Network and Mobile Summit, FutureNetw 2012
To date implementations of Internet of Things (IoT) architectures are confined to particular application areas and tailored to meet only the limited requirements of their narrow applications. To overcome technology and sector boundaries this paper proposes a dynamic service creation environment that employs i) orchestration of business services based on re-usable IoT service components, ii) self-management capable components for automated configuration and testing of services for things, and iii) abstraction of the heterogeneity of underlying technologies to ensure interoperability. To ensure reliability and robustness the presented approach integrates self-testing and self-adaptation in all service life cycle phases. The service life cycle management distinguishes the IoT service creation phase (design-time) and the IoT service provision phase (run-time). For test-friendly service creation (1) semantic service descriptions are employed to derive semi-automatically services and related tests, (2) and testing is systematically integrated into a Service Creation Environment. For reliable and robust service provisioning the presented system (3) forces validation tests in a sandbox environment before deployment and (4) enables run-time monitoring for service adaptation. The system under test is modelled by finite state machines (FSM) that are semi-automatically composed of re-usable test components. Then path searching algorithms are applied to derive automatically tests from the FSM model. The resulting tests are specified in the test control notation TTCN-3 and compiled to run the validation tests. © 2012 IIMC Ltd.
Barnaghi P, Abdul Kareem S (2007) A Survey of Automated Multimedia Presentation Generation Frameworks, Research Excellence and Knowledge Enrichment in ICT: Proceedings of the 2nd International Conference on Informatics
Barnaghi PM, Kareem SA (2006) A Flexible Architecture for Semantic Annotation and Automated Multimedia Presentation Generation., SEMPS 228 CEUR-WS.org
Kolozali S, Bermudez-Edo M, Puschmann D, Ganz F, Barnaghi P (2014) A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing,
Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large intercon- nected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.
Cassar G, Barnaghi P, Moessner K (2011) A probabilistic latent factor approach to service ranking, Proceedings - 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, ICCP 2011 pp. 103-109
In this paper we investigate the use of probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors provide a model to represent service descriptions of any type in vector form. With this conversion, heterogeneous service descriptions can be represented on the same homogeneous plane thus achieving interoperability between different service description technologies. Automated service discovery and ranking is achieved by extracting latent factors from queries and representing the queries in vector form. Vector algebra can then be used to match services to the query. This approach is scalable to large service repositories and provides an efficient mechanism for publishing new services after the system is deployed. © 2011 IEEE.
Compton M, Barnaghi P, Bermudez L, García-Castro R, Corcho O, Cox S, Graybeal J, Hauswirth M, Henson C, Herzog A, Huang V, Janowicz K, Kelsey WD, Le Phuoc D, Lefort L, Leggieri M, Neuhaus H, Nikolov A, Page K, Passant A, Sheth A, Taylor K (2012) The SSN ontology of the W3C semantic sensor network incubator group, Journal of Web Semantics 17 pp. 25-32
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects. © 2012 Elsevier B.V. All rights reserved.
Barnaghi P, Presser M, Moessner K (2010) Publishing Linked Sensor Data, CEUR Workshop Proceedings: Proceedings of the 3rd International Workshop on Semantic Sensor Networks (SSN), Organised in conjunction with the International Semantic Web Conference 668
This paper describes a linked-data platform to publish sen-
sor data and link them to existing resource on the semantic Web. The
linked sensor data platform, called Sense2Web supports
exible and in-
teroperable descriptions and provide association of di erent sensor data
ontologies to resources described on the semantic Web and the Web of
data. The current advancements in (wireless) sensor networks and being
able to manufacture low cost and energy e cient hardware for sensors
has lead to much interest in integrating physical world data into theWeb.
Wireless sensor networks employ various types of hardware and software
components to observe and measure physical phenomena and make the
obtained data available through di erent networking services. Applica-
tions and users are typically interested in querying various events and
requesting measurement and observation data from the physical world.
Using a linked data approach enables data consumers to access sensor
data and query the data and relations to obtain information and/or inte-
grate data from various sources. Global access to sensor data can provide
a wide range of applications in di erent domains such as geographical
information systems, healthcare, smart homes, and business applications
and scenarios. In this paper we focus on publishing linked-data to anno-
tate sensors and link them to other existing resources on the Web.
Kolozali ^, Elsaleh T, Barnaghi P (2014) A validation tool for the W3C SSN ontology based sensory semantic knowledge, CEUR Workshop Proceedings 1401 pp. 83-88
This paper describes an ontology validation tool that is designed for the W3C Semantic Sensor Networks Ontology (W3C SSN). The tool allows ontologies and linked-data descriptions to be validated against the concepts and properties used in the W3C SSN model. It generates validation reports and collects statistics regarding the most commonly used terms and concepts within the ontologies. An online version of the tool is available at: (http://iot.ee.Surrey.ac.uk/SSNValidation). This tool can be used as a checking and validation service for new ontology developments in the IoT domain. It can also be used to give feedback to W3C SSN and other similar ontology developers regarding the most commonly used concepts and properties from the reference ontology and this information can be used to create core ontologies that have higher level interoperability across different systems and various application domains.
Omitola T, Breslin J, Barnaghi P (2014) Preface, CEUR Workshop Proceedings 1280
Ganz F, Barnaghi P, Carrez F, Moessner K (2011) A mediated gossiping mechanism for large-scale sensor networks, 2011 IEEE GLOBECOM Workshops, GC Wkshps 2011 pp. 405-409 IEEE
Gateways in sensor networks are used to relay, aggregate and communicate information from capillary networks to more capable (e.g. IP-based) networks. However Gateway-to-Gateway (G2G) communication to exchange and update information among the gateways in large-scale sensor networks for query processing, data fusion and other similar tasks has been less discussed in recent works. The requirements for large-scale sensor networks such as dynamic topology and update strategies to reduce the overall network load makes G2G communications an important aspect in the network design. In this paper, we introduce a mediated gossip-based G2G communication mechanism. The proposed solution leverages the publish/subscribe approach and uses high-level context assigned to publish/subscribe channels to enable the information discovery and G2G communications. Gateways store/aggregate sensor observation and measurement data according to specific context which is defined based on features such as spatial and temporal attributes, observed phenomena (i.e. feature of interest) and sensor device features. The gateways communicate with each other to exchange data and also to forward related queries for data aggregation in cases that the data should be aggregated from two different sources. The proposed solution also facilitates reliable sensor service provisioning by enabling gateways to communicate and/or forward requests to other gateways when a resource fails or a sensor node becomes unavailable. We compare our results to probabilistic gossiping algorithms and run benchmarks on different dynamic network topologies based on indicators such as number of sent messages and dissemination delay.
Ahrabian A, Kolozali S, Enshaeifar S, Cheong Took C, Barnaghi P (2017) Stream Data Analysis as a web service: A Case Study Using IoT Sensor Data, Proceedings of ICASSP2017 IEEE
The advent of Internet of Things, has resulted in the development of infrastructure for capturing and storing data from domains ranging from smart devices (e.g. smartphones) to smart cities. This data is often available publicly and has enabled a wider range of data consumers to utilise such data sets for applications ranging from scientific experimentation to enhancing commercial activity for businesses. Accordingly this has resulted in the need for the development data analysis tools that are both simple to use and provide the most effective tools for a given data set. To this end, we introduce data analysis tools as web service, that enables the data consumer to make a simple HTTP request for processing data over the internet. By providing such tools as a web service, we demonstrate the potential of such a system to aid both the advanced and novice data consumer. Furthermore, this work provides an use case example of the proposed tool on publicly available data extracted from the smart city CityPulse IoT project.
Cassar G, Barnaghi P, Wang W, De S, Moessner K (2013) Composition of Services in Pervasive Environments: A Divide and Conquer Approach, IEEE
In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.
Compton M, Barnaghi P, Bermudez L, Garcia-Castro R, Corcho O, Cox S, Graybeal J, Hauswirth M, Henson C, Herzog A, Huang V, Janowicz K, Kelsey D, Phuoc D, Lefort L, Leggieri M, Neuhaus H, Nikolov A, Page K, Passant A, Sheth A, Taylor K (2012) The SSN Ontology of the W3C Semantic Sensor Network Incubator Group, Journal of Web Semantics 17 pp. 25-32 Elsevier
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations ? the SSNontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSNontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSNontology. It further gives an example and describes the use of the ontology in recent research projects.
Taylor K, Barnaghi P (2012) Special issue on sensor networks, internet of things and smart devices, International Journal on Semantic Web and Information Systems 8 (1)
Wei W, Barnaghi P, Bargiela A (2010) Probabilistic Topic Models for Learning Terminological Ontologies, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 22 (7) pp. 1028-1040 IEEE COMPUTER SOC
Wei Wang, Payam Barnaghi, Andrzej Bargiela (2011) Learning SKOS relations for terminological ontologies from text, 7 pp. 129-152 IGI Global
The problem of learning concept hierarchies and terminological ontologies can be divided into two subtasks:
concept extraction and relation learning. The authors of this chapter describe a novel approach
to learn relations automatically from unstructured text corpus based on probabilistic topic models. The
authors provide definition (Information Theory Principle for Concept Relationship) and quantitative
measure for establishing ?broader? (or ?narrower?) and ?related? relations between concepts. They
present a relation learning algorithm to automatically interconnect concepts into concept hierarchies
and terminological ontologies with the probabilistic topic models learned. In this experiment, around
7,000 ontology statements expressed in terms of ?broader? and ?related? relations are generated using
different combination of model parameters. The ontology statements are evaluated by domain experts
and the results show that the highest precision of the learned ontologies is around 86.6% and structures
of learned ontologies remain stable when values of the parameters are changed in the ontology learning
algorithm.
Wang W, Barnaghi P, Cassar G, Ganz F, Navaratnam P (2012) Semantic Sensor Service Networks,
Tsiatsis V, Gluhak A, Bauge T, Montagut F, Bernat J, Bauer M, Villalonga C, Barnaghi P, Krco S (2010) The SENSEI Real World Internet Architecture, pp. 247-256 Ios Pr Inc
The integration of the physical world into the digital world is an important requirement for a Future Internet, as an increasing number of services and applications are relying on real world information and interaction capabilities. Sensor and actuator networks (SAN) are the current means of interacting with the real world although most of the current deployments represent closed vertically integrated solutions. In this paper we present an architecture that enables efficient integration of these heterogeneous and distributed SAN islands into a homogeneous framework for real world information and interactions, contributing to a horizontal reuse of the deployed infrastructure across a variety of application domains. We present the main concepts, their relationships and the proposed real world resource based architecture. Finally, we outline an initial implementation of the architecture based on the current Internet and web technologies.
Ganz F, Barnaghi P, Carrez F (2013) Information Abstraction for Heterogeneous Real World Internet Data, IEEE SENSORS JOURNAL 13 (10) pp. 3793-3805 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Cassar G, Barnaghi P, Wang W, De S, Moessner K (2013) Composition of Services in Pervasive Environments: A Divide and Conquer Approach,
Enshaeifar S, Hoseinitabatabaei SA, Ahrabian A, Barnaghi P (2017) Pattern Identification for State Prediction in Dynamic Data Streams,
This work proposes a pattern identification and
online prediction algorithm for processing Internet of
Things (IoT) time-series data. This is achieved by
first proposing a new data aggregation and datadriven
discretisation method that does not require data
segment normalisation. We apply a dictionary based
algorithm in order to identify patterns of interest along
with prediction of the next pattern. The performance
of the proposed method is evaluated using synthetic
and real-world datasets. The evaluations results shows
that our system is able to identify the patterns by up to
85% accuracy which is 16.5% higher than a baseline
using the Symbolic Aggregation Approximation (SAX)
method.
Winderbank-Scott P, Barnaghi P (2017) A Non-invasive Wireless Monitoring Device for Children and Infants in Pre-hospital and Acute Hospital Environments,
This paper describes the design and development
of a wireless monitoring system for use within a pediatric
environment. The current wired methods used to provide noninvasive
sensing are not best suited to their end user, and there is
a development need for platform independent data transmission.
The main goal has been to develop a practical and flexible
proof-of-concept prototype suitable for the transmission of sensor
data. This prototype consists of an Arduino based multi-input
sensor system with wireless transmission, and an Android
monitoring station with the facility to rebroadcast the collected
data via email/web as a data file. This was achieved using
commercially available hardware platforms. The software
produced for the Android device allows for full control of the
functionality provided by the sensor platform developed on the
Arduino system, as well as storing the data within a relational
database. The data can also be graphically represented in realtime
on the Android device.
Puschmann D, Barnaghi P, Tafazolli R (2017) Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams, IEEE Systems Journal 12 (2) pp. 1755-1766 Institute of Electrical and Electronics Engineers (IEEE)

Recent advancements in sensing, networking technologies
and collecting real-world data on a large scale and from various environments
have created an opportunity for new forms of real-world services
and applications. This is known under the umbrella term of the Internet
of Things (IoT). Physical sensor devices constantly produce very large
amounts of data. Methods are needed which give the raw sensor measurements
a meaningful interpretation for building automated decision
support systems. To extract actionable information from real-world data,
we propose a method that uncovers hidden structures and relations
between multiple IoT data streams. Our novel solution uses Latent
Dirichlet Allocation (LDA), a topic extraction method that is generally
used in text analysis. We apply LDA on meaningful abstractions that
describe the numerical data in human understandable terms. We use
Symbolic Aggregate approXimation (SAX) to convert the raw data into
string-based patterns and create higher level abstractions based on
rules.

We finally investigate how heterogeneous sensory data from multiple
sources can be processed and analysed to create near real-time intelligence
and how our proposed method provides an efficient way to
interpret patterns in the data streams. The proposed method uncovers
the correlations and associations between different pattern in IoT data
streams. The evaluation results show that the proposed solution is able
to identify the correlation with high efficiency with an F-measure up to
90%.

Hasanpour M, Shariat S, Barnaghi P, Hoseinitabatabaei S, Vahid S, Tafazolli R (2017) Quantum Load Balancing in Ad Hoc Networks, Quantum Information Processing 16 (148) Springer Verlag
this paper presents a novel approach in targeting load balancing in ad hoc networks utilizing the properties of quantum game theory. This approach benefits from the instantaneous and information-less capability of entangled particles to synchronize the load balancing strategies in ad hoc networks. The Quantum Load Balancing (QLB) algorithm proposed by this work is implemented on top of OLSR as the baseline routing protocol; its performance is analyzed against the baseline OLSR, and considerable gain is reported regarding some of the main QoS metrics such as delay and jitter. Furthermore, it is shown that QLB algorithm supports a solid stability gain in terms of throughput which stands a proof of concept for the load-balancing properties of the proposed theory.
Bermudez-Edo M, Elsaleh T, Barnaghi P, Taylor K (2017) IoT-Lite: A Lightweight Semantic Model for the Internet of Things, Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016 Intl IEEE Conferences pp. 90-97 IEEE
Over the past few years the semantics community
has developed ontologies to describe concepts and relationships
between different entities in various application domains,
including Internet of Things (IoT) applications. A key problem
is that most of the IoT related semantic descriptions are not
as widely adopted as expected. One of the main concerns
of users and developers is that semantic techniques increase
the complexity and processing time and therefore they are
unsuitable for dynamic and responsive environments such as
the IoT. To address this concern, we propose IoT-Lite, an
instantiation of the semantic sensor network (SSN) ontology
to describe key IoT concepts allowing interoperability and
discovery of sensory data in heterogeneous IoT platforms by
a lightweight semantics. We propose 10 rules for good and
scalable semantic model design and follow them to create
IoT-Lite. We also demonstrate the scalability of IoT-Lite by
providing some experimental analysis, and assess IoT-Lite
against another solution in terms of round time trip (RTT)
performance for query-response times.
Bermudez-Edo M, Elsaleh T, Barnaghi P, Taylor K (2017) IoT-Lite: A Lightweight Semantic Model for the Internet of Things and its Use with Dynamic Semantics, Personal and Ubiquitous Computing Springer Verlag
Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.
Abbas Fathy Abbas Y, Barnaghi P, Tafazolli R (2017) Distributed Spatial Indexing for the Internet of Things Data Management, Proceedings of IM 2017 pp. 1246-1251 IEEE
The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).
Enshaeifar S, Barnaghi P, Skillman S, Markides A, Elsaleh T, Acton T, Nilforooshan R, Rostill H (2018) Internet of Things for Dementia Care, IEEE Internet Computing 22 (1) pp. 8-17 IEEE COMPUTER SOC
In this paper we discuss a technical design and an
ongoing trial that is being conducted in the UK, called Technology
Integrated Health Management (TIHM). TIHM uses Internet of
Things (IoT) enabled solutions provided by various companies
in a collaborative project. The IoT devices and solutions are
integrated in a common platform that supports interoperable
and open standards. A set of machine learning and data analytics
algorithms generate notifications regarding the well-being of the
patients. The information is monitored around the clock by a
group of healthcare practitioners who take appropriate decisions
according to the collected data and generated notifications. In
this paper we discuss the design principles and the lessons that
we have learned by co-designing this system with patients, their
carers, clinicians, and also our industry partners. We discuss
the technical design of TIHM and explain why user-centred and
human-experience should be an integral part of the technological
design.
Papachristou N, Barnaghi P, Hu X, Maguire R, Apostolidis K, Armes J, Conley Y, Hammer M, Katsaragakis S, Kober K, Levine J, McCann L, Patiraki E, Paul S, Ream E, Wright F, Miaskowski C (2017) Congruence Between Latent Class and K-modes Analyses in the Identification of Oncology
Patients with Distinct Symptom Experiences,
Journal of Pain and Symptom Management 55 (2) pp. 318-333 Elsevier
Context:

Risk profiling of oncology patients based on their symptom experience assists
clinicians to provide more personalized symptom management interventions. Recent findings
suggest that oncology patients with distinct symptom profiles can be identified using a variety of
analytic methods.

Objectives:

To evaluate the concordance between the number and types of subgroups of
patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.

Methods:

Using data on the occurrence of 25 symptoms from the Memorial Symptom
Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of
chemotherapy (CTX), Cohen?s kappa coefficient was used to evaluate for concordance between
the two analytic methods. For both LCA and K-modes, differences among the subgroups in
demographic, clinical, and symptom characteristics, as well as quality of life outcomes were
determined using parametric and nonparametric statistics.

Results:

Using both analytic methods, four subgroups of patients with distinct symptom profiles
were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical
and Higher Psychological, All High). The percent agreement between the two methods was
75.32% which suggests a moderate level of agreement. In both analyses, patients in the All
High group were significantly younger and had a higher comorbidity profile, worse MSAS
subscale scores, and poorer QOL outcomes.

Conclusion:

Both analytic methods can be used to identify subgroups of oncology patients with
distinct symptom profiles. Additional research is needed to determine which analytic methods
and which dimension of the symptom experience provides the most sensitive and specific risk
profiles.

Ahrabian A, Elsaleh T, Abbas Fathy Abbas Y, Barnaghi P (2017) Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC, Proceedings of IEEE Sensors 2017 IEEE
An important field in exploratory sensory data
analysis is the segmentation of time-series data to identify
activities of interest. In this work, we analyse the performance
of univariate and multi-sensor Bayesian change detection
algorithms in segmenting accelerometer data. In particular, we
provide theoretical analysis and also performance evaluation on
synthetic data and real-world data. The results illustrate the
advantages of using multi-sensory variance change detection in
the segmentation of dynamic data (e.g. accelerometer data).
Abbas Fathy Abbas Y, Barnaghi P, Enshaeifar S, Tafazolli R (2017) A Distributed In-network Indexing Mechanism for the Internet of Things, IEEE World Forum on Internet of Things pp. 585-590 IEEE
The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.
Enshaeifar S, Farajidavar N, Ahrabian A, Barnaghi P, Hannam K, Deere K, Tobias J, Allison S (2017) Recognising Bone Loading Exercises In Older Adults Using Machine Learning, Medicine & Science in Sports & Exercise 49 (5S) American College of Sports Medicine

Machine learning has been used to accurately recognise physical activity patterns; however, classifiers for recognising targeted bone loading exercises have not been developed.

PURPOSE:

The purpose of this study was to determine the accuracy of machine learning models for classifying the intensity of exercises necessary for bone adaption in older adults.

METHODS:

Triaxial accelerometer data was collected from forty-four older participants (60-70 yrs) wearing a GCDC X16-1C accelerometer on their hip during three aerobics classes consisting of impact aerobic exercises performed at high and low intensities. Multi-class support vector machine (M-SVM) classifiers were trained in parallel for activity type detections where one classifier trained with low intensity activity samples and the other with high intensity samples. In a multi-view scoring manner, the classification confidence of these two learners was utilised for predicting the activity intensity. The leave-one-out cross-validation technique was used for assessment purpose.

RESULTS:

Overall recognition accuracy of the M-SVM classifier for detecting exercise intensity was 73%. For each aerobics class, the M-SVM classifier accurately recognised exercise intensity by 82%, 73% and 65%.

CONCLUSIONS:

Machine learning techniques such as M-SVM accurately recognised the intensity of bone promoting exercises from triaxial accelerometer data in community-dwelling older adults. First results of the developed classifier demonstrate significant potential of machine learning models for the evaluation of exercise adherence and performance in older adults.

Papachristou N, Barnaghi P, Cooper B, Hu X, Maguire R, Apostolidis K, Armes J, Conley Y, Hammer M, Katsaragakis S, Kober K, Levine J, McCann L, Patiraki E, Paul S, Ream E, Wright F, Miaskowski C (2017) Congruence Between Latent Class and K-modes Analyses in the Identification of Oncology Patients with Distinct Symptom Experiences, Journal of Pain and Symptom Management 55 (2) pp. 318-333 Elsevier
Context

Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods.

Objectives

To evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.

Methods

Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen?s kappa coefficient was used to evaluate for concordance between the two analytic methods. For both LCA and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics.

Results

Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical and Higher Psychological, All High). The percent agreement between the two methods was 75.32% which suggests a moderate level of agreement. In both analyses, patients in the All High group were significantly younger and had a higher comorbidity profile, worse MSAS subscale scores, and poorer QOL outcomes.

Conclusion

Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provides the most sensitive and specific risk profiles.

Mahdavinejad Mohammad Saeid, Rezvan Mohammadreza, Barekatain Mohammadamin, Adibi Peyman, Barnaghi Payam, Sheth Amit P. (2017) Machine Learning for Internet of Things Data Analysis: A Survey, Digital Communications and Networks Elsevier
Rapid developments in hardware, software, and communication technologies
have allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25-50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine learning
algorithms explaining how different techniques are applied to the data in order
to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying
Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented
for a more detailed exploration.
Fathy Yasmin, Barnaghi Payam, Tafazolli Rahim (2018) Large-Scale Indexing, Discovery and Ranking for the Internet of Things (IoT), ACM Computing Surveys 51 (2) 29 Association for Computing Machinery (ACM)
Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber
world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies
that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous
amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant
streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated
with different granularities and have diverse structures, types and qualities. Dealing with the data deluge
from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking
mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data.
However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders
their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on
indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop,
implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and
services.
Bermudez-Edo M, Barnaghi P, Moessner K (2018) Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation, Automation in Construction 88 pp. 87-100 Elsevier
Smart Cities use different Internet of Things (IoT) data sources and rely on big data analytics to obtain information or extract actionable knowledge crucial for urban planners for efficiently use and plan the construction infrastructures. Big data analytics algorithms often consider the correlation of different patterns and various data types. However, the use of different techniques to measure the correlation with smart cities data and the exploitation of correlations to infer new knowledge are still open questions. This paper proposes a methodology to analyse data streams, based on spatio-temporal correlations using different correlation algorithms and provides a discussion on co-occurrence vs. causation. The proposed method is evaluated using traffic data collected from the road sensors in the city of Aarhus in Denmark.
Ahrabian A, Enshaeifar S, Cheong Took C, Barnaghi P (2018) Segment parameter labelling in MCMC mean-shift change detection, ICASSP 2018 IEEE
This work addresses the problem of segmentation in time series
data with respect to a statistical parameter of interest in
Bayesian models. It is common to assume that the parameters
are distinct within each segment. As such, many Bayesian
change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work
proposes a Bayesian mean-shift change point detection algorithm
that makes use of repetition in segment parameters, by
introducing segment class labels that utilise a Dirichlet process
prior. The performance of the proposed approach was
assessed on both synthetic and real world data, highlighting
the enhanced performance when using parameter labelling.
Abbas Fathy Abbas Y, Barnaghi P, Tafazolli R (2018) An Adaptive Method for Data Reduction in the Internet of Things, Proceedings of IEEE 4th World Forum on Internet of Things IEEE
Enormous amounts of dynamic observation and
measurement data are collected from sensors in Wireless
Sensor Networks (WSNs) for the Internet of Things (IoT)
applications such as environmental monitoring. However, continuous
transmission of the sensed data requires high energy
consumption. Data transmission between sensor nodes and
cluster heads (sink nodes) consumes much higher energy than
data sensing in WSNs. One way of reducing such energy
consumption is to minimise the number of data transmissions.
In this paper, we propose an Adaptive Method for Data Reduction
(AM-DR). Our method is based on a convex combination
of two decoupled Least-Mean-Square (LMS) windowed filters
with differing sizes for estimating the next measured values
both at the source and the sink node such that sensor nodes
have to transmit only their immediate sensed values that
deviate significantly (with a pre-defined threshold) from the
predicted values. The conducted experiments on a real-world
data show that our approach has been able to achieve up to
95% communication reduction while retaining a high accuracy
(i.e. predicted values have a deviation of ý+0:5 from real data
values).
Hoseinitabatabaei S, Barnaghi P, Dong L, Wang C, Tafazolli R (2017) Scalable data discovery in an internet of things (iot) system,
Data discovery for sensor data in an M2M network uses probabilistic models, such as Gaussian Mixing Models (GMMs) to represent attributes of the sensor data. The parameters of the probabilistic models can be provided to a discovery server (DS) that respond to queries concerning the sensor data. Since the parameters are compressed compared to the attributes of the sensor data itself, this can simplify the distribution of discovery data. A hierarchical arrangement of discovery servers can also be used with multiple levels of discovery servers where higher level discovery servers using more generic probabilistic models.
Gonzalez-Vidal Aurora, Barnaghi Payam, Skarmeta Antonio F. (2018) BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation, IEEE Transactions on Knowledge and Data Engineering Institute of Electrical and Electronics Engineers (IEEE)
The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to
reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and
represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced
mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes
the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting
matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is
designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining
real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation
(SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS
is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of
the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.
Bermudez-Edo M, Barnaghi P (2018) Spatio-Temporal Analysis for Smart City Data, Proceedings of WebConf 2018 pp. 1841-1845 ACM
The data gathered from smart cities can help citizens and city manager
planners know where and when they should be aware of the
repercussions regarding events happening in different parts of the
city. Most of the smart city data analysis solutions are focused on
the events and occurrences of the city as a whole, making it difficult
to discern the exact place and time of the consequences of a particular
event. We propose a novel method to model the events in a city
in space and time. We apply our methodology for vehicular traffic
data basing our models in (convolutional) neuronal networks.
Hoseinitabatabaei Seyed, Fathy Y, Barnaghi Payam, Wang C, Tafazolli Rahim (2018) A Novel Indexing Method for Scalable IoT
Source Lookup,
IEEE Internet of Things Journal 5 (3) pp. 2037-2054 IEEE
When dealing with a large number of devices, the existing indexing solutions for the discovery of IoT sources often fall short
to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to
create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach
for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The
proposed method creates concise references to IoT sources by using Gaussian Mixture Models (GMM). Furthermore, a summary update
mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The
proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed
solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network
architecture it may slightly increase the discovery time
Skarmeta A, Santa J, Martínez J, Parreira J, Barnaghi P, Enshaeifar S, Beliatis M, Presser M, Iggena T, Fischer M, Tönjes R, Strohbach M, Sforzin A, Truong H (2018) IoTCrawler: Browsing the Internet of Things, Proceedings of The 2018 Global IoT Summit (GIoTS) Institute of Electrical and Electronics Engineers (IEEE)
The Internet of Things (IoT) offers an incredible
innovation potential for developing smarter applications and
services. However, today we see solutions in the development of
vertical applications and services reflecting what used to be the
early days of the Web, leading to fragmentation and intra-nets of
Things. To achieve an open IoT ecosystem of systems and
platforms, several key enablers are needed for effective, adaptive
and scalable mechanisms for exploring and discovering IoT
resources and their data/capabilities. This paper discusses our
work in the EU H2020 IoTCrawler project. Its focus is on the
integration and interoperability across different platforms,
through dynamic and reconfigurable solutions for discovery and
integration of data and services from legacy and new systems. This
is complemented with adaptive, privacy-aware and secure
solutions for crawling, indexing and searching in distributed IoT
systems. IoTCrawler targets IoT development and demonstrations
with a focus on Industry 4.0, Social IoT, Smart City and Smart
Energy use cases.
Cassar G, Barnaghi P, Wang W, De S, Moessner K (2013) Composition of Services in Pervasive Environments: A Divide and Conquer Approach, IEEE Symposium on Computers and Communications
In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.
Puschmann D (2018) Extracting information from heterogeneous internet of things data streams.,
Recent advancements in sensing, networking technologies and collecting real-world data on a large scale and from various environments
have created an opportunity for new forms of services and applications. This is known under the umbrella term of the Internet of
Things (IoT). Physical sensor devices constantly produce very large amounts of data. Methods are needed which give the raw sensor measurements a meaningful interpretation for building automated decision support systems. One of the main research challenges in this domain is to extract actionable information from real-world data, that is information that can readily be used to make informed automatic
decisions in intelligent systems. Most existing approaches are application or domain dependent or are only able to deal with specific data
sources of one kind. This PhD research concerns multiple approaches for analysing IoT data streams. We propose a method which determines how many different clusters can be found in a stream based on the data distribution. After selecting the number of clusters, we use an online clustering mechanism to cluster the incoming data from the streams. Our approach remains adaptive to drifts by adjusting itself as the data changes. The work is benchmarked against state-of-the art stream clustering algorithms on data streams with data drift. We show how our method can be applied in a use case scenario involving near real-time traffic data. Our results allow to cluster, label and interpret IoT data streams dynamically according to the data distribution. This enables to adaptively process large volumes of dynamic data online based on the current situation. We show how our method adapts itself to the changes and we demonstrate how the number of clusters in a real-world data stream can be determined by analysing the data distributions.
Using the ideas and concepts of this approach as a starting point we designed another novel dynamic and adaptable clustering approach
that is more suitable for multi-variate time-series data clustering. Our solution uses probability distributions and analytical methods to adjust the centroids as the data and feature distributions change over time. We have evaluated our work against some well-known time-series clustering methods and have shown how the proposed method can reduce the complexity and perform efficient in multi-variate datastreams.
Finally we propose a method that uncovers hidden structures and relations between multiple IoT data streams. Our novel solution uses Latent Dirichlet Allocation (LDA), a topic extraction method that is generally used in text analysis. We apply LDA on meaningful labels that describe the numerical data in human understandable terms. To create the labels we use Symbolic Aggregate approXimation (SAX), a method that converts raw data into string-based patterns. The extracted patterns are then transformed with a rule engine into the labels.
The work investigates how heterogeneous sensory data from multiple sources can be processed and analysed to create near real-time intelligence and how our proposed method provides an efficient way to interpret patterns in the data streams. The proposed method provides a novel way to uncover the correlations and associations between different pattern in IoT data streams. The evaluation results show that the proposed solution is able to identify the correlation with high efficiency with an F-measure up to 90%.
Overall, this PhD research has designed, implemented and evaluated unsupervised adaptive algorithms to analyse, structure and extract information from dynamic and multi-variate sensory data streams. The results of this research has significant impact in designing flexible and scalable solutions in analysing real-world sensory data streams and specially in cases where labelled and annotated data is not available or it is too costly to be collected. Research and advancements in healthcare and smarter cities are two key areas that can directly fr