Dr Payam Barnaghi

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

Contact Me

E-mail:
Phone: 01483 68 3460

Find me on campus
Room: 09 CII 02

Publications

Journal articles

  • 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'. Springer Verlag Personal and Ubiquitous Computing,

    Abstract

    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.

  • 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
  • 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-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE ACCESS, 4, pp. 1086-1108.
  • Puschmann D, Barnaghi P, Tafazolli R. (2016) 'Adaptive Clustering for Dynamic IoT Data Streams'. IEEE IEEE Internet of Things,
    [ Status: Accepted ]
  • barnaghi P, Bermudez M, Toenjes R. (2015) 'Challenges for Quality of Data in Smart Cities'. Association for Computing Machinery ACM Journal of Data and Information Quality,
  • Barnaghi P, Sheth A, Singh V, Hauswirth M. (2015) 'Physical-Cyber-Social Computing: Looking Back, Looking Forward'. IEEE COMPUTER SOC IEEE INTERNET COMPUTING, 19 (3), pp. 7-11.
  • 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)
  • Liu X, Barnaghi P, Cheng B, Wan L, Yang Y. (2015) 'OMI-DL: An Ontology Matching Framework'. Services Computing, IEEE Transactions on, PP Article number 99 , pp. 1-1-1-1.
    [ Status: Accepted ]
  • Anantharam P, Barnaghi P, Sheth AP. (2015) 'Extracting City Traffic Events from Social Streams'. ACM Transactions on Intelligent Systems and Technology, n Article number n
  • Ganz F, Barnaghi P, Carrez F. (2014) 'Automated Semantic Knowledge Acquisition From Sensor Data'. IEEE Systems Journal,
    [ Status: Accepted ]
  • 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)
  • Cassar G, Barnaghi P, Moessner K. (2013) 'Probabilistic Matchmaking Methods for Automated Service Discovery'. IEEE Transactions on Service Computing,

    Abstract

    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.

  • Ganz F, Barnaghi P, Carrez F. (2013) 'Information Abstraction for Heterogeneous Real World Internet Data'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE SENSORS JOURNAL, 13 (10), pp. 3793-3805.
  • 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.
  • 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'. Elsevier Journal of Web Semantics, 17, pp. 25-32.

    Abstract

    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.

  • Cassar G, Barnaghi P, Moessner K. (2012) 'A Distributed Hybrid Match- maker for IoT Services'. SIWN Press International Transactions on Systems Science and Applications, 8, pp. 113-128.
  • De S, Elsaleh T, Barnaghi P, Meissner S. (2012) 'An Internet of Things Platform for Real-World and Digital Objects'. West University of Timisoara Scalable Computing: Practice and Experience, 13 (1), pp. 45-57.

    Abstract

    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, Wang W, Henson C, Tayolor K. (2012) 'Semantics for the Internet of Things: early progress and back to the future'. IGI Global International Journal on Semantic Web and Information Systems, 8 (1)
  • Wei W, Barnaghi P, Bargiela A. (2011) 'Rational Research model for ranking semantic entities'. Information Sciences, 181 (13), pp. 2823-2840.
  • Wei W, Barnaghi P, Bargiela A. (2011) 'Rational Research model for ranking semantic entities'. Information Sciences,
  • Wei W, Barnaghi P, Bargiela A. (2010) 'Probabilistic Topic Models for Learning Terminological Ontologies'. IEEE COMPUTER SOC IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 22 (7), pp. 1028-1040.
  • 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.
  • Barnaghi P, Abdul Kareem S. (2007) 'Relation Robustness Evaluation for the Semantic Associations'. Electronic Journal of Knowledge Management, 5 (3), pp. 265-272.

Conference papers

  • Ahrabian A, Kolozali , Enshaeifar S, Cheong-Took C, Barnaghi P. (2017) 'Stream Data Analysis as a web service: A Case Study Using IoT Sensor Data'. IEEE Proceedings of ICASSP 2017, New Orleans, USA: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP2017
    [ Status: Accepted ]

    Abstract

    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.

  • Kolozali Ş, Elsaleh T, Barnaghi P. (2014) 'A validation tool for the W3C SSN ontology based sensory semantic knowledge'. CEUR Workshop Proceedings, 1401, pp. 83-88.
  • 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'. IEEE International Conference on Internet of Things (iThings 2014)
  • Bermudez M, kolozali S, Puschmann D, Ganz F, Barnaghi P. (2014) 'A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing'.
  • . (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.'. CEUR-WS.org S4SC@ISWC, 1280
  • 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.
  • Barnaghi P, Wang W, Dong L, Wang C. (2013) 'A Linked-data Model for Semantic Sensor Streams'. IEEE Proceedings of 2013 IEEE Internet of Things (iThings), Beijing, China: IEEE International Conference on Internet of Things (iThings 2013), pp. 468-475.
  • Cassar G, Barnaghi P, Wang W, De S, Moessner K. (2013) 'Composition of Services in Pervasive Environments: A Divide and Conquer Approach'. IEEE Split, Croatia: 18th IEEE Symposium on Computers and Communications

    Abstract

    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.

  • Cassar G, Barnaghi P, Wang W, De S, Moessner K. (2013) 'Composition of Services in Pervasive Environments: A Divide and Conquer Approach'. Split Croatia: the 18th IEEE Symposium on Computers and Communications
  • 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, Madrid: The Second IEEE WoWMoM Workshop on the Internet of Things: Smart Objects and Services, pp. 1-6.
  • 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,
  • . (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.'. CEUR-WS.org SSN@ISWC, 1063
  • Wang W, Barnaghi P, Cassar G, Ganz F, Navaratnam P. (2012) 'Semantic Sensor Service Networks'. Taipei, Taiwan: IEEE Sensors 2012
  • Barnaghi P, Ganz F, Henson C, Sheth A. (2012) 'Computing Perception from Sensor Data'. Taipei, Taiwan: IEEE Sensors 2012
    [ Status: Accepted ]

    Abstract

    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.

  • 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, Berlin, Germany:

    Abstract

    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.

  • 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.

    Abstract

    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.

  • Cassar G, Barnaghi P, Wang W, Moessner K. (2012) 'A hybrid semantic matchmaker for IoT services'. IEEE 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, Besancon: IEEE International Conference on Green Computing and Communications (GreenCom), 2012, pp. 210-216.

    Abstract

    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.

  • 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.

    Abstract

    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.

  • Ganz F, Barnaghi P, Carrez F, Moessner K. (2011) 'Context-aware management for sensor networks'. New York, USA : ACM Proceedings of the 5th International Conference on Communication System Software and Middleware, Verona, Italy: COMSWARE 2011, pp. 6:1-6:6.

    Abstract

    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.

  • 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.
  • Ganz F, Barnaghi P, Carrez F, Moessner K. (2011) 'A mediated gossiping mechanism for large-scale sensor networks'. IEEE 2011 IEEE GLOBECOM Workshops, GC Wkshps 2011, Houston, Texas: GLOBECOM Workshops (GC Wkshps), 2011 IEEE, pp. 405-409.

    Abstract

    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.

  • 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, Shanghai, China: SMR2 2010 667

    Abstract

    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.

  • 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, Shanghai, China: ISWC 2010 668

    Abstract

    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.

  • 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, Shanghai, China: OM-2010 689

    Abstract

    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.

  • 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, Florence, Italy: Future Network Summit 2010
  • Villalonga P, Bauer M, Huang V, Bernat J, Barnaghi P. (2010) 'Modeling of Sensor Data and Context for the Real World Internet'. IEEE Proceedings of 8th IEEE PERCOM Workshops, Mannheim, Germany: 7th IEEE Workshop on Context Modeling and Reasoning (CoMoRea)

    Abstract

    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.

  • Wei W, Barnaghi P. (2009) 'Semantic Annotation and Reasoning for Sensor Data'. Springer Lecture Notes in Computer Science: Smart Sensing and Context, Guildford, UK: 4th European Conference on Smart Sensing and Context 5741, pp. 66-76.

    Abstract

    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.

  • 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,
  • Barnaghi P, Moessner K, Presser M, Meissner S. (2009) 'Preface'. Springer Lecture Notes in Computer Science, Guildford, UK: 4th European Conference, EuroSSC 2009 5741, pp. v-vi.
  • Wei W, Barnaghi PM, Bargiela A. (2008) 'Knowledge Acquisition for Semantic Search Systems'. IEEE Proceedings of International Symposium on Information Technology 2008, Kuala Lumpur, Malaysia: ITSIM '08, pp. 1157-1162.

    Abstract

    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, 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, Petaling Jaya, Malaysia: Informatics 2007
  • 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.
  • Wei W, Barnaghi PM. (2007) 'Semantic support for medical image search and retrieval'. Annaheim : ACTA Press Biomedical Engineering 2007: Proceedings of the 5th IASTED International Conference on Biomedical Engineering, Innsbruck, Austria: Biomed 2007, pp. 315-319.

    Abstract

    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.

  • Liong SS, Barnaghi PM. (2007) 'Bluetooth network security: A new approach to secure scatternet formation'. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2007
  • 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.
  • Lee JR, Barnaghi PM. (2007) 'Semantic media in medical information systems'. SMAP07 - Second International Workshop on Semantic Media Adaptation and Personalization, , pp. 27-31.
  • Barnaghi PM, Kareem SA. (2006) 'A Flexible Architecture for Semantic Annotation and Automated Multimedia Presentation Generation.'. CEUR-WS.org SEMPS, Athens Greece: Proceedings of the 1st International Workshop on Semantic-Enhanced Multimedia Presentation Systems (SEMPS-2006) 228
  • 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.
  • Kurian JC, Barnaghi PM, Hartley MI. (2006) 'Semantics-based Dynamic Hypermedia Adaptation using the Hidden Markov Model.'. CEUR-WS.org SEMPS, 228
  • Barnaghi PM, Kareem SA. (2005) 'Ontology-based multimedia presentation generation'. IEEE IEEE Region 10 Annual International Conference, Proceedings/TENCON, Melbourne, Qld.: TENCON 2005 2005 IEEE Region 10, pp. 1-5.

    Abstract

    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.

Books

  • Barnaghi P, Moessner K, Presser M, Meissner S. (2009) Smart Sensing and Context. Springer
  • Barnaghi P, Moessner K, Presser M. (2009) Smart Sensing and Context. Springer-Verlag New York Inc

Book chapters

  • Wei Wang , Payam Barnaghi , Andrzej Bargiela . (2011) 'Learning SKOS relations for terminological ontologies from text'. in Wong W, Liu W, Bennamoun M (eds.) Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances IGI Global Article number 7 , pp. 129-152.

    Abstract

    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.

  • 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'. in Tselentis G, Galis A, Gavras A, Krco S, Lotz V, Simperl E, Stiller B, Zahariadis T (eds.) Towards the Future Internet Ios Pr Inc , pp. 247-256.
  • Barnaghi P, Wang W, Kurian JC. (2009) 'Semantic Association Analysis in Ontology-based Information Retrieval'. in Theng Y-L, Foo S, Goh D, Na J-C (eds.) Handbook of Research on Digital Libraries: Design, Development, and Impact IGI Global Article number XIII , pp. 131-141.

    Abstract

    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.

Patents

  • Hoseinitabatabaei SA, barnaghi P, tafazolli R, wang C. Method and apparatus for scalable data discovery in IoT systems.

Reports

  • Barnaghi P. (2015) Digital Technology Adoption in the Smart Built Environment. in (ed.) IET Sector Technical Briefing, The Institution of Engineering and Technology (IET) IET
  • Sheth AP, Barnaghi PM, Strohmaier M, Jain R, Staab S. (2013) Physical-Cyber-Social Computing (Dagstuhl Reports 13402).. Article number 9 , pp. 245-263.
  • 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.
  • 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]. IEEE , pp. 1-4.

Software

  • 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

Other publications

  • Omitola T, Breslin J, Barnaghi P. (2014) Preface. CEUR Workshop Proceedings, 1280
  • 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)

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