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Tarek Elsaleh


Research Fellow
BEng, MSc
+44 (0)1483 684587
41 BA 01

Biography

Areas of specialism

Internet of Things; System integration; Linked Open Data; Web services

University roles and responsibilities

  • Systems Developer
  • Research Fellow

    My qualifications

    2006
    BEng Electronic Engineering
    Oxford Brookes University
    2008
    MSc Communications, Networks and Software
    University of Surrey

    My publications

    Publications

    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 re- garding 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 de- velopments 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.

    Shirin Enshaeifar, Payam Barnaghi, Severin Skillman, Andreas Markides, Tarek Elsaleh, Thomas Acton, Ramin Nilforooshan, H Rostill (2018)Internet of Things for Dementia Care, In: IEEE Internet Computing22(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.

    Shirin Enshaeifar, Ahmed Zoha, Severin Skillman, Andreas Markides, Tom Acton, Tarek Elsaleh, M. Kenny, H. Rostill, Ramin Nilforooshan, Payam Barnaghi (2019)Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia, In: PLOS One14(1)e0209909pp. 1-22 PLOS

    Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.

    Tarek Elsaleh, Shirin Enshaeifar, Roonak Rezvani, Sahr Thomas Acton, Valentinas Janeiko, Maria Bermudez-Edo (2020)IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services, In: Sensors20(4) MDPI

    With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

    Francois Carrez, Tarek Elsaleh, D Gomez, L Sanchez, J Lanza, P Grace (2017)A reference architecture for federating IoT infrastructures supporting semantic interoperability, In: European Conference on Networks and Communications 2017 IEEE

    : The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming vertical silos. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potential of smart applications in terms of size, scope and targeted business context. This paper describes the system architecture for the FIESTA-IoT platform, whose main aim is to federate a large number of testbeds across the planet, in order to offer experimenters the unique experience of dealing with a large number of semantically interoperable data sources. This system architecture was developed by following the Architectural Reference Model (ARM) methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). Through this process, the FIESTAIoT architecture is composed of a set of Views that deals with a “logical” functional decomposition (Functional View, FV) and data structuring and annotation, data flows and inter-functional component interactions (Information View, IV).

    F Ramparany, F Galan Marquez, J Soriano, T Elsaleh (2014)Handling smart environment devices, data and services at the semantic level with the FI-WARE core platform, In: 2014 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
    M Bermudez-Edo, Tarek Elsaleh, Payam Barnaghi, K Taylor (2017)IoT-Lite: A Lightweight Semantic Model for the Internet of Things, In: 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 Conferencespp. 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.

    R Agarwal, D Gomez Fernandez, Tarek Elsaleh, A Gyrard, J Lanza, L Sanchez, N Georgantas, V Issarny (2017)Unified IoT ontology to enable interoperability and federation of testbeds, In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)pp. 70-75 IEEE

    After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology.

    Jorge Lanza, Luis Sanchez, Juan Ramon Santana, Rachit Agarwal, Nikolaos Kefalakis, Paul Grace, Tarek Elsaleh, Mengxuan Zhao, Elias Tragos, Hung Nguyen, Flavio Cirillo, Ronald Steinke, John Soldatos (2018)Experimentation as a Service Over Semantically Interoperable Internet of Things Testbeds, In: IEEE Access6pp. 51607-51625 Institute of Electrical and Electronics Engineers (IEEE)

    Infrastructures enabling experimental assessment of Internet of Things (IoT) solutions are scarce. Moreover, such infrastructures are typically bound to a specific application domain, thus, not facilitating the testing of solutions with a horizontal approach. This paper presents a platform that supports Experimentation as s Service (EaaS) over a federation of IoT testbeds. This platform brings two major advances. First, it leverages semantic web technologies to enable interoperability so that testbed agnostic access to the underlying facilities is allowed. Second, a set of tools ease both the experimentation workflow and the federation of other IoT deployments, independently of their domain of interest. Apart from the platform specification, this paper presents how this design has been actually instantiated into a cloud-based EaaS platform that has been used for supporting a wide variety of novel experiments targeting different research and innovation challenges. In this respect, this paper summarizes some of the experiences from these experiments and the key performance metrics that this instance of the platform has exhibited during the experimentation.

    F Ganz, S Enshaeifar, D Puschmann, A Ahrabian, T Elsaleh (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.

    Luis Sánchez, Jorge Lanza, Juan Santana, Rachit Agarwal, Pierre Raverdy, Tarek Elsaleh, Yasmin Fathy, SeungMyeong Jeong, Aris Dadoukis, Thanasis Korakis, Stratos Keranidis, Philip O’Brien, Jerry Horgan, Antonio Sacchetti, Giuseppe Mastandrea, Alexandros Fragkiadakis, Pavlos Charalampidis, Nicolas Seydoux, Christelle Ecrepont, Mengxuan Zhao (2018)Federation of Internet of Things Testbeds for the Realization of a Semantically-Enabled Multi-Domain Data Marketplace, In: Sensors18(10) MDPI

    The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.

    Alireza Ahrabian, Tarek Elsaleh, Yasmin Fathy, Payam Barnaghi (2017)Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC, In: 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).

    T Elsaleh, A Gluhak, K Moessner (2011)Service continuity for subscribers of the mobile real world Internet, In: IEEE International Conference on Communications

    The Real World Internet or the Web of Things has brought an approach to integrate wireless sensor devices in a manner that is natural to the Web, where sensors are exposed as addressable web resources like any other web resource. Although there is still a clear deficiency with regards to managing the mobility of the sensor devices in this approach, and how it affects the service and the users interacting with it. The work presented here addresses this issue and aims to provide an approach towards maintaining service continuity of migrating sensor devices in a framework that builds upon the concept of the 'Web of Things'. © 2011 IEEE.

    S De, T Elsaleh, P Barnaghi, S Meissner (2012)An Internet of Things Platform for Real-World and Digital Objects, In: Scalable Computing: Practice and Experience13(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.

    Thorben Iggena, Eushay Bin Ilyas, Marten Fischer, Ralf Tönjes, Tarek Elsaleh, Roonak Rezvani, Narges Pourshahrokhi, Stefan Bischof, Andreas Fernbach, Josiane Xavier Parreira, Patrik Schneider, Pavel Smirnov, Martin Strohbach, Hien Truong, Aurora González-Vidal, Antonio F Skarmeta, Parwinder Singh, Michail J Beliatis, Mirko Presser, Juan A Martinez, Pedro Gonzalez-Gil, Marianne Krogbæk, Sebastian Holmgård Christophersen (2021)IoTCrawler: Challenges and Solutions for Searching the Internet of Things, In: Sensors (Basel, Switzerland)21(5)1559 MDPI

    Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

    V. Janieko, R. Rezvani, N. Pourshahrokhi, Shirin Enshaeifar, M. Krogbæk, S.H Christophersen, Tarek Elsaleh, Payam Barnaghi (2020)Enabling Context-Aware Search using Extracted Insights from IoT Data Streams, In: GIoTS 2020

    The rapid growth in collecting and sharing sensory observation form the urban environments provides opportunities to plan and manage the services in the cities better and allows citizens to also observe and understand the changes in their surrounding in a better way. The new urban sensory data also creates opportunities for further application and service development by creative industries and start-ups. However, as the size and diversity of this data increase, finding and accessing the right set of data in a timely manner is becoming more challenging. This paper describes a search engine designed for indexing, searching and accessing urban sensory data. We present the key feature and architecture of the system and demonstrate some of the functionalities that are provided by searching for raw sensory observations and also pattern search functions that are enabled by a pattern analysis algorithm, supported by monitoring of data streams for changes in quality of information and remediation.

    M Bermudez-Edo, Tarek Elsaleh, Payam Barnaghi, K Taylor (2017)IoT-Lite: A Lightweight Semantic Model for the Internet of Things and its Use with Dynamic Semantics, In: 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.

    T. Elsaleh, M. Bermudez-Edo, S. Enshaeifar, S. T. Acton, R. Rezvani, P. Barnaghi (2019)IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams, In: Proceedings of the 3rd Global IoT Summit (GIoTS 2019) Institute of Electrical and Electronics Engineers (IEEE)

    In recent years, the development and deployment of Internet of Things (IoT) devices has led to the generation of large volumes of real world data. Analytical models can be used to extract meaningful insights from this data. However, most of IoT data is not fully utilised, which is mainly due to interoperability issues and the difficulties to analyse data collected by heterogeneous resources. To overcome this heterogeneity, semantic technologies are used to create common models to share various data originated from heterogeneous sources. However, semantics add further overhead to data delivery, and the processing time to annotate the data with the model can increase the latency and complexity in publishing and querying the annotated data. In this paper, we present a lightweight semantic model to annotate IoT streams. The metadata descriptions that are provided in the models are used for search and discovery of the data using various attributes such as value and type. The proposed model extends commonly used ontologies such as W3C/OGC SSN ontology and its recent lightweight core, SOSA, and includes concepts to describe streaming IoT data. We also show use cases, tools and applications where the proposed model has been used.