EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing

Today, ubiquitously sensing technologies enable inter-connection of physical\ua0objects, as part of Internet of Things (IoT), and provide massive amounts of\ua0data streams. In such scenarios, the demand for timely analysis has resulted in\ua0a shift of data processing paradigms towards continuous, parallel, and multitier\ua0computing. However, these paradigms are followed by several challenges\ua0especially regarding analysis speed, precision, costs, and deterministic execution.\ua0This thesis studies a number of such challenges to enable efficient continuous\ua0processing of streams of data in a decentralized and timely manner.In the first part of the thesis, we investigate techniques aiming at speeding\ua0up the processing without a loss in precision. The focus is on continuous\ua0machine learning/data mining types of problems, appearing commonly in IoT\ua0applications, and in particular continuous clustering and monitoring, for which\ua0we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data\ua0points collected by LiDAR (a distance sensor that creates a 3D mapping of the\ua0environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and\ua0data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version\ua0to reuse the information and prevent redundant computations, (iv) g-Lisco, a\ua0generalized version of Lisco to cluster any data with spatio-temporal locality\ua0by leveraging the implicit ordering of the data, and (v) Amble, a continuous\ua0monitoring solution in an industrial process.In the second part, we investigate techniques to reduce the analysis costs\ua0in addition to speeding up the processing while also supporting deterministic\ua0execution. The focus is on problems associated with availability and utilization\ua0of computing resources, namely reducing the volumes of data, involving\ua0concurrent computing elements, and adjusting the level of concurrency. For\ua0that, we propose three frameworks; (i) DRIVEN, a framework to continuously\ua0compress the data and enable efficient transmission of the compact data in the\ua0processing pipeline, (ii) STRATUM, a framework to continuously pre-process\ua0the data before transferring the later to upper tiers for further processing, and\ua0(iii) STRETCH, a framework to enable instantaneous elastic reconfigurations\ua0to adjust intra-node resources at runtime while ensuring determinism.The algorithms and frameworks presented in this thesis contribute to an\ua0efficient processing of data streams in an online manner while utilizing available\ua0resources. Using extensive evaluations, we show the efficiency and achievements\ua0of the proposed techniques for IoT representative applications that involve a\ua0wide spectrum of platforms, and illustrate that the performance of our work\ua0exceeds that of state-of-the-art techniques

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

RFID in the warehouse:a literature analysis (1995-2010) of its applications, benefits, challenges and future trends

Radio Frequency Identification (RFID) has been identified as a crucial technology for the modern 21st century knowledge-based economy. Some businesses have realised benefits of RFID adoption through improvements in operational efficiency, additional cost savings, and opportunities for higher revenues. RFID research in warehousing operations has been less prominent than in other application domains. To investigate how RFID technology has had an impact in warehousing, a comprehensive analysis of research findings available from articles through leading scientific article databases has been conducted. Articles from years 1995 to 2010 have been reviewed and analysed with respect to warehouse operations, RFID application domains, benefits achieved and obstacles encountered. Four discussion topics are presented covering RFID in warehousing focusing on its applications, perceived benefits, obstacles to its adoption and future trends. This is aimed at elucidating the current state of RFID in the warehouse and providing insights for researchers to establish new research agendas and for practitioners to consider and assess the adoption of RFID in warehousing functions

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed for investigating and mitigating such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Future prospects and challenges in IoT research and development are also highlighted. As demonstrated in the literature, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. Utilizing a blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.Comment: 77 pages, 5 figures, 5 table

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu