Modeling context aware features for pervasive computing

For more than two decades, research in the development of context-aware applications has gained significant attention, in which the context aware should be taken into account to adapt them to the requirements of the environment and users. However, advances in application models to support the development of these systems have not kept up. Researchers have been building and deploying context-aware applications with their behaviors often tailored to specific problem domains, by designing the anticipated context and the desired application behavior. Motivated by the raised facts, this paper presents a context aware model with ability to interact and to adapt smartly and dynamically to environment and needs of users. We are revisiting the context life cycle, especially the representation and the modeling of context features, regarding the relations within these features and the systems functionalities. Different kinds of components adaptation are described and scenarios are presented to illustrate these adaptations. As a proof of concept, we have simulated the context model in health care systems and show the results

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

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

Context modelling for natural Human Computer Interaction applications in e-health

The conception of IoT (Internet of Things) is accepted as the future tendency of Internet among academia and industry. It will enable people and things to be connected at anytime and anyplace, with anything and anyone. IoT has been proposed to be applied into many areas such as Healthcare, Transportation，Logistics, and Smart environment etc. However, this thesis emphasizes on the home healthcare area as it is the potential healthcare model to solve many problems such as the limited medical resources, the increasing demands for healthcare from elderly and chronic patients which the traditional model is not capable of. A remarkable change in IoT in semantic oriented vision is that vast sensors or devices are involved which could generate enormous data. Methods to manage the data including acquiring, interpreting, processing and storing data need to be implemented. Apart from this, other abilities that IoT is not capable of are concluded, namely, interoperation, context awareness and security & privacy. Context awareness is an emerging technology to manage and take advantage of context to enable any type of system to provide personalized services. The aim of this thesis is to explore ways to facilitate context awareness in IoT. In order to realize this objective, a preliminary research is carried out in this thesis. The most basic premise to realize context awareness is to collect, model, understand, reason and make use of context. A complete literature review for the existing context modelling and context reasoning techniques is conducted. The conclusion is that the ontology-based context modelling and ontology-based context reasoning are the most promising and efficient techniques to manage context. In order to fuse ontology into IoT, a specific ontology-based context awareness framework is proposed for IoT applications. In general, the framework is composed of eight components which are hardware, UI (User Interface), Context modelling, Context fusion, Context reasoning, Context repository, Security unit and Context dissemination. Moreover, on the basis of TOVE (Toronto Virtual Enterprise), a formal ontology developing methodology is proposed and illustrated which consists of four stages: Specification & Conceptualization, Competency Formulation, Implementation and Validation & Documentation. In addition, a home healthcare scenario is elaborated by listing its well-defined functionalities. Aiming at representing this specific scenario, the proposed ontology developing methodology is applied and the ontology-based model is developed in a free and open-source ontology editor called Protégé. Finally, the accuracy and completeness of the proposed ontology are validated to show that this proposed ontology is able to accurately represent the scenario of interest

Software framework for the development of context-aware reconfigurable systems

In this project we propose a new software framework for the development of context-aware and secure controlling software of distributed reconfigurable systems. Context-awareness is a key feature allowing the adaptation of systems behaviour according to the changing environment. We introduce a new definition of the term “context” for reconfigurable systems then we define a new context modelling and reasoning approach. Afterwards, we define a meta-model of context-aware reconfigurable applications that paves the way to the proposed framework. The proposed framework has a three-layer architecture: reconfiguration, context control, and services layer, where each layer has its well-defined role. We define also a new secure conversation protocol between distributed trustless parts based on the blockchain technology as well as the elliptic curve cryptography. To get better correctness and deployment guarantees of applications models in early development stages, we propose a new UML profile called GR-UML to add new semantics allowing the modelling of probabilistic scenarios running under memory and energy constraints, then we propose a methodology using transformations between the GR-UML, the GR-TNCES Petri nets formalism, and the IEC 61499 function blocks. A software tool implementing the methodology concepts is developed. To show the suitability of the mentioned contributions two case studies (baggage handling system and microgrids) are considered.In diesem Projekt schlagen wir ein Framework für die Entwicklung von kontextbewussten, sicheren Anwendungen von verteilten rekonfigurierbaren Systemen vor. Kontextbewusstheit ist eine Schlüsseleigenschaft, die die Anpassung des Systemverhaltens an die sich ändernde Umgebung ermöglicht. Wir führen eine Definition des Begriffs ``Kontext" für rekonfigurierbare Systeme ein und definieren dann einen Kontextmodellierungs- und Reasoning-Ansatz. Danach definieren wir ein Metamodell für kontextbewusste rekonfigurierbare Anwendungen, das den Weg zum vorgeschlagenen Framework ebnet. Das Framework hat eine dreischichtige Architektur: Rekonfigurations-, Kontextkontroll- und Dienste-Schicht, wobei jede Schicht ihre wohldefinierte Rolle hat. Wir definieren auch ein sicheres Konversationsprotokoll zwischen verteilten Teilen, das auf der Blockchain-Technologie sowie der elliptischen Kurven-Kryptographie basiert. Um bessere Korrektheits- und Einsatzgarantien für Anwendungsmodelle zu erhalten, schlagen wir ein UML-Profil namens GR-UML vor, um Semantik umzufassen, die die Modellierung probabilistischer Szenarien unter Speicher- und Energiebeschränkungen ermöglicht. Dann schlagen wir eine Methodik vor, die Transformationen zwischen GR-UML, dem GR-TNCES-Petrinetz-Formalismus und den IEC 61499-Funktionsblöcken verwendet. Es wird ein Software entwickelt, das die Konzepte der Methodik implementiert. Um die Eignung der genannten Beiträge zu zeigen, werden zwei Fallstudien betrachtet

Context-aware gestural interaction in the smart environments of the ubiquitous computing era

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyTechnology is becoming pervasive and the current interfaces are not adequate for the interaction with the smart environments of the ubiquitous computing era. Recently, researchers have started to address this issue introducing the concept of natural user interface, which is mainly based on gestural interactions. Many issues are still open in this emerging domain and, in particular, there is a lack of common guidelines for coherent implementation of gestural interfaces. This research investigates gestural interactions between humans and smart environments. It proposes a novel framework for the high-level organization of the context information. The framework is conceived to provide the support for a novel approach using functional gestures to reduce the gesture ambiguity and the number of gestures in taxonomies and improve the usability. In order to validate this framework, a proof-of-concept has been developed. A prototype has been developed by implementing a novel method for the view-invariant recognition of deictic and dynamic gestures. Tests have been conducted to assess the gesture recognition accuracy and the usability of the interfaces developed following the proposed framework. The results show that the method provides optimal gesture recognition from very different view-points whilst the usability tests have yielded high scores. Further investigation on the context information has been performed tackling the problem of user status. It is intended as human activity and a technique based on an innovative application of electromyography is proposed. The tests show that the proposed technique has achieved good activity recognition accuracy. The context is treated also as system status. In ubiquitous computing, the system can adopt different paradigms: wearable, environmental and pervasive. A novel paradigm, called synergistic paradigm, is presented combining the advantages of the wearable and environmental paradigms. Moreover, it augments the interaction possibilities of the user and ensures better gesture recognition accuracy than with the other paradigms

The Internet of Things: A Review of Enabled Technologies and Future Challenges

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm. 2013 IEEE.This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government(MSIT) (No. 2018-0-01411, A Micro-Service IoTWare Framework Technology Development for Ultra small IoT Device).Scopus2-s2.0-8505888625

Development of a context-aware internet of things framework for remote monitoring services

Asset management is concerned with the management practices necessary to maximise the value delivered by physical engineering assets. Internet of Things (IoT)-generated data are increasingly considered as an asset and the data asset value needs to be maximised too. However, asset-generated data in practice are often collected in non-actionable form. Moreover, IoT data create challenges for data management and processing. One way to handle challenges is to introduce context information management, wherein data and service delivery are determined through resolving the context of a service or data request. This research was aimed at developing a context awareness framework and implementing it in an architecture integrating IoT with cloud computing for industrial monitoring services. The overall aim was achieved through a methodological investigation consisting of four phases: establish the research baseline, define experimentation materials and methods, framework design and development, as well as case study validation and expert judgment. The framework comprises three layers: the edge, context information management, and application. Moreover, a maintenance context ontology for the framework has developed focused on modelling failure analysis of mechanical components, so as to drive monitoring services adaptation. The developed context-awareness architecture is expressed business, usage, functional and implementation viewpoints to frame concerns of relevant stakeholders. The developed framework was validated through a case study and expert judgement that provided supporting evidence for its validity and applicability in industrial contexts. The outcomes of the work can be used in other industrially-relevant application scenarios to drive maintenance service adaptation. Context adaptive services can help manufacturing companies in better managing the value of their assets, while ensuring that they continue to function properly over their lifecycle.Manufacturin

Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications

“Big Data” best characterized by its three features namely “Variety”, “Volume” and “Velocity” is revolutionizing nearly every aspect of our lives ranging from enterprises to consumers, from science to government. A fourth characteristic namely “value” is delivered via the use of smart data analytics over Big Data. One such Big Data Analytics application considered in this thesis is Topic Detection and Tracking (TDT). The characteristics of Big Data brings with it unprecedented challenges such as too large for traditional devices to process and store (volume), too fast for traditional methods to scale (velocity), and heterogeneous data (variety). In recent times, cloud computing has emerged as a practical and technical solution for processing big data. However, while deploying Big data analytics applications such as TDT in cloud (called cloud-based TDT), the challenge is to cost-effectively orchestrate and provision Cloud resources to meet performance Service Level Agreements (SLAs). Although there exist limited work on performance modeling of cloud-based TDT applications none of these methods can be directly applied to guarantee the performance SLA of cloud-based TDT applications. For instance, current literature lacks a systematic, reliable and accurate methodology to measure, predict and finally guarantee performances of TDT applications. Furthermore, existing performance models fail to consider the end-to-end complexity of TDT applications and focus only on the individual processing components (e.g. map reduce). To tackle this challenge, in this thesis, we develop a layered performance model of cloud-based TDT applications that take into account big data characteristics, the data and event flow across myriad cloud software and hardware resources and diverse SLA considerations. In particular, we propose and develop models to capture in detail with great accuracy, the factors having a pivotal role in performances of cloud-based TDT applications and identify ways in which these factors affect the performance and determine the dependencies between the factors. Further, we have developed models to predict the performance of cloud-based TDT applications under uncertainty conditions imposed by Big Data characteristics. The model developed in this thesis is aimed to be generic allowing its application to other cloud-based data analytics applications. We have demonstrated the feasibility, efficiency, validity and prediction accuracy of the proposed models via experimental evaluations using a real-world Flu detection use-case on Apache Hadoop Map Reduce, HDFS and Mahout Frameworks