Context-aware Authorization in Highly Dynamic Environments

Highly dynamic computing environments, like ubiquitous and pervasive computing environments, require frequent adaptation of applications. Context is a key to adapt suiting user needs. On the other hand, standard access control trusts users once they have authenticated, despite the fact that they may reach unauthorized contexts. We analyse how taking into account dynamic information like context in the authorization subsystem can improve security, and how this new access control applies to interaction patterns, like messaging or eventing. We experiment and validate our approach using context as an authorization factor for eventing in Web service for device (like UPnP or DPWS), in smart home security

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

Supporting service discovery, querying and interaction in ubiquitous computing environments.

In this paper, we contend that ubiquitous computing environments will be highly heterogeneous, service rich domains. Moreover, future applications will consequently be required to interact with multiple, specialised service location and interaction protocols simultaneously. We argue that existing service discovery techniques do not provide sufficient support to address the challenges of building applications targeted to these emerging environments. This paper makes a number of contributions. Firstly, using a set of short ubiquitous computing scenarios we identify several key limitations of existing service discovery approaches that reduce their ability to support ubiquitous computing applications. Secondly, we present a detailed analysis of requirements for providing effective support in this domain. Thirdly, we provide the design of a simple extensible meta-service discovery architecture that uses database techniques to unify service discovery protocols and addresses several of our key requirements. Lastly, we examine the lessons learnt through the development of a prototype implementation of our architecture

Context-aware Dynamic Discovery and Configuration of 'Things' in Smart Environments

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or `things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5 to 10 years. To be able to develop IoT applications, such `things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of `things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of {Things} (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.Comment: Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence book series, Springer Berlin Heidelberg, 201

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs