Arkady B. Zaslavsky

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.

Mining data streams: a review

The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.

Towards a theory of context spaces

We propose initial work on a conceptual framework for context-aware systems, towards a general context model to aid thinking, describing, manipulating and utilizing context. Much work on context-aware computing have utilized context in specialized applications, with application-specific software (and perhaps hardware) and specialized context representation and manipulation. Context-aware systems are becoming increasingly important, and emerging research has begun to look at context-aware systems more generally, independently of specific applications, including context middleware and toolkits and ontologies for describing context.

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating “things” or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

OpenIoT: Open Source Internet-of-Things in the Cloud

Despite the proliferation of Internet-of-Things (IoT) platforms for building and deploying IoT applications in the cloud, there is still no easy way to integrate heterogeneous geographically and administratively dispersed sensors and IoT services in a semantically interoperable fashion. In this paper we provide an overview of the OpenIoT project, which has developed and provided a first-of-kind open source IoT platform enabling the semantic interoperability of IoT services in the cloud. At the heart of OpenIoT lies the W3C Semantic Sensor Networks (SSN) ontology, which provides a common standards-based model for representing physical and virtual sensors. OpenIoT includes also sensor middleware that eases the collection of data from virtually any sensor, while at the same time ensuring their proper semantic annotation. Furthermore, it offers a wide range of visual tools that enable the development and deployment of IoT applications with almost zero programming. Another key feature of OpenIoT is its ability to handle mobile sensors, thereby enabling the emerging wave of mobile crowd sensing applications. OpenIoT is currently supported by an active community of IoT researchers, while being extensively used for the development of IoT applications in areas where semantic interoperability is a major concern.

An approach to data fusion for context awareness

We propose and develop an approach modeled with multi-attribute utility theory for sensor fusion in context-aware environments. Our approach is distinguished from existing general purpose fusion techniques by a number of factors including a general underlying context model it is built upon and a set of heuristics it covers. The technique is developed for context-aware applications and we argue that it provides various advantages for data fusion in context-aware scenarios. We experimentally evaluate our approach with actual use cases using real sensors.

Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

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 substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.

Using On-the-Move Mining for Mobile Crowdsensing

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.

Multiple-Agent Perspectives in Reasoning About Situations for Context-Aware Pervasive Computing Systems

In open heterogeneous context-aware pervasive computing systems, suitable context models and reasoning approaches are necessary to enable collaboration and distributed reasoning among agents. This paper proposes, develops, and demonstrates the following: 1) a novel context model and reasoning approach developed with concepts from the state-space model, which describes context and situations as geometrical structures in a multidimensional space; and 2) a context algebra based on the model, which enables distributed reasoning by merging and partitioning context models that represent different perspectives of computing entities over the object of reasoning. We show how merging and reconciling different points of view over context enhances the outcomes of reasoning about the context. We develop and evaluate our proposed algebraic operators and reasoning approaches with cases using real sensors and with simulations. We embed agents and mobile agents with these modeling and reasoning capabilities, thus facilitating context-aware and adaptive mobile agents operating in open pervasive environments.

Submission of transactions from mobile workstations in a cooperative multidatabase processing environment

In a multidatabase environment with mobile computers involved, the nature of computing is such that the user may not wait for the submitted global transaction to complete before disconnecting from the network. In this paper, a basic architectural framework to support transaction management in multidatabase systems is proposed and discussed. A simple message and queuing facility is suggested which provides a common communication and data exchange protocol to effectively manage global transactions submitted by mobile workstations (MWS). The state of global transactions is modelled through the use of transaction sub-queues. The proposed strategy allows an MWS to submit global transactions and then disconnect itself from the network to perform some other tasks, thereby increasing processing parallelism and independence.<<ETX>>