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 (ICO) which 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 takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The 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 work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with arXiv:1303.244

Internet of Things: Usage of LiFi and Need for Flow Control Protocol

The Internet of Things (IoT) is the future of Internet. It is the network of physical objects accessed through Internet. The objects have embedded sensors that will capture potentially enormous amounts of data, A processing system inside the object processes the captured data and these processed data are to be transmitted as quickly as possible. Thus there is a requirement for high bandwidth network and appropriate data transfer protocols

The effects of relative importance of user constraints in cloud of things resource discovery: a case study

Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept

Rôle d'une base de connaissance dans SemIoTics, un système autonome contrôlant un appartement connecté

National audienceL'Internet des Objets représente une réalité de plus en plus concrète au fur et à mesure que se déploient de larges réseaux d'objets connectés. Ceux-ci ouvrent de larges perspectives d'applications, mais rencontrent des difficultés en terme d'interopérabilité, de configuration ou de passage à l'échelle. Ces probléma-tiques peuvent être traitées par le recours aux principes du web de données liées, d'où l'émergence d'ontologies dédiées aux applications de l'IoT, comme IoT-O, une ontologie pour l'IoT.Par ailleurs, une description en-richie des systèmes permet d'envisager leur configuration autonome : on parle alors d'autonomic computing. Ce papier présente SemIoTics, un système autonome reposant sur des bases de connaissance pour la gestion d'un appartement connecté. Nous présentons tout d'abord une vision générique d'une architecture de réseaux d'objets connectés qui permet de guider une analyse des travaux à l'interface du web sémantique et de l'IoT. Nous décrivons ensuite les deux bases de connaissances spécialisant IoT-O sur lesquelles s'appuie SemIoTics, et leur relation avec le dispositif expérimental. Enfin, la structure de ce système autonome de domotique est présenté en détails, et mis en relation avec l'architecture identifiée dans l'état de l'art

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

Communication and Content Trust Aware Routing For Clustered IoT Network

Security has become a major concern in practical applications related to Internet of Things, a Trust Aware Routing is found as second line of defence. To ensure a secure and hassle-free communication in IoT, this paper proposes a new routing strategy called as Communication and Content Trust Aware Routing (CCTAR) for Clustered IoT network. CCTAR is applied on a clustered IoT network in which the entire nodes are clustered into different clusters. Distance, initial energy, transmission range, angle of overlap and the sensing range are the fur major metrics used to cluster the network into hierarchical clusters followed by Cluster Head Selection. Next, the Trust Aware routing computes three different trust metrics namely Nobility rust, bilateral trust and Data oriented trust to determine the trustworthiness of Cluster Heads. The experimental evaluation of the proposed mechanism shows its superiority in terms of malicious nodes identification, Storage overhead reduction and Network lifetime improvisation

Contextual variety, Internet-of-things and the choice of tailoring over platform : mass customisation strategy in supply chain management

This paper considers the implications for Supply Chain Management from the development of the Internet of Things (IoT) or Internet Connected Objects (ICO). We focus on the opportunities and challenges arising from consumption data as a result of ICO and how this can be translated into a provider’s strategy of offering different varieties of products. In our model, we consider two possible strategies: tailoring strategy and platform strategy. Tailoring strategy implies that a provider produces multiple varieties of a product that meet consumers’ needs. Platform strategy depicts the provider’s actions in offering a flexible and standardised platform which enables consumers’ needs to be met by incorporating personal ICO data onto various customisable applications independently produced by other providers that could be called on in context and on demand. We derive conditions under which each of the strategies may be profitable for the provider through maximising consumers’ value. We conclude by considering the implications for SCM research and practice including an extension of postponement taxonomies to include the customer as the completer of the product

Distributed spatial indexing for the Internet of Things data management

The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts)

A Generic Framework for Quality-based Autonomic Adaptation within Sensor-based Systems

With the growth of the Internet of Things (IoT), sensor-based systems deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Since network QoS is rarely sufficient to expertise Quality of Observation (QoO), managing such diversity at the application level is a very complex task and requires high levels of experience from application developers. Given this statement, this paper proposes a generic framework for QoO-based autonomic adaptation within sensor-based systems. An abstract architecture is first introduced, intended to bridge the gap between sensors capabilities and application needs thanks to the Autonomic Computing paradigm. Then, the framework is instantiated and practical considerations when implementing an autonomous sensor-based system are given. We illustrate this instantiation with concrete examples of sensor middlewares and IoT platforms