Energy efficient scheduling and allocation of tasks in sensor cloud

Wireless Sensor Network (WSN) is a class of ad hoc networks that has capability of self-organizing, in-network data processing, and unattended environment monitoring. Sensor-cloud is a cloud of heterogeneous WSNs. It is attractive as it can change the computation paradigm of wireless sensor networks. In Sensor-Cloud, to gain profit from underutilized WSNs, multiple WSN owners collaborate to provide a cloud service. Sensor Cloud users can simply rent the sensing services which eliminates the cost of ownership, enabling the usage of large scale sensor networks become affordable. The nature of Sensor-Cloud enables resource sharing and allows virtual sensors to be scaled up or down. It abstracts different platforms hence giving the impression of a homogeneous network. Further in multi-application environment, users of different applications may require data based on different needs. Hence scheduling scheme in WSNs is required which serves maximum users of various applications. We have proposed a scheduling scheme suitable for the multiple applications in Sensor Cloud. Scheduling scheme is based on TDMA which considers fine granularity of tasks. The performance evaluation shows the better response time, throughput and overall energy consumption as compared to the base case we developed. On the other hand, to minimize the energy consumption in WSN, we design an allocation scheme. In Sensor Cloud, we consider sparsely and densely deployed WSNs working together. Also, in a WSN there might be sparsely and densely deployed zones. Based on spatial correlation and with the help of Voronoi diagram, we turn on minimum number of sensors hence increasing WSN lifetime and covering almost 100 percent area. The performance evaluation of allocation scheme shows energy efficiency by selecting fewer nodes in comparison to other work --Abstract, page iv

Sensor authentication in collaborating sensor networks

In this thesis, we address a new security problem in the realm of collaborating sensor networks. By collaborating sensor networks, we refer to the networks of sensor networks collaborating on a mission, with each sensor network is independently owned and operated by separate entities. Such networks are practical where a number of independent entities can deploy their own sensor networks in multi-national, commercial, and environmental scenarios, and some of these networks will integrate complementary functionalities for a mission. In the scenario, we address an authentication problem wherein the goal is for the Operator Oi of Sensor Network Si to correctly determine the number of active sensors in Network Si. Such a problem is challenging in collaborating sensor networks where other sensor networks, despite showing an intent to collaborate, may not be completely trustworthy and could compromise the authentication process. We propose two authentication protocols to address this problem. Our protocols rely on Physically Unclonable Functions, which are a hardware based authentication primitive exploiting inherent randomness in circuit fabrication. Our protocols are light-weight, energy efficient, and highly secure against a number of attacks. To the best of our knowledge, ours is the first to addresses a practical security problem in collaborating sensor networks. --Abstract, page iii

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in- and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving. Copyright 2011 ACM