Classification Using Efficient LU Decomposition in Sensornets

We consider the popular application of detection, classification and tracking and their feasibility in resource constrained sensornets. We concentrate on the classification aspect, by decomposing the complex, computationally intensive signal processing Maximum-A-Posterior (MAP) classifier into simpler computationally and communicationally load balanced procedures, using a clustering approach. LU decomposition is an efficient approach for computing the inverse of covariance matrices required in the MAP classifier. We thus explore feasibility of LU decomposition in sensornets. We present power-aware and load balanced techniques for LU decomposition of the covariance matrices in sensornets alongwith their analytical and power consumption analyses. KEY WORD

A Service Location Problem with QoS Constraints

Abstract-In this paper, we present and discuss a novel service location problem that satisfies user demand for services, by installing services on nodes in the network, which can also meet quality of service (QoS) constraints of throughput and delay defined by the user. Service location problems can arise in various different networks, we specifically consider service location in Cisco’s Application Oriented Networks (AON). Our goal is to formulate and present the service location problem as an Integer Linear Programming (ILP) problem and solve it optimally for networks with small number of nodes. Our ILP and results meet “real-world ” demands of traffic splitting (due to link layer capacity) and service federation (to incur minimal service installation cost). I

An Efficient MAP Classifier for Sensornets

Abstract. The classification phase is computationally intensive and frequently recurs in tracking applications in sensor networks. Most related work uses traditional signal processing classifiers, such as Maximum A Posterior (MAP) classifier. Naïve formulations of MAP are not feasible for resource constraint sensornet nodes. In this paper, we study computationally efficient methods for classification. We propose to use one-sided Jacobi iterations for eigen value decomposition of the covariance matrices, the inverse of which are needed in MAP classifier. We show that this technique greatly simplifies the execution of MAP classifier and makes it a feasible and efficient choice for sensornet applications

A Localization System using Wireless Network Sensors: A Comparison of Two Techniques

application that has received a lot of attention over recent years. With the emergence of wireless networks and mobile computing devices, providing location-aware technology and services to new applications has become important for developers. Recent advances in sensor technology have allowed wireless sensor networks to provide location services. Many applications of wireless sensor networks assume that the devices are location-aware. In this paper, we discuss the Ferret localization system. Using only the radio features of the sensors, the Ferret system provides two techniques for locating an object. The system provides good results, but several extensions are discussed to make it more scalable and reliable.

Opportunistic Networks: The Concept and Research

Abstract: We introduce a new paradigm and a new technology, which we call opportunistic networks or oppnets. An oppnet grows from its seed—the original set of nodes employed together at the time of the initial oppnet deployment. The seed grows into a larger network by extending invitations to join the oppnet to foreign devices, node clusters, or networks that it is able to contact. A new node that becomes a full-fledged member, or helper, may be allowed to invite external nodes. All helpers collaborate on realizing the goals of the oppnet. They can be employed to execute different kinds of tasks, even though in general they were not designed to become elements of the oppnet that invited them. Oppnets, as an epitome of pervasive computing, are subject to significant privacy and security challenges, inherent to all pervasive systems. To the best of our knowledge, we are the first to define and investigate opportunistic networks