Active node determination for correlated data gathering in wireless sensor networks

Cataloged from PDF version of article.In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. Given that the sensor nodes that are selected as active form a connected wireless network, the inactive sensor nodes can be turned off. Allowing some sensor nodes to be active and some sensor nodes inactive interchangably during the lifecycle of the application helps the wireless sensor network to have a longer lifetime. The problem of determining a set of active sensor nodes in a correlated data environment for a fully operational wireless sensor network can be formulated as an instance of the connected correlation-dominating set problem. In this work, our contribution is twofold; we propose an effective and runtime-efficient iterative improvement heuristic to solve the active sensor node determination problem, and a benefit function that aims to minimize the number of active sensor nodes while maximizing the residual energy levels of the selected active sensor nodes. Extensive simulations we performed show that the proposed approach achieves a good performance in terms of both network lifetime and runtime efficiency. © 2012 Elsevier B.V. All rights reserved

Opportunistic source coding for data gathering in wireless sensor networks

We propose a jointly opportunistic source coding and oppor tunistic routing (OSCOR) protocol for correlated data gathering in wireless sensor networks. OSCOR improves data gathering efficiency by exploiting opportunistic data compression and multi-user diversity on wireless broadcast. OSCOR attacks challenges across network protocol layers by incorporating a slightly modified 802.11 MAC, a distributed source coding scheme based on Lempel-Ziv code and network coding, and a node compression ratio dependent metric combined with a modified Dijkstra's algorithm for path selection. We simulate OSCOR's performance and show it reduces the number of transmissions by nearly 25% compared with other schemes in small networks

Active node determination for correlated data gathering in wireless sensor networks

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 53-55.In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. However, the sensor nodes that are selected as active should form a connected wireless network in order to transmit the collected correlated data to the data gathering node. The problem of determining a set of active sensor nodes in a correlated data environment for a fully operational wireless sensor network can be formulated as an instance of the connected correlation-dominating set problem. In this work, our contribution is twofold; we propose an effective and runtime efficient iterative improvement heuristic to solve the active sensor node determination problem and a benefit function that aims to minimize the number of active sensor nodes while maximizing the residual energy levels of the selected active sensor nodes. Extensive simulations we performed show that the proposed approach can achieve a good performance in terms of both network lifetime and runtime efficiency.Karasabun, EfeM.S

Networked Slepian-Wolf: theory, algorithms, and scaling laws

Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the Slepian-Wolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closed-form solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NP-complete. For large networks, in some simplified scenarios, the total costs associated with Slepian-Wolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed

Network correlated data gathering with explicit communication: NP-completeness and algorithms

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal

Opportunistic source coding for data gathering in wireless sensor networks

We propose a jointly opportunistic source coding and oppor tunistic routing (OSCOR) protocol for correlated data gathering in wireless sensor networks. OSCOR improves data gathering efficiency by exploiting opportunistic data compression and multi-user diversity on wireless broadcast. OSCOR attacks challenges across network protocol layers by incorporating a slightly modified 802.11 MAC, a distributed source coding scheme based on Lempel-Ziv code and network coding, and a node compression ratio dependent metric combined with a modified Dijkstra's algorithm for path selection. We simulate OSCOR's performance and show it reduces the number of transmissions by nearly 25% compared with other schemes in small networks