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Collaborative broadcasting and compression in cluster-based wireless sensor networks

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Published:01 August 2007Publication History
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Abstract

Achieving energy efficiency to prolong the network lifetime is an important design criterion for wireless sensor networks. In this article, we propose a novel approach that exploits the broadcast nature of the wireless medium for energy conservation in spatially correlated wireless sensor networks. Since wireless transmission is inherently broadcast, when one sensor node transmits, other nodes in its coverage area can receive the transmitted data. When data collected by different sensors are correlated, each sensor can utilize the data it overhears from other sensors to compress its own data and conserve energy in its own transmissions. We apply this idea to a class of cluster-based wireless sensor networks in which each sensing node transmits collected data directly to its cluster head using time division multiple access (TDMA). We formulate the problem in which sensors in each cluster collaborate their transmitting, receiving, and compressing activities to optimize their lifetimes. We show that this lifetime optimization problem can be solved by a sequence of linear programming problems. We also propose a heuristic scheme which has low complexity and achieves near optimal performance. Important characteristics of wireless sensor networks such as node startup cost and packet loss due to transmission errors are also considered. Numerical results show that by exploiting the broadcast nature of the wireless medium, our control schemes achieve significant improvement in the sensors' lifetimes.

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  1. Collaborative broadcasting and compression in cluster-based wireless sensor networks

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      Bernard Kuc

      Collaboration among computational nodes, whether they are wireless or connected, mobile or stationary, is almost always beneficial. Practicalities do, however, limit the level of collaboration possible. In this paper, the authors consider wireless sensor networks (WSNs), aiming to extend remote sensor node battery life. They propose to do this by reducing the energy expended in data transmission through compression of the payload using data in prior transmissions by colocated sensors. The authors start by showing that viewing cluster-based WSNs as communicating using point-to-point channels unnecessarily limits the scope of any analysis, as nodes tend to have omnidirectional antennas. The motivation offered for compression is that spatially colocated sensors tend to exhibit high degrees of data correlation. The authors factor in both the energy consumed for reception of peer transmissions and the energy expended in compression of the data. By formulating an energy-consumption function based on the location of sensor and relay nodes, the authors maximize the time before the next sensor runs out of power. Two optimization schemes are discussed in detail: the first is based on linear programming; the second is a heuristic algorithm that, despite being significantly simpler, exhibits very good results under simulation testing. The simulation analysis is thorough considering such aspects as: spacial sensor layout; compression based on more than one peer's transmission; time to nth sensor's death; compression ratios; cluster sizes; and energy consumption rates for data reception. Even packet header overhead and data loss are analyzed. Despite the supposed potential for improvement, at only the cost of increased complexity, the authors fail to consider some of the practical implications. Is it not cheaper to double the battery capacity by removing the cost of reception capability at the remote sensors__?__ This is pertinent, as neither the analysis nor examples necessitate bidirectional communication from the remote sensors. Online Computing Reviews Service

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