Abstract
We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol.
Highlights
IntroductionIt is obvious that data compression can contribute to reducing radio transmission energy, prolonging the battery life time of the Wireless sensor networks (WSNs)
According to a report by U.S Department of Energy (DOE) and the U.S Environmental ProtectionAgency (EPA), commercially available technologies can help to achieve energy savings up to 25% [1].Wireless sensor networks (WSNs) have been regarded as one of the most promising solutions to reduce energy consumption by monitoring environment and dynamically adjusting network operation with respect to energy efficiency
We focus on designing an energy-optimized data collection scheme by taking a selective data compression strategy in stationary sensor networks that can lead to a significant reduction in network-wide radio and CPU energy consumption
Summary
It is obvious that data compression can contribute to reducing radio transmission energy, prolonging the battery life time of the WSN. Most data compression algorithms require a large memory size for computation, limiting the application to a WSN. The authors of [17] handle this challenge by presenting a computationally-efficient compression algorithm, named Sensor Lempel-Ziv-Welch coding (S-LZW), which fits into some limited RAM in real sensor motes. Researchers developed another compression algorithm for a WSN, namely Run-Length Encoding with Structured Transpose (RLE-ST) [17]. Compared to S-LZW, this algorithm takes less computation time, but instead, leads to a lower compression ratio
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