Abstract
This paper deals with the power limitations in a wireless sensor network scenario. Concretely, we propose to use a conditional downsampling encoder (CDE) at the sensing nodes as an energy-efficient solution for the communication problem. It exploits the knowledge about the signal structure, which is assumed to be time-correlated, in order to decrease the sampling rate and hence to reduce the number of transmissions within the network. We analytically assess the performance of the CDE in terms of quadratic distortion, from which we derive closed-form expressions when it is combined with one of the two decoders: the step decoder and the predictive decoder. Moreover, we propose two methodologies to design the CDE in order to guarantee a given coding rate. We also compare the CDE, both analytically and experimentally, with other classical decimator techniques, which are the deterministic downsampling encoder and the probabilistic downsampling encoder. Numerical simulation validates our analytical results. Moreover, we compare the obtained quadratic distortion and extract the conclusions of the capabilities of the studied encoding-decoding schemes.
Highlights
1.1 Motivation and previous work Wireless sensor network (WSN) design is currently one of the most challenging topics in the communications field
7 Conclusions In this chapter, we have evaluated the performance of different encoding-decoding strategies in order to reduce the number of transmitted samples and to decrease the power spent in transmission
We have presented them as an energy-efficient solution for the wireless sensor network communication problem
Summary
1.1 Motivation and previous work Wireless sensor network (WSN) design is currently one of the most challenging topics in the communications field. WSNs are severely energy-constrained because they consist of many small, cheap, and powerlimited nodes, whose batteries cannot be recharged in most cases. The application of energy-efficient algorithms turns out to be crucial. Following with this motivation, many energy-efficient strategies can be found in order to mitigate the energy costs and increase the lifetime of the WSN. Energy-aware routing for cooperative WSNs and ad hoc networks [1,2]. These techniques seek the optimum path that minimizes the total spent energy in multihop WSNs
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