Abstract
Data communication incurs the highest energy cost in wireless sensor networks, and restricts the application of wireless sensor networks. Data compression is a promising technique that can reduce the amount of data exchanged between nodes and results in energy saving. However, there is a lack of effective methods to evaluate the efficiency of data compression algorithms and to increase nodes’ energy efficiency. The energy saving of nodes is related to both hardware and software, this article proposes a new scheme for evaluating energy efficiency of data compression in wireless sensor networks according to the node’s hardware and software. The relationship between the energy efficiency and the hardware and software factors is expressed by a formula. In this formula, energy efficiency can be improved by increasing the compression ratio and decreasing the ratio of s/ k, in which k represents the node’s hardware factor related to energy consumption of processor, wireless module, and so on and s represents the software factor that reflects the energy consumption of the algorithm. Based on the scheme, a mechanism is proposed to improve the node’s energy efficiency by selecting effective algorithms in accordance with the node’s radio frequency power. The feasibility of the scheme is demonstrated with lossless data compression algorithms on the MSP430F2618 processor.
Highlights
Data compression is very important to improve the energy efficiency of data storage and wireless communication
As the transmission of data consumes the majority of energy of nodes and the energy required for transmitting a single bit is approximately equal to the energy for execution of 4000 instructions,[2] data compression is used naturally in wireless sensor networks (WSNs) node energy savings
Eincome is equal to the difference between Esave and Ecmp, Esave is the energy saved by compressing data, and Ecmp is the energy consumption for running algorithms, as follows
Summary
Data compression is very important to improve the energy efficiency of data storage and wireless communication. The energy efficiency evaluation scheme of the data compression algorithm must be based on the hardware and software considerations in WSNs. Through analyzing the three kinds of energy consumption related to the transmitting raw data, the transmitting compressed data, and the compressing data in WSNs, it can be found that in addition to the compression ratio, the complexity and operation environment of algorithms and wireless communication environment are non-negligible factors. For the existing shortages of evaluation schemes, appropriate improvements are necessary
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