Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
A survey on wireless multimedia sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network
Computer Communications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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The availability of low-cost hardware coupled with the advancement of VLSI (Very Large Scale Integration) technology has promoted the development of wireless multimedia sensor network (WMSN). However, the strict constraints in terms of processing power, storage, bandwidth etc. pose a great challenge to data processing in WMSN. Transmitting raw data is very costly while limited processing power prevents sophisticated multimedia processing. Exploring low-overhead data compression technique is a solution towards this problem. In this paper we propose an energy-saving audio data compression technique for WMSN combining wavelet lifting with a newly proposed difference detection technique. We consider the application domain where data are co-related. To exploit this co-relation we propose to run one wavelet round followed by a dynamic number of difference round and thereby, local communication overhead is saved compared to pure wavelet based scheme without sacrificing the target of sending data with less number of bits to the sink. Moreover, in addition to capturing spatial correlation by wavelet, this scheme captures temporal correlation by introducing the difference detection round. Performance of the proposed scheme is evaluated through simulation where efficacy of the scheme is not only evaluated in terms of energy consumption but also by computing SNR. The scheme is compared with one of the existing schemes. The results confirm our scheme's supremacy over the competing scheme both in terms of energy saving and signal reconstruction quality (SNR).