Maximizing growth codes utility in large-scale wireless sensor networks

Authors:
Yao Zhao;Xin Wang;Jin Zhao;Xiangyang Xue
Affiliations:
School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Shanghai, China;School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Shanghai, China;School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Shanghai, China;School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Shanghai, China
Venue:
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Year:
2010

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Abstract

The goal of Growth Codes proposed by Karma et.al. is to increase the "persistence" of sensed data, so as to promise that data is more likely to reach a data sink. In many "zero-configuration" sensor networks, where the network topology would change very rapidly, Growth Codes are especially useful. However, the design of Growth Codes is based on two assumptions: (1) each sensor node contains only one single-snapshot of the monitored environment, and each packet contains only one sensed symbol; (2) all codewords have the same probability to be received by the sink. Obviously, these two assumptions do not hold in many practical scenarios of large-scale sensor networks, thus the performance of Growth Codes would be sub-optimal. In this paper, we generalize the scenarios to include multi-snapshot and less random encounters. By associating the decimal degree with the codewords, and by using priority broadcast to exchange codewords, we aim to achieve a better performance of Growth Codes over a wider range of sensor networks applications. The proposed approaches are described in detail by means of both analysis and simulations.