Residual energy aware mobile data gathering in wireless sensor networks

Xuan Rao (), Hongyu Huang (), Jiqiang Tang () and Huan Zhao ()
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Xuan Rao: Chongqing University
Hongyu Huang: Chongqing University
Jiqiang Tang: Chongqing University
Huan Zhao: Chongqing University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2016, vol. 62, issue 1, No 4, 41 pages

Abstract: Abstract The intrinsic characteristic of wireless sensor networks is the power limitation of sensor nodes. The most difficult challenge is how to save energy of sensor nodes so that the lifetime of a sensor network will be prolonged. A mobile data collector (MDC) is introduced to achieve this goal. We suppose that all sensor nodes are kept static once deployed, and a single MDC traverses the network to reduce the communication of relaying data among sensors. In general, we need to consider two factors when designing a traveling path of a MDC, i.e., the data overflow on a sensor node and the timeliness of each data. In this paper, we aim to prolong lifetime of a sensor network by designing heuristic traveling paths of the MDC under these two constraints. It is obviously that a fixed MDC path leads to a quicker energy consumption of the nodes near that path. So we propose an iterative scheme which determines the traveling path of the MDC before each round of the data gathering. For each data gathering round, our scheme consists of four steps. First we iteratively partition the network into clusters by spectral clustering, and then select a cluster head as the polling point which is a special position for collecting data depended on residual energy. Following that, we construct a balanced data relay tree in each cluster. Last, we design a shortest path for the MDC. Since the paths of MDC are different in each round, the lifetime of the sensor network can be prolonged. Simulations reveal that our method is better than the existing methods and prolong the lifetime of wireless sensor network.

Keywords: Residual energy awareness; Spectral clustering; Data relay tree; Path scheduling (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11235-015-9980-1

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