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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 235289, 16 pages
Research Article

Energy Dependent Divisible Load Theory for Wireless Sensor Network Workload Allocation

1College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China
2School of Computer Science and Technology, Shaoxing University, Shaoxing 312000, China
3School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney 2052, Australia

Received 31 October 2012; Accepted 4 December 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Haiyan Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The wireless sensor network (WSN), consisting of a large number of microsensors with wireless communication abilities, has become an indispensable tool for use in monitoring and surveillance applications. Despite its advantages in deployment flexibility and fault tolerance, the WSN is vulnerable to failures due to the depletion of limited onboard battery energy. A major portion of energy consumption is caused by the transmission of sensed results to the master processor. The amount of energy used, in fact, is related to both the duration of sensing and data transmission. Hence, in order to extend the operation lifespan of the WSN, a proper allocation of sensing workload among the sensors is necessary. An assignment scheme is here formulated on the basis of the divisible load theory, namely, the energy dependent divisible load theory (EDDLT) for sensing workload allocations. In particular, the amount of residual energies onboard sensors are considered while deciding the workload assigned to each sensor. Sensors with smaller amount of residual energy are assigned lighter workloads, thus, allowing for a reduced energy consumption and the sensor lifespan is extended. Simulation studies are conducted and results have illustrated the effectiveness of the proposed workload allocation method.