Energy-balanced sampling workload allocation in wireless sensor networks
Computers & Mathematics with Applications
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The quality of an environmental monitoring application can often be measured by the number of samples collected per unit time. This is to guarantee the reconstruction of the monitored signal. For applications that use wireless sensors for untethered monitoring at rural areas, the challenge is to satisfy the sensing quality while maintaining a long-running operation. Due to the spatial locality of the monitored signal and the oftentimes dense deployment of sensors, the samples taken by a set of nearby sensors can be regarded as equivalent. The problem is how to assign the sampling workload to this set of sensors and maintain a long-running operation. This requires taking account of the energy required to sample as well as to transmit and relay the data to the sink. Previous works on energy-aware routing are not applicable due to the added complexity of sampling assignment. In this paper, we formulate this problem as an optimization problem and present the optimal solution. We compare our algorithm with two commonly used sampling workload assignment algorithms for balancing energy consumption through PowerTOSSIM. The results show that our algorithm can achieve as much as a 25 percent improvement in the residual energy of the critical node, which is the node with the lowest residual energy among all nodes.