Adaptive Indexed Divisible Load Theory for Wireless Sensor Network Workload Allocation

Energy depletion in wireless sensors is a major obstacle for a wireless sensor network (WSN) to operate over an extended period of time. This problem can be extenuated by minimizing the need for high-power transmission from sensors to the master processor. Sensors could be arranged in clusters, and their sensing workloads are properly determined for minimal energy consumption during the sensing and result reporting stages. The divisible load theory (DLT) is applied here to obtain optimal allocation of sensor workloads taking into account the balance of energy used such that the failure of the first sensor can be delayed. Since standard DLT assumes an ordered indexing of the sensors, its direct application in WSNs may result in unbalanced energy usage. Adaptive indexing schemes with the application of DLT, adaptive indexed divisible load theory (AIDLT), are thus proposed to redefine the indices of sensors in each sensing round while calculating the assigned workload portions. Furthermore, adaptations based on transmission distances, sensor residual energies, double ranking of distances with residual energies, and randomized sensor identifications are formulated and evaluated. Simulation results on a cluster of sensors have shown that adaptation based on residual energies outperforms the other indexing schemes while the randomization scheme is the simplest.