(Received: 2018-11-08, Revised: 2018-12-17 , Accepted: 2018-12-23)
This paper examines the problem of Task Mapping and Scheduling (TMS) in Wireless Sensor Networks (WSNs). The application, which is supposed to be executed in WSNs, can be divided into interdependent tasks. The key objectives of TMS in WSNs are the improvement of execution time, energy consumption and network lifetime. A modified version of Random Bit Climbing (RBC) optimization method, also called λ-Modified Random Bit Climbing (λ-mRBC), is developed to get better and faster optimal or near-optimal solution. In the proposed λ- mRBC method, a new operator, called transposition operator, is added to improve the exploration of search space and hence to escape from the local optima. The deepth of exploration is controlled by using a single parameter (λ). Firstly, a number of sensor nodes is selected to cooperatively execute the application with the purpose of improving the network lifetime. After that, the proposed λ-mRBC method is performed to get the optimal or near-optimal task/sensor pair solution, so that the execution time and energy consumption are minimized. The simulation results show that λ-mRBC method enhances the TMS performance. Compared with the traditional RBC method, the proposed λ-mRBC method converges to better fitness value, make-span and total energy consumption by 19.1%, 19.6% and 22.3%, respectively. Furthermore, the network lifetime is prolonged through using the proposed selection algorithm. The distribution of remaining energy among sensor nodes is improved about three times, compared with the random selection scheme. Furthermore, compared with the random selection, the number of neighbours for sensor nodes is improved by 20.1% using the proposed selection algorithm.

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