Energy Efficient Scheduling in Wireless Sensor Networks for Periodic Data Gathering

Abstract

Nodes in the wireless sensor networks are battery operated, and thus, the efficient use of the node's energy during wireless communication is pivotal for the long battery life. This paper addresses the challenges in energy-efficient data aggregation and transmission scheduling for each node by using time division multiple access (TDMA). We introduce multi-channel TDMA scheduling algorithms with the objective of minimizing the total energy consumption in the network. The proposed algorithms utilize multiple radio channels to bestow efficient scheduling while eliminating collisions and overhearing. First, we formulate an integer linear programming (ILP) algorithm that finds the minimum bound for the network's energy consumption. Then, we propose a near-optimal heuristic algorithm based on backtracking, which uses memoization to spurn the suboptimal schedules. Subsequently, we propose a computationally efficient heuristic algorithm by using Langford subset generation. The algorithm reduces the energy consumption in each timeslot while avoiding revisiting the same timeslot. We conducted extensive simulations to evaluate three proposed algorithms. The simulation results demonstrate that the proposed heuristic algorithms provide performance comparable to the optimum results of the ILP algorithm and reduce the magnitude of computation time.