Energy-Efficient Optimization Strategies For Wireless Sensor Network

Energy constraints in a wireless sensor network (WSN) are crucial issues. Energy saving and energy efficiency for a sensor node are one of the key challenges in WSN.In this master dissertation the energy optimization problem for the sensor node is concerned, and the objective is to obtain the long-term average maximum throughput per energy consumption. First, we analyze the energy conservation optimization problem in the background of energy efficient transmission strategy over fading channels. For less energy consumption on data sensing, receiving and sending we refer to a cross-layer mechanism which dynamically turns on or off different components, adjusts transmit power and modulation level of the sensor node while maintaining required performance. Then, the problem is modified by introducing dynamic power management (DPM) technique and modeled as an average reward Markov decision process (MDP). Combined with simulated annealing (SA), Q learning algorithm is proposed to solve the energy conservation optimization problem with average performance criteria. Finally, the simulation results show that the approach in this dissertation is more efficient than adaptive transmission policy or DPM policy. As the energy consumption of the sensor node can be well balanced while the throughput doesn't decrease significantly.In contrast to the point-to-point communication, we consider transmitters simultaneously communicate to one receiver in multinode scenario. The channel link experienced by one node depends on the decision employed by other nodes in multinode scenarios. Hence, the optimal equilibrium solution generally depends on the policy employed by the other nodes. We further extend the algorithm to solve the optimization problem in a multinode scenario by distribution Q learning. The simulation results show that the algorithm can achieve good performance.