Resource and environment aware sensor communications: Framework, optimization, and applications

This thesis addresses various challenging issues encountered in wireless sensor networks. The most important characteristic in sensor networks is to prolong the network lifetime. However, due to the stringent energy requirement, the network requires highly energy efficient resource allocation. This highly energy-efficient resource allocation requires the application of an energy awareness system. In fact, we envision a broader resource and environment aware optimization in the sensor networks. This framework reconfigures the parameters from different communication layers according to its environment and resource. We first investigate the application of online reinforcement learning in solving the modulation and transmit power selection. We analyze the effectiveness of the learning algorithm by comparing the effective good throughput that is successfully delivered per unit energy as a metric. This metric shows how efficient the energy usage in sensor communication is. In many practical sensor scenarios; maximizing the energy efficient in a single sensor node may not be sufficient. Therefore, we continue to work on the routing problem to maximize the number of delivered packet before the network becomes useless. The useless network is characterized by the disintegrated remaining network. We design a class of energy efficient routing algorithms that explicitly takes the connectivity condition of the remaining network in to account. We also present the distributed asynchronous routing implementation based on reinforcement learning algorithm. This work can be viewed as distributed connectivity-aware energy efficient routing. We then explore the advantages obtained by doing cooperative routing for network lifetime maximization. We propose a power allocation in the cooperative routing called the maximum lifetime power allocation. The proposed allocation takes into account the residual energy in the nodes when doing the cooperation. In fact, our criterion lets the nodes with more energy to help more compared to the nodes with less energy. We continue to look at the problem of cooperation enforcement in ad-hoc network. We show that by combining the repeated game and self learning algorithm, a better cooperation point can be obtained. Finally, we demonstrate an example of channel-aware application for multimedia communication. In all case studies, we employ optimization scheme that is equipped with the resource and environment awareness. We hope that the proposed resource and environment aware optimization framework will serve as the first step towards the realization of intelligent sensor communications. (Abstract shortened by UMI.)...