Optimization Of Cross-layer Design In Wireless Sensor Networks

The wireless sensor network is energy-constrained, where each node is powered by a small battery. Under this hard constraint, reducing energy consumption is the most important design consideration for such networks. We show that a joint optimization of the hardware, link, Multiple Access (MAC), routing designs and cooperative MIMO technique is a beneficial and feasible approach to implement an energy efficient wireless network. Firstly, we start with a point-to-point link, establish a mathematical model of energy consumption, research on the performance improvement of energy consumption and transmission delay with MIMO and cooperative MIMO techniques. Then, we establish a energy consumption model of wireless sensor networks. Secondly, we consider a multiple access scenario, where multiple sensor nodes are sending data to a central node, and analyze the energy consumption, delay and network rate performance of variable-rate system. Lastly, we discuss the energy consumption, delay and network rate performance of the fixed-rate system and variable-rate systems under the decided route conditions. Compared to the route optimization networks, the network rate improved significantly with the same delay and energy constraints.In order to increase the efficiency of wireless spectrum usage of unused spectrum band allocated to primary users in cognitive radio networks, the fourth chapter discusses the capacity of secondary user optimization problem with a combination of the cross-layer design and cooperative spectrum sensing. Firstly, we analyze the local spectrum sensing and cooperative spectrum sensing performance. Then, jointing optimization of physical layer and MAC layer, we establish a new optimization model for the capacity of secondary user under primary user protection and secondary user spectrum utilization condition. Lastly, we discuss the influence of the signal-to-noise ratio, sensing slot, total frame duration and number of cooperative users on the capacity of the local spectrum sensing and cooperative spectrum sensing under these two scenarios. Then comparing the two modes, we discuss the ratio of the interfering capacity with the efficient capacity.