Joint Optimizations Of Energy Strategies And Routing In Wireless Sensor Networks

Wireless sensor networks (WSN) have gained wide attention for the low cost and high coverage in many applications, such as environmental monitoring, disaster surveillance and vehicular tracking. In traditional scenarios, nodes are battery-operated and deployed over a target region for sensing, transmission and processing. It is impossible or prohibitively expensive to replace batteries, especially in remote locations. So it is a main challenge to prolong the network lifetime under limited energy resource. With the development of energy-harvesting technology, nodes can harvest renewable energy from the environment and the network is provided an opportunity of granting unlimited lifetime. In this scenario, the main task is to maximize the network capacity, which means the largest sample rate under energy sustainability. Recently wireless energy transfer and energy cooperation are proposed to realize the energy transfer between nodes, and make the WSN utilize the energy more reasonable.In this dissertation, we will investigate the following three scenarios, which are categorized by the energy supply method. Each scenario has its own energy policy such as battery allocation, energy-harvesting device allocation and energy routing. We respectively propose the joint optimization algorithms on energy policy and routing in responding scenarios, to maximize network lifetime or capacity.For the traditional WSN without energy-harvesting devices, we jointly optimize the routing and battery allocation policy to prolong the network lifetime. The joint optimization problem is modeled with continuous and discrete battery levels respectively. In the continuous case, a linear programming is established, which obtains the optimal routing and battery allocation simultaneously, In the discrete case, a suboptimal but efficient method is proposed with an optimal battery discretization algorithm. The simulation results show our methods can substantially prolong the network lifetime.For the WSN with energy-harvesting devices, we propose an algorithm to jointly optimize routing and energy-harvesting rate for network capacity maximization. By designing the network routing and the size of each node’s harvesting devices, we aim at maximizing the sensors’sample rate under the budget constraint. This optimization problem is modeled as a combination problem. By the step of convex relaxation and discretization, our optimization algorithm avoids exhaustive search, and gets a suboptimal solution efficiently. The simulation results illustrate that our algorithm performs better than others in all network scales.For the WSN with energy-harvesting devices and wireless energy transfer devices, energy cooperation is achieved and nodes can transfer energy wirelessly with each other. We jointly optimization the data route and energy route to maximize the network capacity. We solved the optimization problems with a centralized method and a distributed one separately. In the centralized case, we establish a linear programming to solve these two route policies. In the distributed case, we transform the original problem and make use of the projected subgradient algorithms, and each node can solve the routes by simple computations and communication with neighbors. The simulation results illustrate the benefit of energy cooperation and the convergence of the distributed algorithm.