| Wireless Sensor Networks (WSN), usually battery-powered, thus extremely energy-constrained, present energy efficiency as the main challenge. To improve energy efficiency, one strategy is to minimize the total energy consumption of the whole network; the other one is to balance the energy consumption among nodes. In this dissertation, problem of coverage control and routing in WSN is studied, and four algorithms to improve energy efficiency are proposed. Main contributions of this dissertation are listed as follows:(1) To improve scalability and node's coverage efficiency, we propose to build a quasi-grid network from randomly distributed sensor nodes by constructing a virtual global grid structure to control the activation process. In the algorithm, a distributed scheme is presented to determine the position of closest vertex for each node, and a neighboring repulsion technique scheme is designed to guide the supplementary activation process. To balance energy consumption, an original point initiated grid shift scheme is also designed. In addition, to tolerant some degree of irregularity, a method to calculate the grid side length is proposed. Simulation results show that the proposed algorithm can reduce the number of active nodes and prolong the coverage lifetime of WSN.(2) To overcome the well-known hot spot problem and simplify routing construction in large area, a new ring based multi-hop clustering routing algorithm is proposed. The whole area is divided into concentric rings and nodes in each ring are arranged into a number of clusters. To overcome energy hot spot problem, optimal number of clusters is derived through balancing energy consumption of cluster heads in all rings. In addition, to confine the distribution of cluster heads in each ring, a new cluster head competition scheme based on sector and grid division is designed. Simulation results show that the proposed algorithm can balance energy consumption among nodes and prolong the network lifetime.(3) The problem of energy efficient data collection is studied, and a new adaptive data collection algorithm based on Bayesian Compressive Sensing (BCS) is proposed. The problem of building a sparse projection vector containing routing structure is proved to be NP-hard. To find a suboptimal solution, it is divided into three sub-problems:target nodes selection, routing construction, and projection coefficients determination. For target nodes selection problem, two metrics are proposed:principal components of the eigenvector with largest eigenvalue, and nodes with the least total coefficients energy. For routing construction problem, a formal description of the problem is formulated, and it is converted into a problem of building double node disjoint multicast trees. Finally, projection coefficients are obtained by maximizing the reduction of differential entropy. Simulation results show that the proposed algorithm can reduce computation complexity, and improve reconstruction quality.(4) Routing problem in a special kind of WSN -- Delay Tolerant WSN (DTWSN) is studied. Firstly, due to extremely energy limitation, we declare that the optimization object for routing in DTWSN should be to minimize the transmission cost, under the constraints of QoS requirements. Secondly, to save energy consumption, based on the progress status of a bundle, an adaptive routing algorithm is proposed, which selects duplication strategy and forwarding strategy dynamically. In addition, mechanisms for queue management and transmission scheduling are designed. Simulation results show that the proposed algorithm can reduce the transmission cost and keep an acceptable delivery ratio, at the cost of increasing in transmission delay.
|
PDF Full Text Request