Recently, wireless sensor networks have attracted a lot of attention. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with base station nodes through wireless links. In this paper, we survey a fundamental problem in wireless sensor networks, the energy consumption problem, which reflects how well a sensor field is deployed. Therefore, a critical aspect of applications with wireless sensor networks is network lifetime. Furthermore, one of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. We formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. In this paper, we propose an energy-efficient method based on Genetic Algorithms to deal with the deployment problem of wireless sensor networks such that it provides target-location and surveillance services.

Wireless sensor networks consist of hundreds to thousands of low-power multifunctioning sensor nodes, operating in an unattended environment, with limited computational and sensing capabilities. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is essential that the network be energy efficient in order to maximize the life span of the network. Hierarchical routing is an efficient way to lower energy consumption within a cluster, performing data aggregation and fusion.Within a clustering organization, intra-cluster communication can be single hop or multihop, as well as inter-cluster communication. Multihop communication between a data source and a data sink is usually more energy efficient than direct transmission because of the characteristics of wireless channel. However, the hot-spots problem arises when using the multihop forwarding model in inter-cluster communication. Because the cluster heads closer to the data sink are burdened with heavy relay traffic, they will die much faster than the other cluster heads. This paper presents an cluster-based routing protocol named An Adjustable Load Balancing Cluster-based Routing for Wireless Sensor Networks(ALBAC).The aim of the work is to let the cluster size be small nearby base station because cluster heads closer to the base station need relay more data.We wnat to let every cluster heads consume same energy. Simulation results show that our unequal clustering mechanism clearly improves the network lifetime over LEACH and BCDCP.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 74-80). Also available in print.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 124-128).

Thesis (M.S.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 62-64).

Thesis (M.S. in computer science)--Washington State University, December 2009. / Title from PDF title page (viewed on Jan. 20, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 60-64).

Dissertation (Ph.D.)--Auburn University, 2009. / Abstract. Vita. Includes bibliographic references (p.131-139).

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Feb. 19, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Wireless sensor network localization opens the door to many location based applications. In this thesis, some solutions obtained from localization algorithms are investigated. There are two categories of problem on localization. Range-based methods are applied to the situation in which information on the distances between each pair of nodes is available. Algorithms are developed to estimate the location of each sensor in the network. Usually, the distance between each pair of nodes is estimated by the signal strength received between them, and this information is very noisy. Range-free methods, which are also called connectivity-based methods, assume that the distances between any two nodes are unknown but the connectivity information between them is known. If the distance between any two nodes in the network is within a communication range, connectivity between these two nodes is said to be established. In a range-based scenario, with the information of inter-sensor distance measurements as well as the absolute locations of the anchors, the objective is to obtain the location of all the unknown nodes. Two new localization methods based on gradient descent are shown in the thesis. The gradient descent methods would minimize the difference between the measured distances and the distances obtained from the estimated locations. From a comparison with other well-known localization methods, the two newly developed gradient descent algorithms can reach better accuracy at the expense of computational complexity. This is not surprising as the proposed algorithms are iterative in nature. For range-free scenario, a new model utilizing all the information derived from connectivity-based sensor network localization is introduced. Unlike other algorithms which only utilize the information on connections, this model makes use of both information on connections and disconnections between any pair of nodes. The connectivity information between any pair of nodes is modeled as convex and non-convex constraints. The localization problem is solved by an optimization algorithm to obtain a solution that would satisfy all the constraints established in the problem. The simulation has shown that better accuracy is obtained when compared with algorithms developed by other researchers. Another solution for the range-free scenario is obtained with the use of a two-objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES). In an evolutionary algorithm, the aim is to search for a solution that would satisfy all the convex and non-convex constraints of the problem. The number of wrong connections and the summation of corresponding distances are set as the two objectives. A starting point on the location of the unknown nodes is obtained using a solution from the result of all convex constraints. The final solution can reach the most suitable configuration of the unknown nodes as all the information on the constraints (convex and non-convex) related to connectivity have been used. From the simulation results, a relationship between the communication range and accuracy is obtained. In this thesis, another evolutionary algorithm has been examined to obtain a solution for our problem. The solution is based on a modified differential evolution algorithm with heuristic procedures peculiar to our domain of application. The characteristics of the sensor network localization are thoroughly investigated and utilized to produce corresponding treatment to search for the reasonable node locations. The modified differential evolution algorithm uses a new crossover step that is based on the characteristics of the problem. With the combination of some heuristics, the solution search can move the node to jump out of local minimums more easily, and give better accuracy than current algorithms. In the last part of the thesis, a novel two-level range connectivity-based sensor network localization problem is proposed, which would enrich the connectivity information. In this new problem, the information of the connectivity between any pair of nodes is either strong, weak or zero. Again, a two-objective evolutionary algorithm is used to search for a solution that would satisfy all the convex and non-convex constraints of the problem. Based on simulations on a range of situations, a suitable range value for the second range is found. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy