Maximizing The Lifetime Of Wireless Sensor Network

There are many challenges in implementation of wireless sensor network systems such as clustering and grouping. The grouping problem of nodes is computational process intended to partition the nodes of network into groups such that each group contains a number of nodes, in the same time the node can be an element of multiple groups. In the first chapter of this work, we provided a Sensors Grouping Hierarchy Structure (GHS) to divide the nodes in wireless sensor network into groups to assist the collaborative, dynamic, distributed computing and communication of the system. Our idea is to partition the nodes according to their geographical maximum covered regions such that each group contains a number of nodes and a number of leaders. To evaluate the performance of our proposed grouping structure, we have implemented a grouped based routing model. The proposed grouping structure shows a good performance in energy consumption and energy dissipation during data routing as well as itTree Routing (TR) is a simplified routing algorithm suggested for simple low-cost and low-power wireless sensor networks. Unlike the node in Flooding/Gossiping algorithms, the node in TR transmits the data packets either upwards to its parent node or downwards to one of its children. The major drawback of TR is the energy balancing as compared with more sophisticated energy aware routing protocols. To prolong and maximize the network lifetime, the final objective purpose should be let each sensor in the same level of tree consume their energy at similar rate. However, practically this could be difficult due to the fact that the close nodes to BS will die out first and the leaves nodes will die out last. In the second chapter of this work, we presented Balanced Tree-Based Energy Aware Routing (BTEAR), which is designed to achieve a potential balancing of energy consumption per level in routing tree such that each node in the same level has approximately the same number of children. The simulation results expose that BTEAR not only outperforms TR in terms of network lifespan, but also it is more energy-efficient than TR.The aim of coverage problem is to ensure a minimum number of nodes (at least one sensor) with little redundant data cover every point inside the interest area. In the third chapter of this work, we provided an algorithm for WSN coverage based on Zigzag Pattern. The interest area divided into multiple Zigzag Patterns with multiple corners and lines segments, each node is deployed in a corner of Zigzag Pattern. Zigzag Pattern Scheme Deployment Algorithm expresses a very high coverage efficiency 91%, as well as, it expands and covers the whole interest area with minimum number of nodes, while it generates a very little coverage redundancy. We provided geometrical analysis to illustrate when the algorithm reaches the maximal and optimal coverage efficiency. The algorithm reaches the maximal and optimal coverage efficiency when the circumference of sensing range for each node is equal to the sum of its vertical Arcs length and horizontal Arcs length, while the optimal length of each line segment in Zigzag Pattern is ≈ (?) r and the optimal angle of each corner is 60°.The fourth chapter of this work explores Topology Group-based Coordinated Routing (TGCR) in wireless sensor networks and compares the energy consumption in the network over time for different coverage schemes in wireless sensor network. Fully charged battery powered sensors are systemically deployed in the area according to different coverage schemes. TGCR is started by partition the network into groups according to the maximum covered regions. Each group contains a fixed number of sensors. The sensor in network can be a member of multiple groups. When there is a traffic to send, the sensor forwards the data packet to the leader of group (node to leader forwarding), and then the leader of group forwards the packet to the nearest adjacent group (leader-to-leader forwarding).The fifth chapter of this work addresses the problems of coverage of WSN by proposing two grid-based algorithms:Grid Square Coverage version (1) and Grid Square Coverage version (2). Moreover, we have analyzed the performance of both algorithms and provided a compression between them. The results present that the Grid Square Coverage (1) algorithms has 78%coverage efficiency while the Grid Square Coverage (2) has 73%.The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor and track a target object or event. In this work, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). In the chapter 6 of this work, we present a tracking algorithm called Grouping based Location Tracking (GLT) that scales well with the number of none-mobile nodes and the number of tracked mobile objects. GLT is based on the Grouping Hierarchy Structure GHS. In GHS structure the nodes are partitioned into groups (not clusters) according to their Geographical Maximum Covered Regions (GMCR) in the network such that each group contains a number of nodes and a number of leaders. The GLT algorithm ensures high performance in avoiding data redundancy and energy balancing between the nodes by the support of HST (Hierarchal Spanning Tree) and NT (Notification Tree). The data routing of GLT is based on HST while the avoiding data redundancy mechanism and energy balancing strategy are based on NT tree. Both of NT and HST are working together to achieve high deliver rate and little duplicated packets. The simulations results shown that the lifetime of network using HST and NT is longer than the lifetime using TR, BTEAR and TGCR.