Research On Sensor Networks Coverage For Water Environment Monitoring

Water environment surveillance network is a wireless network system that contains multiple sensor nodes which will find applications in oceanographic data collection, pollution monitoring, oshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The network coverage is one of the fundamental issues in wireless sensor networks, which can determine the scope of services provided by the network, and have a great influence on the network cost and performance of specific applications. This thesis expounds the overseas and domestic research status, and then we propose optimal coverage algorithms and the localization algorithms in underwater sensor networks (USNs) for environment monitoring. Finally, sufficient simulations are made and experimental results demonstrate the better performance and feasibility of those proposed protocols. The major works and innovative achievements of this thesis are listed as following:1. A coverage optimal algorithm based on fixed movement direction We propose a coverage optimal algorithm based on Virtual Distance (VD) as a sensor deployment strategy to enhance the coverage after initial random placement of sensors. We define the ideal position of node related to its neighbor, and then the distance vector of one node’s real position to ideal position is defined as virtual distance related to the neighbor node. The actual travelled path is determined by the weighted sum of all virtual distance of sensor. Sensor nodes continue to adjust their positions until the coverage rate is tending towards stability. Moreover, we deduce the VD algorithm details on three different application scenarios and investigate the performance of VD algorithm through the simulations. The experimental results show that VD algorithm enables a remarkably improved coverage of the interested area in various environments.2. A coverage optimal algorithm based on sampling for 3D underwater sensor network We discuss the problem of maximizing the sensor field coverage for a specific number of sensors while minimizing the distance travelled by the sensor nodes. We propose a coverage optimal algorithm based on sampling to enhance the coverage of 3D underwater sensor networks. The proposed coverage optimization algorithm is inspired by the simple random sampling in probability theory. The main objective of this study is to lessen computation complexity by dimension reduction, which is composed of two detailed steps. First, the coverage problem in 3D space is converted into a 2D plane for heterogeneous networks via sampling plane in the target 3D space. Second, the optimization in the 2D plane is converted into an optimization in a line segment by using the line sampling method in the sample plane. We establish a quadratic programming mathematical model to optimize the line segment coverage according to the intersection between sensing circles and line segments while minimizing the moving distance of the nodes.The intersection among sensors is decreased to increase the coverage rate, while the effective sensor positions are identified. Simulation results show the effectiveness of the proposed approach.3. A coverage algorithm based on polar coordinates in circle fields We consider the problem of wireless sensors deploying in a circle area to achieve a desired degree of coverage. To this end, we propose a coverage algorithm based on polar coordinate to enhance the coverage after an initial random deployment. First, the coordinates of sensor nodes are switched to polar coordinate according to the relationship between polar coordinate and rectangular coordinate system. Second, the desired positions of neighbor nodes in different directions are defined, thus the virtual radius and the virtual angles of sensor node in the polar coordinates are calculated while the sensor node moving to the desired position along radial direction and circumferential direction respectively. We adjust the nodes polar coordinates via radius and angle in order of the node’s ID as much as possible to reduce the overlapping sensing area of nodes and improve the coverage of Wireless Sensor Networks (WSNs). Meanwhile, the sensor nodes are restricted in the scope of deployment area by limiting the radius of polar coordinates. We also conduct extensive simulations to evaluate the performance of the proposed algorithms.4. Iterative localization algorithm for water environment monitoring We propose an iterative location algorithm based on adaptive grid applying to poor environment in which beacons are hard to deploy or fail to keep working. By putting the beacon nodes on the edge of the unknown nodes’distribution area, the cosines theorem of triangle is used to obtain the unknown nodes’ coordinates. Then, some optimally located nodes are selected as new beacon nodes (called NBNs). According to these NBNs’coordinates and their communication range, next grid’s width or height is calculated. By repeating this iterative process, all the unknown nodes will be located gradually. We improve the localization algorithm to enhance the applicability of the algorithm. Combined the maximum likelihood positioning algorithm with the triangle cosines theorem positioning algorithm, an iterative localization algorithm for the particularity of the water environment is proposed. First, the beacon nodes are placed in the edge of the nodes distribution area according to rules, then marks the nodes as beacon nodes whose location information has been estimated by the cosine theorem. Finally, counting the neighbors of residual unknown nodes, if there are 3 or more beacon nodes among the neighbors, the unknown node’s position will be calculated by the maximum likelihood localization algorithm. By repeating this iterative process, all the unknown nodes will be located gradually.