Pollution Source Localization Problems In Wireless Sensor Networks

The reasearch of pollution monitoring and pollution source localization has importantsignificance to environmental protection. The sensor networks is based on collaborate effort of alarge number of low-cost nodes, which are densely deployed, and can monitor large-scale area.The deployment is not restricted by geographical environment. Because of these advantages,wireless sensor network has been widely used in pollution monitoring and pollution sourcelocalization, making up the shortcomings of the traditional methods, such as remote sensing,mobile robot detection and artificial detection. This disertation study some problems, includingthe coverage problem in the multiple pollution objects monitoring applications, the waterpollution source localization problems under different terrain conditions and the mobile pollutionsource tracking problem. The main research contents of this paper are as follows:1. The multi-coverage problem in the multi-objects source monitoring applications isproposed and solved. In sensor networks with random distributed nodes, an average subnetlifetime model is proposed. Given the constraints of cost budget and area coverage of differentobjects, a multi-objective multi-coverage algorithm based on integer vector programming isproposed. The first step is to compute the number of each type of sensors used to monitor onesubobject, and the second step is to determine the number of different kinds of heterogeneousnodes based on the average subnet life model. To solve the proposed vector programming issues,two suboptimal methods are given. In the simulation experiments, different suboptimal methodsare compared, and the computational complexity of the proposed algorithm is analysed.Simulation examples verify the effectiveness of the proposed algorithm.2. The SL-n (Samples for Localization-n nodes) algorithm is proposed to obtain robustposition estimation. In the algorithm, all samples using partial least square from everycombination of n observing nodes were obtained first, then the location of the source wereestimated based on the samples. To solve the localization problem of the diffusion source whichis in static water without boundaries, the SL-n algorithm is applied. In the simulation part, thelocalization errors varying with the number of observing nodes and different observation errorswere compared between least squares, SL-n and least absolute localization methods. Thesimulation results prove the advantages of the SL-n algorithm.3. Offshore plume source diffusion in static water is analysed and a piecewise concentrationmodel is put forward. Combining with different application requirements, nonlinear least squaresmethod and Unscented Kalman Filter(UKF) method were applied to the localization problemmodeling of the pollution source which nears the impervious boundary in static water. Threedifferent algorithms called the general localization model algorithm, the approximate functionalgorithm and the algorithm based on (UKF) are proposed. In the simulation part, thehydrological simulation of source diffusion exhibits the diffusion processes. Different parametersestimation algorithms are compared based on the MODFLOW simulation data. The results illustrate that, through the general localization model algorithm, source parameters can beacquired promptly, the approximate function algorithm is more robust in parameters estimatingcompared to the general localization model algorithm，and the algorithm based on UKF balancesthe computation complexity and the estimation accuracy well.4. The diffusion process of mobile diffusion source is analyzed, and a discretizationconcentration model is proposed. The continuous line diffusion source trajectory estimationproblem is transformed into the suboptimal problem which is tracking the moving diffusion pointsource. A mobile diffusion source tracking algorithm based on the discretization concentrationmodel has been proposed. In the algorithm, a constrained least square method is adopted toestimate the related parameters including initial positions and arrival times first. And then, theSage-Husa kalman filter method is used to obtain the optimal estimation of the target positions inreal time. The algorithm overcomes the shortcoming that the general tracking methods based ondynamic sequence can not be applied to the discrete mobile diffusion source tracking directly.The simulation experiments are carried out in two scenarios, in one of which the target movesalong a smooth curve with a constant rate, and the other a non-smooth curve with varyingvelocity. In the simulations, the tracking effects varying with sampling density and nodes densityare also studied. The results illustrate the effectiveness of the proposed algorithm.