The Research Of Localization Algoirthm For Underwater Wireless Sensor Networks Based On Mobility Model

Today the ocean has played a more and more important effect on national economy,so how to develop and make use of the ocean becomes the theme for the futureeconomic development. Underwater wireless sensor network can collect information ofthe ocean environmental, monitor and supervision of marine environmental pollution,explore oceanic resources, warn the disaster timely and now it has been widely used inmilitary. Therefore underwater wireless sensor networks have aroused more and moreattention. Since many applications of underwater wireless sensor networks depend onthe location information of sensor nodes, so raw sensor data without its positioninformation has less capability, so how to locate the underwater nodes accurately isinvestigated keystone today. During the process of node localization, the nodes arefaced with influences and challenges of underwater conditions and underwateracoustics communication.Now during the process of node localization, many underwater sensor networklocalization algorithms assume nodes are static. But these underwater sensor nodes areaffected by ocean currents and tides, and they may move, the distance between thenodes may have changed when calculate node coordinates. So this may lead to thelocation information of these nodes are useless. The nodes can move with the mobilemodel in scalable localization with mobility prediction for underwater sensor networkslocalization algorithm, it has attracted the research interest of many scholars.The paper carried on intensive research to the location principle of SLMPlocalization algorithm. This paper makes study from node density, confidence threshold,prediction window, the length of prediction step and prediction error threshold fiveaspects and research the five aspects how to influence on the localization coverage andlocation error. SLMP algorithm uses maximum likelihood estimation method tocalculate the unknown node coordinates in unknown node positioning stage, but itexists system error when we use maximum likelihood estimation method to calculatethe unknown node coordinates, it will degrade the positioning accuracy. Therefore, forthe purpose of reducing the node location error, increasing the algorithm locationaccuracy and improving the SLMP localization algorithm, the paper put forward a genetic optimization SLMP localization algorithm (GA-SLMP).Based on SLMP localization algorithm, GA-SLMP localization algorithm hadmade improved in the unknown node positioning stage. First unknown node calculatedits coordinate by using the maximum likelihood estimation method. Then expand theunknown node coordinates in the three-dimensional space and form the search space ofthe unknown node coordinates. Finally genetic algorithm is used to find the unknownnodes coordinate in this expanded three-dimensional space through iterative search.First, calculated the fitness function value of individuals in each iteration. And thenchose individual to heredity to the next generation according to the fitness functionvalue of individuals, each new generation genetic operation included selectioncrossover, and mutation. When reaching the maximum number of iteration thealgorithm stopped genetic operation and output the optimal solution.Using MATLAB2013a to compare the simulation results of GA-SLMPlocalization algorithm and SLMP localization algorithm in the aspects of node density,confidence threshold, prediction window, the length of prediction step and predictionerror threshold, the simulation results show that GA-SLMP localization algorithm hasbetter location accuracy than SLMP localization algorithm in different node density,confidence threshold, prediction window, the length of prediction step and predictionerror threshold.