Research On Localization Scheme Based On Error Beacon Filtering In UWSNs

Underwater wireless sensor networks consist of abundant low-cost and self-organizing sensor nodes tied to underwater vehicles,and they are deployed to collaboratively monitor the underwater environment over the interest area.The network can be widely used in many fields,such as environmental monitoring,undersea monitoring,natural disaster prevention,military prevention.In most applications,the collected data by sensors can only be interpreted meaningfully when they are related with location information.Furthermore,node location helps to improve the efficiency of the routing protocol,optimize the network topology and balance the energy consumption of nodes.Therefore,it is necessary to study the localization algorithm in the underwater wireless sensor networks.In the complicated and harsh underwater environment,due to variations of water currents,underwater creature touches,and strong electromagnetic interferences,some beacon nodes tend to move or be damaged,and their coordinates become obsolete or even wrong.Thus,the ordinary nodes obtain a larger localization error under the assistances of these error beacons.Aiming at the localization problem,this thesis proposes two kinds of filtering algorithms for error beacons.An error beacon filtering algorithm based on K-means clustering is proposed for underwater wireless sensor networks firstly.The coordinate of each beacon is calculated through an improved trilateration method with the helps of adjacent beacon nodes,and then the beacon with the maximum localization error is filtered out via the K-means clustering algorithm.The remaining beacons repeat the above processes until the distance error of each beacon does not exceed a preset threshold.In addition,the K-means clustering algorithm has two disadvantages: its classification accuracy depends on the initial values,and the classification results are easy to fall into the local optimum.In order to overcome these drawbacks and differentiate the beacon accurately,this thesis combines the particle swarm optimization algorithm and K-means algorithm,and hence proposes another error beacon filtering algorithm which is based on particle swarm optimization.Finally,the complexity analysis and the simulation tests for our proposed algorithms are presented.The simulation results indicate that the proposed algorithms can detect almost all error beacons accurately,and thus the localization accuracy can be improved.