Research On The TOF-based Localization Technology In WSN

Wireless sensor networks(WSN) has been gradually applied to the various fields as a newmeans of sensing world and obtaining information, which brought far-reaching influence to thepeople’s production and life. In the application of wireless sensor network, the data acquisitionand information transmission function is achieved through the arrangement of sensor nodes,whose position has important significance on the whole network node monitoring task. Thispaper is aim to reduce the average localization error under the Non Line of Sight(NLOS)environment and improve the localization coverage in the sparse networks based on Time ofFlight(TOF) ranging method, and where the node positioning technology is studied in depth. Themain contents include the following:1. Summarizes some typical wireless sensor network node localization algorithm, analyzestheir positioning performance and the existing advantages and disadvantages, gives the relatedliteratures in recent years for their improvement, and some common location algorithms arecompared by computer simulations. The simulation results show that, the range-basedlocalization algorithm in positioning accuracy is significantly higher than the range-freelocalization algorithm under the small distance measuring error.2. A particle swarm optimization algorithm of reducing the influence of NLOS error is putforward to improve the poor localization performance of the range-based algorithm in the NLOSenvironment. In this algorithm, the particle swarm optimization algorithm is applied to the nodelocalization under the NLOS environment whose parameter setting and the choice of objectivefunction is improved. This paper proposes the improvement scheme of the inertia weight and theTOF ranging results are classified using, NLOS ranging values is used to constitute the unknownnode constraint region, then LOS ranging values is mainly used for the composition of theobjective function, which is weighted by all of the ranging values. The simulation results showthat, the algorithm can improve the node positioning accuracy and the number of iterationsobviously.3. The staged treatment scheme is put forward to improve the poor localization coverage ofthe TOF range-based algorithm in the sparse network. Firstly according to TOF ranging resultsusing multilateration method compute the position of the unknown nodes, whose neighboranchor node reached three in the network. Then the initial values of estimated node locationswhich achieve the accuracy threshold with the iterative refinement method upgraded to anchornodes involve to locating the other unknown nodes. In addition, the misbehavior nodeslocalization solutions are proposed to estimate the positions of some misbehavior nodes. Thesimulation results show that the improved algorithm has obviously better localization coverageon condition of high positioning accuracy.4. Completed the design and realization of the positioning subsystem in valuablessurveillance system. In positioning subsystem, the NanoLOC nodes are used to TOF ranging,and the improved particle swarm optimization algorithm is used to the position estimation of thevaluables. At last, node localization visual interface is designed by the C#programming language. The test results show that, the system can complete positioning, alarm functions, anddisplay the real-time positions of the items intuitively and accurately.