The Research Of Optimization For Routing And Localization In Wireless Sensor Network

Compared to traditional networks,WSN has the characteristics of limited energy,communication,computing and storage,so how to achieve energy conservation,longer network lifetime and better network performance under these constrained resources has been the urgent issue need to be solved in WSN.Routing and node localization are two of the key technologies in wireless sensor network(WSN).Studies have shown that 80%of the energy in the whole WSN has been consumed to exchange and transmit the data within the network.Therefore,energy efficient routing protocol is feasible solution.In addition,the sensed data collected by WSN is closely related to the location of sensor nodes.Most of the WSN applications are based on the positions of sensor nodes.Therefore,how to obtain the exact location of sensor nodes is also the focus of current WSN research.Employing optimization techniques to solve the problems in WSN and improve the QOS of WSN is an important branch and research focus of WSN research.Design of routing protocol in WSN has been proved to be NP-hard problem,and node localization also can be transformed into continuous optimization problem.Swarm intelligent(SI)technology is an effective method to solve these NP-hard problems and continuous optimization problems.SI optimization algorithm(SIA)has the characteristics of adaptability,scalability and robustness,which are the requirements of routing design in WSN.Simultaneously,SIAs can achieve outstanding performance in solving the continuous optimization problem.In this paper,according to the characteristics of routing and node localization in WSN,the optimization models for WSN routing and node localization are designed and the corresponding optimization algorithm are presented respectively.The main works of this paper are summarized as followings:(1)An energy-efficient routing protocol for MWSN with multi-mobile Sinks is proposed.The frequent change of the paths between source nodes and the Sinks caused by Sink mobility introduces significant overhead in terms of energy,packet delays and communication.To solve these problems,we present a new routing protocol in which mobile Sinks collect sensed data using agent.At first,we present an efficient routing strategy,which take the new designed neighbor table as the core and adopt the greedy forward forwarding mechanism as the underground route realization,while recovering the interrupted route which caused by Sink movement and node failure.In addition,the strategy adopts unicast flooding method to replace the network-wide flood broadcast to save the energy which consumed to broadcast the node's neighbor table and other information.Based on the new route strategy,we design our energy efficient optimization model for route in MWSN.In the new route optimization model,we discuss detailly the energy consumption of the relay nodes,and try to minimize the transmit distance between two relay nodes and the total route length to save energy in the network while ensure the reliability of data transmission.Especially,we detail the share relay nodes problem in our optimization model.Next,we should select the appropriate optimization algorithm to solve our optimization model.Because the simpleness and high efficiency of PSO,we employ PSO to build the optimal routing tree.However,the original PSO is insufficient to solve discrete routing optimization problems.Therefore,a novel discrete particle swarm optimization for MWSN(GMDPO)is put forward to address this problem.Finally,based on the candidate relay nodes set which collected by each Sink,the optimal route solution is found by solving the our optimization model using our new GMDPSO,and the simulation results demonstrate our new routing protocol has higher network throughput,less power consumption and lower network latency.(2)An energy-efficient routing protocol for cluster-based WSN is proposed.Cluster-based WSN are another important energy-efficient topology.Two optimization models for clustering and routing are designed respectively in the new route protocol.In the new clustering optimization model,energy consumption of the Cluster Head node(CH)and the member node are considered respectively,and the sum of transmission distances of member nodes in the cluster is tried to be minimized to save energy.In addition,the effects of the unClustered nodes on energy consumption and throughout of WSN are also considered in our new clustering optimization model.Here,the unClustered node means the isolated nodes that still keeping remaining energy and doesn't belong to any cluster.None of the existing research on clustering in WSN have balanced the CH,member nodes and unClustered nodes simultaneously to achieve optimal cluster result.In our route optimization model,the total route length and the number of relay nodes are tried to be minimized to saving energy.Additionally,the energy consumption of CH when it acts as relay node is calculated detailly in our route optimization model.For the improvement of optimization algorithm,the implementation of the previously proposed GMDPSO has been improved to present a new discrete PSO for cluster-based WSN(iGMDPSO).Notably,the encode/decode schema of iGMDPSO is slightly different for clustering and routing in WSN.By combining iGMDPSO with our two optimization models,a clustering algorithm named eiGMDPSO-C and a routing algorithm named eiGMDPSO-R are proposed respectively.And these two algorithms constitute our new energy efficient routing protocol(eiGMDPSO-CR),and the simulation results show that our new routing protocol has longer network lifetime,better network connectivity,higher network throughput and less energy consumption.(3)A rang-free static node localization algorithm based on QPSO with Levy flights and Memetic algorithm is proposed.Our new localization algorithm is independent of WSN topology and is suitable for static node location in different WSN(static ordynamic).For the localization optimization model,we adopt the general localization optimization model,in the new optimization model,the low cost and WSN topology-independent DV-Hop is adopted as the underground realization to complete the distance estimation between the unknown node and the anchors.To reduce the impact of the estimated distance error on the accurate of localization result,the reasons which product the errors during the distance estimation in DV-Hop are further analyzed,and the specific improvements are put forward from the point of view of energy saving as followings:(1)During the network initialization,the calculation of hop count is completed together with the network status information collection of route design,which can reduce the communication overhead and energy consumption caused by multi flooding.(2)The original average hop distance of anchor is modified according the estimated distance error among anchors to reduce the impact of average hop distance of anchor on position accurate.(3)The weight value _kw in the localization optimization model is calculated according to the distance between the unknown node and the kth anchor to further reduce the estimated distance error.For the improvement of the optimization algorithm,since our localization optimization model actually is binary quadratic function optimization problem and QPSO has better performance among SIAs in solving such problem.However,QPSO still suffers from its slow convergence and easy to fall into local optimum.To overcome these disadvantages,we reconstruct the original QPSO by combined the advantages of Levy flight method(i.e.,random walks)and memetic algorithm(i.e.,ideal of divide to conquer)and QPSO(i.e.,wonderful search strategy)at the first time to enhance the searching capability and proposed a novel variant of QPSO(called QPSO with Levy flight and memetic algorithm,LMQPSO).Additionally,a novel fast local search rule is designed to accelerate the convergence speed.LMQPSO is used to solve our new localization optimization model to achieve high accurate position result,and the simulation results show that our new localization algorithm outperform the classic ones without SI-based and the other SI-based ones in term of positon accurate.