Research On Swarm Intelligent Wireless Sensor Network Nodes Deployment Based On Cooperative Transmission

Wireless Sensor Networks (WSN) is a hot research pot in science and technologywhich has been applied in different fields, and caused great pressure to dataacquasion and transmission. In order to reduce the pressure, reserachers proposedcooperative transmission (CT) technology, which is virtual distributed multiple-input-multiple-output (MIMO) system in the diversity configuration with only oneantenna in every node to expand the coverage area. It is widely concered in thecommunication and control area. There is not enough research on the long distancedata transmission by limit number of nodes or data acquasion where is far awayfrom the source node, and which is not suitable to practical application. There is noapplication by using the range expansion of CT to WSN.In this dissertation of “Research on swarm intelligent wireless sensor networknodes deployment based on cooperative transmission”, the cooperative transmissiontheory and WSN is researched and analyzed to see the gain by combining the twotheories, to find the optimal deployment method. This dissertation proposed to usethe fixed number of mobile wireless sensor network nodes which has only oneantenna in every node and enough energy like a base station to get the maximumrange expansion along an appointed line by combining the CT technology. Everynode in the network would work very well without waste. Aiming at problem toapply this model to different environments, this dissertation compared and improveddifferent optimization algorithms to reduce time cost and improve the precision, andgive some rules for nodes deployment. There are some applications for thisstraight-line deployment such as disaster area data acquisition, structure healthmonitoring, communication between combat units, and personal area network, et al.The main research contents of this dissertation is as follows:Aiming at the problem that build WSN nodes deployment model with fixednumber of nodes based on CT technology. This dissertation proposed ADF (AutoDecode and Forward) model by using the MRC (Maximal Ratio Combining) methodto combine the copies in each orthogonal channel and use decode and forwardprotocol to receive and retransmit the signals, and to realize the maximum rangeexpansion of WSN. The simulation results show that the ADF model is useful andeffective to get longer end-to-end distance. In order to avoid no decode and not workof any node, this dissertation proposed the MS-DF (Message Sharing Decode andForward) model. This novel model supposed the message sharing of all the nodes inthe same cluster, and then forward to the next hop which could avoid the waste ofnodes in the network and improve the gain of the network. Simulation results show that comparing between MS-DF and ADF models, the MS-DF model great improvesthe range expansion under the same constraint of transmission quality requirement.Take5nodes as an example, the transmission distance of MS-DF is longer thanDET-DA5%to54%.Aiming at the problem that there is no solution of the MS-DF model by traditionalmethod. This dissertation proposed the improved ant colony algorithm (IACA) toget the optimal result of the CT models. IACA improved the heuristic function bydiscreting segements of the distance; proposed novel pheromone update rule basedon the law of jungle, proposed the novel tabu list combined the greedy algorithm, toget the optimal results systematically. Simulation results show that the IACA isconvergent, useful and effective, which is suitable for the application requirement ofhigh precision but do not care about time cost. Take7nodes as an example, whilethe ant number is10, the iteration times is100, the inaccuracy is only0.07%, whichprove the IACA is useful to get the optimal result of CT model.Aiming at application requirement of short time cost but do not care much aboutprecision. This dissertation proposed the improved glowworm swarm optimization(IGSO) algorithm to solve this problem, which improved the glowworms’ movementprobability function and heuristic factor to adapt to the CT model, improved thelocal-decision range and movement direction function to increase the algorithmconvergent speed and avoid the extremum value shock. By using the advantage ofparallel computing to reduce the calculation time cost. Simulation results show thatIGSO algorithm is useful and effective to reduce the cost time under the constraintof the algorithm is convergent to the optimal result. Take13nodes as an example,the IGSO cost only30%of IACA to solve the model, which is more suitable to theapplication of short time cost.Aiming at the problem that build the deployment model of big number of WSNnodes based on CT technology, this dissertation proposed two deployment methodsbased on the same number of nodes in every cluster and same distance between twoneighbor clusters. This dissertation proposed the two methods based on the MS-DFmodel and full diversity gain theory, separately. The simulation results show that thetwo methods are both useful and effective, with the advantage of simple topology,which could deploy a big number of nodes quickly.