基于协作传输的群智能无线传感器网节点部署研究

发布时间：2018-04-04 10:57

本文选题：无线传感器网络　切入点：节点部署　出处：《哈尔滨工业大学》2014年博士论文

【摘要】：无线传感器网络（Wireless Sensor Networks，WSN）是目前科研领域的热点研究方向，被广泛应用于各个领域但带来信息传输数量和质量的巨大压力。研究人员提出一种协作传输技术（Cooperative Transmission, CT），利用携带单天线的无线网络节点组建虚拟MIMO（Multiple Input Multiple Output）系统获得空间分集增益，扩大无线网络的覆盖范围以减轻该压力。该理论在通讯、控制等领域得到了广泛关注，但在利用节点数量有限的无线传感器网络完成长距离数据传输或在指定位置进行远距离信息采集等类似的研究较少，且不利于实际应用，没有将协作传输的扩展覆盖范围特性应用到多跳无线传感器网络中。本课题“基于协作传输的群智能无线传感器网节点部署研究”，在对协作传输以及无线传感器网络研究基础上，分析两者结合带来的增益效果，寻找最佳部署方案，提出利用仅携带单天线能量充足的数量固定类似基站的特殊节点，应用协作传输技术组建在直线上可以获得最远传输距离的无线传感器网络，以充分利用有限节点完成数据传输任务。针对不同场合不同需求下的数据传输任务，研究并改进了多种智能优化算法以提高节点部署的计算精度减少计算时间，并提出了相应的节点部署策略。在灾后信息获取、结构健康监测、作战单元信息传递、个域网构建等领域具有重要应用。本文的主要研究工作如下：针对固定节点数目的线形无线传感器网络节点部署问题，提出利用协作传输理论构建自动解码转发（Auto Decode and Forward，ADF）节点部署模型，利用最大比合并（Maximal Ratio Combining，MRC）方法合并多径信号，用解码转发协议对中继信号进行译码转发，以实现协作传输技术在无线传感器网络上应用并获得传输距离的扩展。实验表明，与非协作传输方法DET-CA相比，ADF节点部署模型可以获得更远的传输距离，覆盖距离增大。为了避免出现节点不能译码导致不工作的情况，提出数据共享解码转发（Message SharingDecode and Forward，MS-DF）协作模型，该方法在同一簇内节点进行数据共享，所有无线传感器网络节点全部工作，增大网络的分集增益。实验表明，MS-DF模型有效可行，与ADF协作模型相比，在保证信号传输质量前提下，极大地提高了无线传感器网络的直线传输距离。以5节点为例，比DET-CA传输距离增长5%-54%。针对协作传输MS-DF节点部署模型无法常规求解问题，提出改进的蚁群优化算法来寻找模型最优解。该方法使用离散分段方式改进蚁群算法的启发函数，提出引入丛林法则加大信息素更新量，提出融合贪婪算法到禁忌列表（tabulist）更新原则加快算法收敛速度，逐步获得最优解。实验表明，改进的蚁群方法可以有效收敛，并且获得最优解，适用于要求计算结果误差小，但对计算时间要求不高的环境。仿真实验表明，，7节点时蚁群算法种群数量是10，迭代次数100次时结果误差仅为0.07%，验证了该算法的可行性和有效性，可以应用于优化求解协作传输节点部署模型。针对要求无线传感器网络节点部署计算时间短但对计算结果误差要求不高的部署问题。提出应用萤火虫群优化算法，通过改进萤火虫移动函数和启发因子以适应协作模型求解问题需要，改进决策半径更新函数和步进函数加快算法的收敛速度，避免局部最优以及极值震荡问题，利用算法的多维并发计算优势减少计算时间获得最优解。实验表明，在保证最优值稳定收敛情况下，改进萤火虫群优化算法可以有效地减少计算时间，以13节点为例，萤火虫算法耗时仅是蚁群算法的30%。适合应用于要求计算时间短的场合。针对具有大量节点的无线传感器网络的节点部署问题，提出了基于协作传输技术的等数目节点簇，簇间距相等的节点部署方案。该方案分别基于MS-DF协作模型和满分集增益的协作传输模型，每簇节点数目相同，每簇节点中心间距离相等，两种方法均具有网络结构简单、部署速度快的优点，实验结果表明，可以有效地进行大量节点的快速部署。
[Abstract]:Wireless sensor network (Wireless Sensor Networks, WSN) is currently a hot research direction in the field of scientific research, is widely used in various fields but the huge pressure on the quantity and quality of information transmission. The researchers propose a cooperative transmission technology (Cooperative Transmission CT), using MIMO to build virtual wireless network node with single antenna (Multiple Input Multiple Output) system to obtain spatial diversity gain, expanding the coverage of the wireless network in order to alleviate the pressure. The theory in communication, control and other fields has been widely concerned, but in the use of a limited number of nodes complete the long-distance data transmission of remote information collection or less similar to the location specified in the wireless sensor network, and is not conducive to the practical application, there will be extended cooperative transmission coverage characteristics applied to multi hop wireless sensor networks.
The research of "cooperative transmission group of intelligent wireless sensor networks deployment research based on the cooperative transmission and wireless sensor networks on the basis of analysis of both gain the effect brought by the search for the best plan, put forward by carrying only a fixed number of similar special node base station single antenna energy sufficient, application of cooperative transmission technology set up in a straight line can be obtained in wireless sensor network far transmission distance, to make full use of the limited node data transmission. The data transmission task of different needs of different occasions, studied and improved several intelligent optimization algorithm to improve the calculation accuracy of the node deployment to reduce the computing time, and put forward the corresponding node deployment strategy. In the post disaster information acquisition, structural health monitoring, combat unit information transmission, network construction and other fields has important application in this paper. The main research work is as follows:
The linear wireless sensor network node deployment problem for a fixed number of nodes is proposed using automatic decode and forward cooperative transmission theory (Auto Decode and Forward, ADF) node deployment model, using the maximum ratio combining (Maximal Ratio, Combining, MRC) method combined with multipath signal, decode and forward relay protocol for signal decode and forward, to achieve cooperation transmission technology in wireless sensor network applications and extend the transmission distance. Experimental results show that compared with the non cooperative transmission method DET-CA, ADF node deployment model can obtain the transmission distance more far, covering the distance increased. In order to avoid decoding node cannot causes on working conditions, puts forward the data sharing Message SharingDecode and Forward (decode and forward MS-DF), cooperation model, the method of nodes in the same cluster data sharing, all wireless sensor network node All the work, increase the diversity gain of the network. Experimental results show that the MS-DF model is feasible and effective, compared with the ADF cooperation model, in the premise of ensuring the quality of signal transmission, greatly improves the transmission distance of the wireless sensor network with 5 nodes. For example, the transmission distance than the DET-CA growth of 5%-54%.
MS-DF for cooperative transmission node deployment model cannot solve the problem of the conventional and proposed optimization to find the optimal solution of the improved ant colony algorithm. The method uses heuristic function discrete mode improved ant colony algorithm, proposed the introduction of the law of the jungle to increase the amount of pheromone, proposed fusion greedy algorithm to update the tabu list (tabulist) principle to speed up the convergence of the algorithm and gradually get the optimal solution. Experimental results show that the improved ant colony approach can effectively convergence, and obtain the optimal solution, suitable for the calculation error is small, but not high on the computational time requirements of the environment. Simulation results show that the 7 node number of ant colony algorithm population is 10, the number of iterations is 100 times the error is only 0.07% and verify the feasibility and effectiveness of the algorithm can be applied to the optimization of cooperative transmission node deployment model.
According to the requirement of wireless sensor network node deployment short calculation time but the calculation error requirements of the deployment problem. Proposed glowworm swarm optimization algorithm, by improving the firefly mobile function and heuristic factor in order to meet the need of cooperation model to solve the problem, an improved decision radius update function and step function to accelerate the convergence speed and avoid local optimal and extreme vibration problems, using multidimensional concurrent computational advantages reduce the computation time of optimal solution is obtained. Experimental results show that the optimal value in ensuring stable convergence conditions, improved glowworm swarm optimization algorithm can effectively reduce the computation time, with 13 nodes as an example, the firefly algorithm is time-consuming only ant colony algorithm suitable for 30%. for short computation time occasions.
Node deployment problem in wireless sensor networks with a large number of nodes, this paper proposes a cooperative transmission technology such as the number of cluster nodes based on node deployment scheme for cluster equal spacing. The scheme based on cooperative transmission MS-DF cooperation model and full diversity gain model, the same number of clusters per day, each cluster node is equal to the distance between the center. The two methods have simple structure, fast deployment, the experimental results show that the rapid deployment can be effectively carried out a large number of nodes.

【学位授予单位】：哈尔滨工业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5

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