基于MR-MC链状无线传感器网络的SHM批量异构数据汇集机制研究

Wireless Sensors Network (WSN) is promising in the structural health monitoring (SHM) of civil infrastructures. However, conventional WSN adopting single channel half-duplex communication suffer from higher link conflict, interference and delay, which is not so suitable for the heteregenous intensive data gathering of the long and narrow chain-type sensors network of SHM systems. Moreover, it will cause the mismatch between payload and bandwidth, leading to obvious decreasement of the network throughput. This project will adopt the multi-radio multi-channel (MR-MC) full duplex communication, and exploit the channel spacial reuse to construct a hirarchical data collection tree, which features load balance among the relaying nodes of the same layer and the match between the load and bandwidth of the relaying nodes of different layer; study polynomial time heuristics algorithm to optimize the channel assignment and the time slot scheduling, so as to maximize concurrent transmissions and minimize link conflict, thus improving the thoughtput of data gathering. Furthermore, this project will adopt bioinspired swarm intelligent algorithm to optimize the deployment of sink node and the resource configuration of sensor nodes, to further improve the network performance. The research is hopeful to greatly increase the data collection throughput of the chain-type WSN, and thus promote the application of WSN in the SHM like intensive data convergent fields.

无线传感网络(WSN)在土木基础设施结构健康监测(SHM)领域有广阔的应用前景。常规WSN采用单信道半双工通信方式，存在较大的链路冲突、干扰和时延，难以适应SHM三维长窄链状传感网络的密集异构数据汇集应用需求，易造成中继节点负载与带宽不匹配、链路负载不均衡，导致网络吞吐量急剧下降。本项目拟引入多收发器多信道(MR-MC)全双工通信方式，充分利用长窄链状分布下信道易空间复用的特点，构建层内中继节点负载均衡、层间中继节点负载与有效带宽匹配的分层数据汇集树；根据负载和带宽情况，为链路分配不同优先级，采用多项式时间启发式算法来优化信道分配以最大化并发传输、优化时隙调度以最小化链路冲突，从而提高WSN数据汇集吞吐量；采用仿生群智能算法，优化汇聚节点的部署和传感节点的资源配置，进一步提高网络吞吐量。研究结果有望大幅提高网络吞吐量，为推动WSN在SHM等长链状传感网络的批量异构数据汇集应用奠定理论基础。

无线传感器网络(WSN)具有部署灵活、扩展性强、成本低等优点，在土木基础设施结构健康监测(SHM)领域有广阔的应用前景。针对常规基于单信道半双工通信方式的无线传感器网络因存在较大的链路冲突、干扰和时延而难以满足土木结构健康监测（SHM）三维长窄链状传感网络的密集型数据汇集应用需求的问题，本项目开展了面向SHM的基于多收发器-多信道-多功率（MR-MC-MP）链状无线传感器网络的批量异构数据汇集机制的研究。首先建立了基于精确物理干扰模型的长链状三维异构传感MR-MC-MP网络模型，并且定义了网络能量和网络容量模型，构建了基于最优发射功率等级的并行传输链路集（CTLS），研究了MR-MC通信方式与常规的单收发器单信道（SR-SC）及单收发器-多信道（SR-MC）对密集型异构数据收集的影响。其次，提出了基于激活链路冲突图的最优数据收集树的构建方法，采用贪婪算法最大化汇聚节点的数据接受率，以优化整体网络容量；在此基础上，提出了基于并行传输链路集的链路调度算法，以最小化一轮数据收集所需要的时隙树。然后，以网络吞吐量和能耗为联合适应度函数，提出了基于二值化花粉传播-粒子群混合优化算法（BFPA-PSO）的最优数据汇集方案，实现链路调度与收发器、信道、功率等级配置的联合优化。基于MATLAB的仿真实验结果验证了基于BFPA-PSO的MR-MC-MP数据收集方法的可行性和优越性。最后，研发了基于双收发器多信道的简化版MR-MC无线数据采集系统原型，初步验证了MR-MC通信方式的可行性和优越性。