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。

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.