中文摘要：

提出一种理论优化路由树的启发式算法，实现地理信息辅助的传感器网络服务质量数据收集架构。算法采用群智能蚁群优化机理进行设计：首先通过构造基于流量的能量有效性权将网络划分为不同的功能区域，使得路由的选择过程能够低延时地自适应网内不均衡性的能耗状况；然后，设计了新颖的启发式因子和信息素更新规则，赋予人工蚂蚁代理感知网络局域能量状况和逼近理论优化树的能力，从而提高路由构建的自适应性和能量有效性。仿真实验结果表明，本文提出的路由机制能够在数据收集的应用背景下，有效提高收集质量和降低传输时延，并在健壮性和节能效果方面优于许多经典的传感器网络路由算法。

英文摘要：

A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.