On computing mobile agent routes for data fusion in distributed sensor networks
On computing mobile agent routes for data fusion in distributed sensor networks
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DOI：
10.1109/tkde.2004.12
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发表时间：
2004-06
影响因子：
8.9
通讯作者：
C. Wu;N. Rao;J. Barhen;S. Iyengar;V. Vaishnavi;H. Qi;K. Chakrabarty
中科院分区：
文献类型：
--
作者：
C. Wu;N. Rao;J. Barhen;S. Iyengar;V. Vaishnavi;H. Qi;K. Chakrabarty
考虑了移动智能体在访问分布式传感器网络节点时对数据进行增量融合的路由计算问题。路径上访问节点的顺序对融合数据的质量和成本有重要影响，进而影响传感器网络的主要目标，如目标分类或跟踪。本文提出了一种简化的分布式传感器网络解析模型，并根据目标函数的最大化问题来表述路由计算问题，该目标函数与接收信号强度成正比，与路径损耗和能量消耗成反比。我们证明了这个问题是np完全的，并提出了一种遗传算法，通过适当地采用两级编码方案和针对目标函数定制的遗传算子来计算近似解。我们给出了具有不同节点大小和传感器分布的网络的仿真结果，证明了我们的算法优于现有的两种启发式方法，即局部最接近优先和全局最接近优先方法。
The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.