Centre for Communication Systems Research (CCSR), University of Surrey, Guildford GU2 7XH, UK.
Sensors (Basel). 2013 Oct 16;13(10):13998-4028. doi: 10.3390/s131013998.
This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.
本文提出了一种面向任务分配的框架，以实现低节点移动性（例如行人速度）的动态多跳多媒体无线传感器网络中的高效网络内处理和具有成本效益的多跳资源共享。所提出的系统结合了快速任务重新分配算法，以便快速从可能的网络服务中断（例如节点或链路故障）中恢复。基于遗传算法的进化自学习机制不断调整系统参数，以满足所需的应用延迟要求，同时实现足够长的网络寿命。由于算法运行时在更新任务分配时会产生相当大的延迟，因此我们引入了自适应窗口大小来限制延迟周期，并确保基于节点移动模式和设备处理能力的最新解决方案。据我们所知，这是首次在动态条件下的移动多跳无线环境中进行多目标任务分配的研究。在各种设置下进行了模拟，结果表明与启发式机制相比，在延长网络寿命方面有了相当大的性能提升。此外，所提出的框架还显著减少了错过应用截止日期的频率。
