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Energy Efficient COGnitive-MAC for Sensor Networks Under WLAN Co-existence

机译:WLAN共存下用于传感器网络的节能COGnitive-MAC

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摘要

Energy efficiency has been the driving force behind the design of communication protocols for battery-constrained wireless sensor networks (WSNs). The energy efficiency and the performance of the proposed protocol stacks, however, degrade dramatically in case the low-powered WSNS are subject to interference from high-power wireless systems such as WLANs. In this paper we propose COG-MAC, a novel cognitive medium access control scheme (MAC) for IEEE 802.15.4-compliant WSNS that minimizes the energy cost for multihop communications, by deriving energy-optimal packet lengths and single-hop transmission distances based on the experienced interference from IEEE 802.11 WLANs. We evaluate COG-MAC by deriving a detailed analytic model for its performance and by comparing it with previous access control schemes. Numerical and simulation results show that a significant decrease in packet transmission energy cost, up to 66%, can be achieved in a wide range of scenarios, particularly under severe WLAN interference. COG-MAC is, also, lightweight and shows high robustness against WLAN model estimation errors and is, therefore, an effective, implementable solution to reduce the WSN performance impairment when coexisting with WLANs.
机译:能源效率一直是电池受限的无线传感器网络(WSN)通信协议设计背后的驱动力。但是,如果低功率WSNS受到来自诸如WLAN等高功率无线系统的干扰,则提议的协议栈的能源效率和性能会大大降低。在本文中,我们提出了COG-MAC,这是一种适用于IEEE 802.15.4的WSNS的新型认知媒体访问控制方案(MAC),它通过得出能量最佳的数据包长度和单跳传输距离,从而将多跳通信的能量成本降至最低关于来自IEEE 802.11 WLAN的干扰。我们通过导出详细的性能分析模型并将其与以前的访问控制方案进行比较来评估COG-MAC。数值和仿真结果表明,在各种情况下,尤其是在严重的WLAN干扰下,可以显着降低数据包传输能量成本,最高可降低66%。 COG-MAC也是轻量级的,并且对WLAN模型估计错误表现出很高的鲁棒性,因此,当与WLAN共存时,COG-MAC是减少WSN性能损害的有效且可实施的解决方案。

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