ADABCAST:太阳能收集无线传感器网络的自适应广播方法

Mustapha Khiati, D. Djenouri
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引用次数: 4

摘要

研究了太阳能收集无线传感器网络(EHWSN)中从基站到传感器节点的消息广播问题。其目的是在不干扰上游通信(从SNs到BS)的情况下确保快速可靠的广播,同时考虑到能量收集的限制。提出了一种自适应方法,其中BS首先选择广播时隙,给定SN的唤醒时间表(SN处于活动状态并处于接收模式的时隙)。因此,社交网站调整他们的时间表。然后迭代寻找广播时隙的最优选择,从而最小化广播开销(传输的消息)和延迟。与现有的解决方案相比,我们的方法实现了快速广播,并且消除了增加协议开销(冗余)的需要。隐马尔可夫模型(HMM)和Baum-Welch学习算法用于此目的。数值结果证实,与最先进的方法相比,我们的方案在更短的时间内执行广播操作,并减少了广播开销。
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ADABCAST: Adaptive broadcast approach for solar Energy Harvesting Wireless Sensor Networks
The problem of message broadcasting from the base station (BS) to sensor nodes (SNs) in solar energy harvesting wireless sensor networks (EHWSN) is considered in this paper. The aim is to ensure fast and reliable broadcasting without interfering with upstream communications (from SNs to BS), whilst taking into account energy harvesting constraints. An adaptive approach is proposed where the BS first selects the broadcast time slots, given a wake-up schedule for the SNs (the time slots where the SN are active and in receiving mode). Hence, the SNs adapt their schedules. This is then iterated seeking optimal selection of the broadcast time slots, so as to minimize broadcast overhead (transmitted messages) and latency. Our approach enables fast broadcast and eliminates the need for adding protocol overhead (redundancy), compared to the existing solutions. Hidden Markov Model (HMM) and Baum-Welch learning algorithm are used for this purpose. Numerical results confirm that our scheme performs the broadcast operation in less time, and by reducing the broadcast overhead, as compared to state-of-the-art approaches.
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