基于同步分组传输的低功耗无线协议建模研究

Marco Zimmerling, F. Ferrari, L. Mottola, L. Thiele
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引用次数: 36

摘要

数学模型在理解和设计先进的低功耗无线系统中起着关键作用。然而,传统的多跳低功耗无线协议的分布式和不协调的运行方式给其精确建模带来了很大的困难。这主要是因为这些协议建立和维护大量的网络状态,以应对低功耗无线链路的动态变化。最近的协议通过利用同步传输(ST)偏离了这种设计,即多个节点同时向同一接收器传输,而不是对基于链路的传输(LT)。ST在一定程度上提高了单跳报文的可靠性,使得网络状态少的高效多跳协议成为可能。本文研究ST是否也能对这些协议进行简单而准确的建模。我们为此目的的贡献是双重的。首先,我们通过在139个节点的试验台上的实验表明,将数据包接收和丢失表征为独立和同分布(i.i.d)的序列。伯努利试验——协议建模中的一个常见假设,但对于lt来说通常是不合法的——在很大程度上对st有效。然后,我们通过推导(i)端到端数据包可靠性概率保证的充分条件,以及(ii)估计长期能耗的马尔可夫模型,展示了这一发现如何简化了最近基于st的协议的建模。使用试验台实验的验证证实,我们的简单模型也非常准确,例如,模型能量与实际测量值的误差为0.25%,这是相关文献中从未报道过的数字。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Modeling Low-Power Wireless Protocols Based on Synchronous Packet Transmissions
Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pair wise link-based transmissions (LT). ST improve the one-hop packet reliability to an extent that efficient multi-hop protocols with little network state are feasible. This paper studies whether ST also enable simple yet accurate modeling of these protocols. Our contribution to this end is two-fold. First, we show, through experiments on a 139-node test bed, that characterizing packet receptions and losses as a sequence of independent and identically distributed (i.i.d.) Bernoulli trials-a common assumption in protocol modeling but often illegitimate for LT-is largely valid for ST. We then show how this finding simplifies the modeling of a recent ST-based protocol, by deriving (i) sufficient conditions for probabilistic guarantees on the end-to-end packet reliability, and (ii) a Markovian model to estimate the long-term energy consumption. Validation using test bed experiments confirms that our simple models are also highly accurate, for example, the model error in energy against real measurements is 0.25%, a figure never reported before in the related literature.
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