On the lifetime analysis of always-on wireless sensor network applications

Majority of papers in the area of wireless sensor networks (WSNs) have an element of energy-efficiency and associated with it an analysis of network lifetime. Yet, there is no agreement on how to analyze the lifetime of a WSN. As a result, errors are frequently made on both sides. Some underestimate the network lifetime by an order of magnitude, while others end up overestimating the lifetime by a significant factor. This paper presents a first step towards standardizing the lifetime analysis of WSNs. We focus on WSNs deployed for always-on applications, where the problem of power management is most severe because the environment needs to be monitored continuously. Underestimation of network lifetime is common when proposing sleep-wakeup schemes, where it is frequently assumed that in the absence of a sleep-wakeup scheme, a sensor node from the Mica family lasts 3-5 days on a pair of AA batteries. We show that the same sensor node can be made to last more than 36 days, even if it is continuously monitoring the environment. Overestimation typically occurs when proposing non-sleep-wake up power management schemes such as in-network data aggregation. Overestimation occurs because several network activities (e.g periodic routing messages) are assumed to have negligible effect on the network lifetime and therefore are ignored in the lifetime analysis. We use our recent experience in deploying ExScal (a large-scale WSN for intrusion detection) to identify major components in the network lifetime analysis. We then present a careful lifetime analysis of ExScal and show how to analyze the effects of using various non-sleep-wake up power management schemes such as hierarchical sensing, low-power listening, and in-network data aggregation on the network lifetime. Our lifetime analysis will be useful as a template in analyzing the lifetime of other WSNs deployed for always-on applications