Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each

Self-determination of maximum supportable receiver wakeup intervals in Energy Harvesting WSN nodes

Energy harvesting wireless sensor network nodes would not be able to operate without duty cycling. In TinyOS, duty cycling is supported through Low Power Listening or LPL. LPL is sender-centric: the longer the wakeup interval, the more power a receiver saves, at the cost of more energy per transmission for the sender. Due to the limitations of energy storage technologies, there is a limit to the sender wakeup interval which energy harvesting senders could support. Currently, the limit could be derived computationally or experimentally. Computational derivation is overly conservative, while manual experimentation is labour intensive. In this paper, we present a protocol which enables sensor nodes to determine the wakeup interval limit experimentally without human intervention or the aid of other nodes. Not only does the protocol allow for easier determination of the said limit, it also allows network nodes to adjust to environmental changes that nodes encounter while in deployment, such as capacitor ageing