As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments.In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment.We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.

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Abstract1. Behavioural events that are important for understanding sociobiology and movement ecology are often rare, transient and localised, but can occur at spatially distant sites e.g. territorial incursions and co-locating individuals. Existing animal tracking technologies, capable of detecting such events, are limited by one or more of: battery life; data resolution; location accuracy; data security; ability to co-locate individuals both spatially and temporally. Technology that at least partly resolves these limitations would be advantageous. European badgers (Meles meles L.), present a challenging test-bed, with extra-group paternity (apparent from genotyping) contradicting established views on rigid group territoriality with little social-group mixing.2. In a proof of concept study we assess the utility of a fully automated active-radiofrequency-identification (aRFID) system combining badger-borne aRFID-tags with static, wirelessly-networked, aRFID-detector base-stations to record badger colocations at setts (burrows) and near notional border latrines. We summarise the time badgers spent co-locating within and between social-groups, applying network analysis to provide evidence of co-location based community structure, at both these scales.3. The aRFID system co-located animals within 31.5 m (adjustable) of base-stations.Efficient radio transmission between aRFIDs and base-stations enables a 20 g tag to last for 2-5 years (depending on transmission interval). Data security was high (data stored off tag), with remote access capability. Badgers spent most co-location time with members of their own social-groups at setts; remaining co-location time was divided evenly between intra-and inter-social-group co-locations near latrines and inter-social-group co-locations at setts. Network analysis showed that 20-100% of tracked badgers engaged in inter-social-group mixing per week, with evidence of trans-border super-groups, that is, badgers frequently transgressed notional territorial borders.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers ( Meles meles ) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.