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.

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.

Currently, there is no existing method for automatically tracking the location of burrowing animals when they are underground, consequently zoologists only have a partial view of their subterranean behaviour and habits. Conventional RF based methods of localization are unsuitable because electromagnetic waves are severely attenuated by soil and moisture. Here, we use an as yet unexploited method of localization, namely magneto-inductive (MI) localization. Magnetic fields are not affected by soil or water, and thus have virtually unattenuated ground penetration. In this paper, we present a method that allows the position of an animal to be determined through soil. Not only does this enable the study of behaviour, it also allows the structure of the tunnel to be automatically mapped as the animal moves through it. We describe the application for tracking wild European Badgers (Meles meles) within their burrows, providing experimental data from a two month deployment.

Existing sensor network deployments for wildlife tracking (e.g. ZebraNet [1]) have concentrated on monitoring animal behaviour above-ground. However, a wide variety of animals create underground tunnels for shelter and protection whilst the animal is asleep. The extent and internal architecture of the underground structure varies considerably amongst species. For example, badgers excavate wide ranging underground tunnel systems which a number of animals inhabit in a community [6]. In addition, their tunnel structure is something which is often only determined by the destructive extreme of excavation [6]. As fossorial animals spend a large proportion of their lifetimes underground, this means that zoologists only have a partial view of their behaviour and habits. There is thus a need for a system which can localize animals whilst they are underground, in a non-invasive and automatic way.

Collecting data from source sensor nodes to designated sinks is a common and challenging task in a wide spectrum of sensor network applications, ranging from animal monitoring to security surveillance. A number of approaches exploiting sink mobility have been proposed in recent years: some are proactive, in that sensor nodes push their readings to storage nodes from where they are collected by roaming mobile sinks, whereas others are reactive, in that mobile sinks pull readings from nearby sensor nodes as they traverse the sensor network. In this paper, we point out that deciding which data collection approach is more energy-efficient depends on application characteristics, including the mobility patterns of sinks and the desired freshness of collected data. We illustrate cases where combining proactive and reactive modes of data collection is particularly beneficial. This motivates the design of TwinRoute, a novel hybrid algorithm that can flexibly mix the two collection modes at appropriate levels depending on the application scenario. Our extensive experimental evaluation, using synthetic and real-world network topologies and sink traces, shows that TwinRoute outperforms the pure approaches, achieving desirable tradeoffs between energy expenditure and timely delivery of sensor data.

Wireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take considerable effort to replace, it is of major importance to use energy carefully. We present two efficient ways of extending the lifetime of such systems: 1. an adaptive duty cycling protocol and 2. an adaptive data management protocol. Further, we present some details of our deployed sensor network in Wytham Woods, Oxfordshire.