On a Wildlife Tracking and Telemetry System: A Wireless Network Approach

Abstract

Existing approaches to monitoring wildlife with wireless networks have not taken into account the vast heterogeneity inherent in the Animal Kingdom, especially with respect to bodyweight (and hence tag carrying capacity). This has resulted in a single design of tag which is only suitable for placement on larger animals. Thus, with existing technology, small animals cannot be monitored using the wireless network system. Motivated by the diversity of animals, a hybrid wildlife tracking system, EcoLocate, is proposed, with lightweight VHF-like tags and high performance GPS enabled tags, bound by a common wireless network design. Tags transfer information amongst one another in a multi-hop store and-forward fashion, and can also monitor the presence of one another, enabling social behaviour studies to be conducted. Information can be gathered from any sensor variable of interest (such as temperature, water level, activity and so on) and forwarded through the network, thus leading to more effective game reserve monitoring. Six classes of tracking tags are presented, varying in weight and functionality, but derived from a common set of code, which facilitates modular tag design and deployment. The link between the tags means that tags can dynamically choose their class based on their remaining energy, prolonging lifetime in the network at the cost of a reduction in function. Lightweight, low functionality tags (that can be placed on small animals) use the capabilities of heavier, high functionality devices (placed on larger animals) to transfer their information. EcoLocate is a modular approach to animal tracking and sensing and it is shown how the same common technology can be used for diverse studies, from simple VHF-like activity research to full social and behavioural research using wireless networks to relay data to the end user. The network is not restricted to only tracking animals – environmental variables, people and vehicles can all be monitored, allowing for rich wildlife tracking studies. To transfer the obtained data effectively through resource diverse nodes, a network protocol, termed the Adaptive Social Hierarchy (ASH) was designed that ranks nodes according to their resources, such as energy or connectivity. ASH provides a scalable and adaptable method for nodes to discover the role within the network, inspired by the way animals form linear dominance hierarchies through dyadic (pairwise) interactions. Three different methods of forming the social hierarchy are presented. In the first method, pairwise ASH, pairs of nodes exchange their attributes and their estimates of rank in a two-way exchange. Although this is a simple method of forming the hierarchy, it does not take advantage of the broadcast nature of the radio channel. In light of this, a one-way method of updating ranks is proposed and shown to be able to estimate the node ranks faster than pairwise ASH, due to multiple nodes receiving the same beacon. However, both methods are unable to form an accurate social hierarchy in a stationary network, due to a limited visibility horizon. It is shown how to extend the horizon by creating pseudo-connections between unconnected nodes, using an agent based approach. Simulation results are presented that demonstrate how the ASH concept can be used as a network underlay to enhance existing protocols or how it can form a cross-layer protocol in its own right. ASH is simple, scalable and has a negligible load on the network as ASH data piggybacks on top of existing network discovery packets. The focus is shifted from the network design to considering how to better schedule location fixes for power hungry GPS receivers. Existing wildlife tracking collars acquire fixes at constant time intervals. This leads to undersampling of high speed motion, and multiple redundant fixes when the animal is stationary. Uniform distance sampling of GPS locations is thus proposed. A low power, neck mounted, accelerometer is used to capture brief acceleration snapshots. An adaptive model, relating the snapshots to host speed is trained when the GPS unit is active. When the GPS receiver is powered down, the model is used to predict the speed of the host, and thus schedule GPS fixes at uniform distance intervals. The proposed scheme is implemented on low power 8 bit microcontrollers, and demonstrates that it is able to automatically learn the habits of the host animal or person. This technique can reduce collar power consumption by as much as 50%, whilst generating more accurate traces than uniform time sampling, as well as creating a detailed speed-time profile as a byproduct of the speed estimation process. This work has considered the problem of tracking and monitoring wild animals and their interaction and dependency on their environment. A scalable, modular and adaptable solution has been proposed, that allows small and large animals to be monitored using the same system and which automatically sends data through the network to the end user. Thus, this work has the potential to greatly enhance the understanding of animal behaviour, by providing large amounts of inter-related sensor data with minimal human input.