In our previous article, we highlighted the impact of illegal poaching on the African black rhinoceros and the African white rhinoceros as well as the importance of anti-poaching initiatives as a means of curbing poaching and protecting the rhino population from extinction. Although anti-poaching initiatives resulted in a slight reduction in this illegal activity, nature conservationists and other stakeholders still lack real-time data about rhino behaviour that could provide vital information about imminent poaching activities. Challenges that are faced include:
1. the vastness of the rhino’s natural habitat;
2. the irregular and complex terrain of the habitat, which makes wireless communication difficult;
3. the anatomical structure of the rhino which renders ex vivo attached devices to monitor and study them difficult;
4. the difficulty, risks and costs associated with capturing the rhino to attach and maintain sensors; and
5. the physiological, social and migratory behaviour of the rhinoceros.
These challenges impede the development and optimisation of any monitoring system which means no durable solution has been found yet.
In an attempt to solve some of these challenges, the Department of Electrical and Electronic Engineering developed and tested “an animal-borne automatic behaviour classification system that can provide information about rhino behaviour in real-time and assist with anti-poaching activities.” This consists of a real-time machine learning model embedded in the microprocessor of a biotelemetry tag which is attached to the rhino. Although the tracking system has been refined and improved, such as by the “inclusion of GPS, on-board data transmission and on-board behaviour classification has been made, battery life remains the greatest limitation”. One of the potential solutions currently being researched is the use of kinetic energy harvesting to ensure the system’s energy self-sufficiency.
How to harvest kinetic energy
As early as the 1830s, it was discovered that passing a magnet through a coil of wire generates a small electrical current. This is the concept upon which a shake flashlight is built – a person shakes the flashlight and a magnet passes back and forth through a coil of wire which, in turn, creates an electrical current that is stored in a capacitor. When the torched is switched on, this capacitor supplies the stored energy to the bulb. In other words, it works much like a battery-powered light but for the fact that the energy is generated by the shaking movement.
This is a fairly simple concept requiring only a few components – a magnet and a coil (to generate the power), a capacitor (to store the power), a switch, a body and a bulb. The quality and design and only the quality of these components will affect the end result.
Developing an energy harvester for footstep excitation
Energy harvesting, as explained briefly above, has become an increasingly popular field of research. The objective is to develop new ways of powering electrical devices in situations where conventional sources of power are unavailable. One such alternative source is kinetic energy, which together with the “mechanism of electromagnetic induction is used to generate electrical energy.” This concept was used in order to design a self-powering animal-borne sensor for use in the monitoring and conservation of large wildlife. The researchers have been able to optimise the performance of this energy harvester specifically for the impulse-like motion of the rhino’s leg. The severe power constraints of the sensor, and the need for energy self-sufficiency, make it crucial to maximise the harvested energy.
To achieve an optimal design, the researchers developed an “electrical and mechanical system model of a single-axis linear-motion kinetic energy harvester for impulsive excitation”. This model allows the generated power capacity to be mathematically optimised as a function of design parameters. The device comprises an assemblage of one or more spaced magnets that are suspended by a “magnetic spring and passing through one or more coils when motion is experienced along the axis”. The design factors that can be optimised include the number of coils, the coil height, coil spacing, the number of magnets, the magnet spacing and the physical size. This model was subsequently used to design “optimal energy harvesters for the case of impulse-like motion like that experienced when attached to the leg of [an animal]”. Several optimised designs were generated and ranked in terms of their predicted capacity output. The three best designs were subjected to a “controlled practical evaluation while attached to the leg of a human subject”.
The results demonstrate that the ranking of the measured output power corresponds to the ranking predicted by the optimisation, and the numerical model correctly forecasts the relative differences in generated power for complex motion. It is also found that all three designs far outperform a baseline design as might be found in a shake flashlight. Perfecting this design means soon the rhino tracking system will be able to use kinetic energy harvesting for energy self-sufficiency and so offer a long-term solution to keeping the system active.
