We propose and develop an electrical and mechanical system model of a singleaxis linear-motion kinetic energy harvester for impulsive excitation that allows its generated load power to be numerically optimised as a function of design parameters. The device consists of an assembly of one or more spaced magnets suspended by a magnetic spring and passing through one or more coils when motion is experienced along the axis. The design parameters 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. We use the proposed model to design optimal energy harvesters for the case of impulse-like motion like that experienced when attached to the leg of a human. We generate several optimised designs, ranked in terms of their predicted load power output. The three best designs are subsequently constructed and subjected to controlled practical evaluation while attached to the leg of a human subject. The results show that the ranking of the measured output power corresponds to the ranking predicted by the optimisation, and that the numerical model correctly Predicts the relative differences in generated power for complex motion. It is also found that all three designs far outperform a baseline design. The best energy harvesters generated an average power of 3.01mW into a 40Ω test load when driven by footsteps whose measured peak impact was approximately 2.2g. With respect to the device dimensions, this corresponds to a power density of 179.380µW/cm 3 .
Background:The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results:The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions:We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.
We propose a routing scheme called energy-efficient beaconless geographic routing with energy supply (EBGRES) for wireless sensor networks. EBGRES provides loop-free, fully stateless, energy-efficient source-to-sink routing with minimal communication overhead without the help of prior neighborhood knowledge. It locally determines the duty-cycle of each node, based on an estimated energy budget for each period, which includes the currently available energy, the predicted energy consumption and the energy expected from the harvesting device. In EBGRES, each node sends out the data packet first rather than a control message. By sending a data packet first, EBGRES performs the neighbor selection only among those neighbors that successfully received the data packet. EBGRES uses a three-way (DATA/ACK/SELECT) handshake and a timer-assignment function, the Discrete Dynamic Forwarding Delay (DDFD). We investigate the lower and upper bounds on hop count and the upper bound on energy consumption under EBGRES for source-to-sink routing. We further demonstrate the expected total energy consumption along a route toward the sink with the proposed EBGRES approach including a lower bound on energy consumption when the node density increases. a cost effective, ubiquitous, commonly known, and well understood powering technology. However, they present specific challenges that include finite useful life, replacement cost, and disposal concerns. Although they are an ideal solution for many applications, there are many other applications where batteries fail to fit application requirements: for example, the asset is not available to replace the batteries, the cost of battery replacement is too expensive over the life of the product, the device is in a hazardous environment, or the device is embedded and a continuous power supply is required. Applications with these needs provide a good fit for receiving power via ambient energy harvesting [8].Unlike the microprocessor industry or the communication hardware industry, where the computation capability or the line rate has been continuously improved (almost doubled every 18 months), battery technology has been relatively unchanged for many years [8]. Ambient energy harvesting as a power solution has steadily gained momentum in recent years, especially with significant progress in the functionality of low power embedded electronics such as wireless sensor nodes. We define an energy harvesting node as any system that draws part or all of its energy from the environment such as solar energy, temperature variations, kinetic energy or vibrations. A key distinction of this energy from that stored in the battery is that this energy is potentially infinite, although there may be a limit on the rate at which it can be used. Energy harvesting sensor nodes have either an onboard energy harvesting component as shown in Figure 1, or a sensor node can be connected to an energy harvesting device/component to form one device. In our research we look at solar-based energy harvesting, which has a diurnal ch...
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