The combination of using Wi-Fi and ZigBee technologies in an application to monitor cattle has the potential to increase the range of the network when the animal herd is roaming out of range of the sink. This paper investigates combining a Wi-Fi and ZigBee transceiver on the same sensor node to reliably obtain data of mobile cattle in a field. The solution proposed is a hybrid network conjoining the IEEE 802.11b/g and IEEE 802.15.4 protocols. Cattle as herd animals always roam in a herd manner. It has been observed that when the herd moves far away from the sink, the link between the herd and the sink breaks because there is no relay node in the middle. In order to maintain the link between the herd and the sink a heterogeneous network is proposed. The proposed solution extends the range of a ZigBee network used for cattle monitoring by pairing with the IEEE 802.11b/g protocol. In this paper, we implement two protocols IEEE 802.11b/g and 802.15.4/ZigBee in a hybrid network. The nodes have been designed to avoid interference between the two protocols and ensure good coverage of 802.11b/g in the area where the cattle moves. Moreover the design had to be cost effective so it could be useful for small to medium or large commercial farmers who are most susceptible to cattle theft.
Stock theft is a major problem in the agricultural sector in South Africa and threatens both commercial and the emerging farming sectors in most of the country. Although there have been several techniques to identify cattle and combat stock theft, the scourge has not been eradicated in the farming sector. This paper investigates how we can model cow behaviour using global positioning wireless nodes to get the expected position of a cow. The objective of this research is to model the typical behaviour of a cow to determine anomalies in behaviour that could indicate the presence of the thieves. A wireless sensor node was designed to sense the position and speed of a cow. The position and the speed of the cow are collected for analysis. A random walk model is applied to the cow's position in order to determine the probability of the boundary condition where we assume there is an increased probability of a cow on the boundary position being stolen. The Continuous Time Markov Processes (CTMP) is applied to the movement pattern of an individual cow in order to find the probability that the cow will be at the boundary position. The value of 2.5 km/h has been found as our treshold to detect any agitation of the animal. The cow has less probability to be at the boundary position. The predictive model allows us to prevent stock theft in farms especially in South Africa and Africa in general.
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