Cattle Monitoring System Using Wireless Sensor Network in Order to ...

IST-Africa 2014 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds) IIMC International Information Management Corporation, 2014 ISBN: 978-1-905824-44-1

Cattle Monitoring System Using Wireless Sensor Network in Order to Prevent Cattle Rustling P. Kibambe Mashoko NKWARI, S. RIMER and B.S. PAUL University of Johannesburg, Corner Kingsway and University Road, Department of Electrical and Electronic Engineering Science, Johannesburg, 2006, South Africa E-mail: [email protected], [email protected] Abstract: 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. Keywords: wireless sensor network, Continuous Time Markov Processes, cattle rustling.

1. Introduction In South Africa and in most African countries stock theft threatens the livelihood of livestock farmers. For example, in the period 2010/2011, goats to the value of R36.3 million, sheep to the value of R85.8 million and cattle worth R256 million Rands were stolen in South Africa [1]. The impact of stock theft on resource-poor farmers is more severe than on commercial farmers, because they own small numbers of animals and often their livestock is their only source of income and sustenance. For each theft from a commercial farm, there were three thefts from an emerging farmer [2, 3]. Stock theft also increases the cost of production for the agricultural sector and ultimately, food prices rise. There are numerous factors contributing to stock theft such as quick cash yield, unattended grazing as many subsistence farmers allow their animals to wander in search of suitable grazing land, and leaving their livestock in grazing fields for long periods without counting them. Stolen livestock is either sold for the pot in townships and other densely populated residential areas, with the resulting negative health consequences of such unregulated livestock trading or taken across the country's borders or to other provinces within South Africa [3]. In addition, in a study on the causes of cattle rustling in Kenya, the authors found that illiteracy has increased the severity of cattle rustling [4]. Copyright © 2014 The authors www.IST-Africa.org/Conference2014

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Wireless sensor nodes have been used to optimize pasture utilization, monitor temperature changes anG WUDFN DQ DQLPDO¶V ORFDWLRQ [5]. The utilisation of mobile wireless sensor nodes allows data collection for animal behavioural studies and may help prevent livestock theft as well as unnecessary loss due to environmental stresses. The data collected from these wireless sensor nodes needs to be correctly analysed and interpreted in order to extract useful information from the data set. This information allows us to obtain a meaningful statistical pattern that could alert the farmer when an animal is stolen or gets left behind. In this project, we built a sensor node that allows us to collect the position of cattle each second and analyse the path of the individual animal with a statistical tool. The statistical tool we chose is the continuous time random walk, also known as the continuous time Markov process (CTMP), which has been applied to various systems in order to predict or to model a specific phenomenon. The use of random walk models is a growing area of applied mathematics that is being increasingly used to model biological systems, notably in ecology (animal movements) and pathos-physiology [6].A random walk is the stochastic process formed by successive summation of independent, identically distributed random variables and is one of the most basic and well-studied topics in probability theory [7]. Animal movement is a stochastic system where the path is always unknown. A cow makes a succession of random steps in a random direction which can be seen as a random walk system. We apply CTMP to the behaviour of the animal¶V PRYHPHQW in order to model its behaviour. ,IZHFDQPRGHODOLYHVWRFNDQLPDO¶VJHQHUDOPRYHPHQWSDWWHUQVEDVHGRQthe time of day, we can use probabilistic models to determine when the animal is close to a boundary SRVLWLRQ RU KDV PRYHG RXWVLGH LWV FXUUHQW ³FRPIRUW ]RQH´ 7KLV QRQ-conformant behaviour may indicate that the livestock is being stolen. The farmer and law enforcement officials can be informed in real-time of the theft which will reduce the amount of theft and increase the likelihood of successful prosecution. In this paper, we designed a wireless sensor node with a GPS to collect data on a FRZ¶V FXUUHQW position and speed if moving. 7KLV GDWD LV XVHG WR PRGHO WKH DQLPDO¶V movement using a Probability Distribution Function (PDF) in order to determine the probability of the animal being stolen. The field where the animal is not threatened by thieves is demarcated. According to data collected on animal behaviour, the probability that the cow is at the boundary of the field is determined. If the cow is at a boundary position we have to determine the time that it is going to spend there then compare this time with the threshold time. The threshold time is determined based on data collected about the animal¶s location and movement patterns in the field using a wireless sensor node. When the threshold probability or the threshold time exceeds a specified value, the farmer is notified. The speed of the cow is also useful to determine if the animal is being stolen. Under normal conditions the cow has a certain speed that will not exceed a specific value. This value is used as a threshold, that when exceeded may indicate theft. The main contribution of this paper is to determine the effectiveness of using the continuous time Markov process to determine if a cow is being stolen or not. Our algorithm is based on determining the probability that an animal will move outside its boundary FRQGLWLRQV RU DW DQ LQFUHDVHG VSHHG EDVHG RQ DFWXDO GDWD FROOHFWHG DERXW WKH DQLPDO¶V movement patterns using a wireless sensor node. The remainder of the paper is organized as follows. In section 2, we discuss related work on the use of wireless sensors in precision agriculture, with a specific focus on livestock monitoring as well as discussions by other UHVHDUFKHU¶V on the different usages of random walk models that was used to detect anomalies of observed behaviour. Section 3 describes the design of the wireless sensor node and section 4 provides an overview of the CTMP model, and the application of the model to prevent stock theft. In section 5, we discuss the results of our experiments and analyse the effectiveness of the models performance. We conclude our work in section 6.

2. Related work Wireless sensor networks (WSNs) is an emerging technology generating a large amount of scientific interest due to the possibility of obtaining more data of a physical phenomenon in real-time. Pioneering work on the use of wireless sensor nodes (hereafter referred to as nodes) to monitor herd behaviour and social interactions and grazing patterns of cattle has been done by researchers at the The Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia [5, 8, 9, 10, 11, 12]. For example, Guo et al [5] used a WSN for automated livestock monitoring and control. They built sensors that were able to FROOHFW LQIRUPDWLRQ VXFK DV HDFK DQLPDO¶V ORFDWLRQ VSHHG WHPSHUDWXUH -axis acceleration values, and 3-axis magnetic field strength. They found that it was difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. CSIRO researchers developed models for relating SRVLWLRQ YHORFLW\ DQG LQHUWLDO REVHUYDWLRQV IURP DQLPDOV WR VSHFLILF OHYHOV RI DQ DQLPDO¶V state. Wark et al also used virtual fencing bordering an environmentally sensitive area to prevent livestock from wandering outside a demarcated area [12]. Kwong et al developed a wireless node and GPS system, to determine the movement and distribution of cattle within a herd. The authors focused on the impact cattle mobility had on network connectivity. Two data transport schemes are proposed to facilitate sending realtime data about an animal's status to the farm manager. Then two analysis metrics, namely connection availability and connection duration, are used to quantify the impact of cattle movement on network connectivity [13]. Other applications of wireless sensor nodes in precision agriculture and livestock monitoring include research done by Mittal et al [14], who propose the design and development of a low cost WSN platform for precision agriculture applications. The authors proposed solution allows for an increase in spatial resolution with a farm size of as small as a IHZDFUHV7KLVLVEHQH¿FLDOIRUPDUJLQDOIDUPHUVLQGHYHORSLQJQDWLRQVZKHUHIDUPHUVKave smaller land holdings. Li et al have also used WSNs to collect data on a cow's body temperature and behaviour characteristics in order to determine the fertility of the animal [15]. In the aforementioned papers, the researchers used different wireless transceivers which are based on IEEE 802.15.4 to wirelessly communicate with a central data collector. However, none of the research discussed previously specifically focus on utilising WSNs and livestock behaviour patterns to prevent or detect stock theft. Random walk theory has been used in various fields to predict certain phenomena or model a system. Most of the results can be generalised and re-applied to the different scenarios. Codling et. al [6] showed that animal and cell movements are often characterized by some directional correlation (persistence). Efendiev et al [16] used random walk theory to study the preconditioning of Markov chain Monte Carlo (MCMC) simulations using inexpensive coarse-scale runs in inverse problems related to subsurface characterization. The purpose of preconditioning was to reduce the near-scale computational cost and increase the acceptance rate in the MCMC sampling. Their goal was achieved by generating Markov chains based on two-stage computations. Moonesinghe and Tan [17] presented a stochastic graph-based algorithm, called OutRank, for detecting outlying objects. The underlying dataset is represented as a weighted undirected graph, where each node represents an object and each (weighted) edge represents similarity between objects. By transforming the edge weights into transition probabilities, the system is modelled as a Markov chain and the dominant eigenvector of the transition

probability matrix can be calculated. The values in the eigenvector were then used to determine the outliers of each object. Li [18] utilised the Markov random field (MRF) theory in image analysis. MRF theory provides a basis for modelling contextual constraints in visual processing and interpretation. The author devoted considerable attention to the problems of parameter estimation and function optimization, both of which are crucial in the MRF paradigm. Specific attention was given to the estimation of MRF parameters in the context of object recognition, and to the issue of algorithm selection for MRF-based function optimization. Modelling problems were addressed mainly from the computational viewpoint.

3. WSN Design A WSN consists of a varying number of low power wireless nodes (which are equipped with one or more mechanical, thermal, biological, chemical, optical, and magnetic sensors), working together to monitor a region to obtain data about the physical world [19]. A WSN has a wide range of applications include health care monitoring, air pollution monitoring etc. The design of the node takes in consideration various factors such as cost and ease of deployment. The node used in this experiment contained a geo-localisation module Gmsu1LP GPS from GlobalTop company; a Microchip microcontroller (18f25k22) and a Xbee (S1 XB24-AWB-001) RF transceiver. The microcontroller is used to compute the data received from the GPS module. The GPS module sends data in National Marine Electrical Association (NMEA) standard. The micro-controller uses the data to extract the latitude, longitude, speed, time, and date. The transceiver (Xbee) operates in the 2.4 GHz frequency band and has a data rate of 250kbps. The XBee transceiver uses ZigBee which uses the same physical and data link formats as specified in the IEEE 802.15.4 standard. The 2.4 GHz frequency band is part of the Industrial, Scientific and Medical (ISM) frequency ranges and is currently unregulated in South Africa. It was decided not to use a GSM transceiver because it requires subscription to a mobile carrier. Certain areas within South Africa and other parts of Africa may not always have GSM coverage. To connect all components together we used a Breeze board from Dizzy enterprises which includes a secure microSD socket to store data onto a secure digital (SD) 2GB memory card. An SD card allows GDWDDERXWWKHDQLPDO¶VPRYHPHQWDQGORFDWLRQto be stored for later retrieval if the animal moves out of range of the central sink receiving station. The various components of the node are shown in Figure 1. Figure 2 shows the node placed on a cow in the field.

Figure 1: node

Figure 2: node worn by the cow

To reduce stress and irritation to the animal, the QRGH¶VVL]HDQGZHLJKW ZHUHLPSRUWDQW factors in our design consideration. An outstanding factor that needs to be still designed is a lightweight waterproof enclosure. Also, the size needs to be made smaller to ensure that it is not easily visible from a distance to thieves.

4. Markov Process Model 4.1 Definitions

1. A stochastic process is a sequence of random variables indicating the random movement patterns of an animal. In this experimental model a stochastic process is a sequence of steps where the events (outcomes) at any stage depend on some probability. 2. A Continuous Time Markov process (CTMP) is a stochastic process with the following properties: (a.) The number of possible outcomes or states is finite. (b.) The outcome at any stage depends only on the outcome of the previous stage. (c.) The total probability in an experimental space is always one. 4.2 Theoretical Summary of CTMP

Let S be the space which the cows occupy and move randomly. Assume the cow has the same step size. This step is denoted by and let the number of steps be. The step made by the cow in time interval ሺͲǡ –ሻ is a stochastic variable. Consider the cow has started its movement from the origin at the time – ൌ Ͳ. After movement, the cow is stationary for a period of time before the next movement. This can be modelled as the cow being stationary to its current position until time –ଵ , it then makes a jump to ο”ଵ . The cow waits on ο”୧ until time – ଶ ൐ –ଵ when its jump to a new position ο”ଵ ൅ ο”ଶ Ǥ The process is continues to the next movement. The two sets obtained, namely: ɒ ൌ ሼ–ଵ ǡ – ଶ ǡ ǥ ǥ Ǥ Ǥ – ୬ ሽ the times of jumping events and the displacement ” ൌ ሼ”ଵ ǡ ”ଶ ǡ ǥ ǥ Ǥ Ǥ ”୬ ሽ are mutually independent. The cow can only take one position at a time because it cannot be at two places in the same time. . We assume that the system has an infinite state because a cow can take an infinite number of positions in the field, which is random. This system can be seen as a continuous time Markov process. In this particular scenario the possible ways to reach one position is call a transition and each transition has a particular transition probability. For example the transition from the point i to point j has ୧୨ probability and the inverse has ୨୧ probability. Intuitively, we can see the cow has infinite possible ways to reach j from i. The total probability ሺ୧୨ ሻthat the cow moves from i to j is the summation of all possible paths from the point i to j. The presence of the cow at the point j or i is mutually exclusive so if the cow is located at j there is no possibility to find the cow at i at the same time. According to Markov axiom each time that the cow is found at the location i the probability that this particular cow will be found at location j is ୧୨ no matter how the cow got there. All these assumptions are interpreted in a Markov chain as follows: there is a step that is taken by the cow from the state i to any neighbour states and so on until state j is reached. From the state i to j this cow has to take n random number of jump steps. We consider that every time the cow has the possibility to stay at the same given state for some period. The period that the cow spends on one place is called waiting time. This description of the cow movement is a continuous time Markov chain. This latter describes roughly the cow behaviour. The probability ୧୨ after n steps can be written as follow. ሺ୬ሻ ୧୨ ൌ ୰ ሺ୬ ൌ Œȁ଴ ൌ ‹ሻሺͳሻ In our case we are interested by the probability to find the cow at a boundary position in the field. To simplify our study we put one cow in an enclosure (Figure 3 and Figure 4) and took measurements of the FRZ¶Vposition and speed. Given that the probability at all four boundaries is’ଵ Ǣ ’ଶ Ǣ ’ଷ Ǣ ’ସ , as shown in Figure 3, then the total boundary probability is: ’ୠ ൌ ’ଵ ൅ ’ଶ ൅’ଷ ൅ ’ସ ሺʹሻ. The probability to find the cow at each probability ’ଵ Ǣ ’ଶ Ǣ ’ଷ Ǣ ’ସ is:

’ଵ ൌ ሾƒଵ ൑ ” ൑ „ଵ ሿ ൌ

෍ ሼ୩ǣୟభ ஸ୰ౡ ஸୠభ ሽ

ͳ ሺ͵ሻ 

Till to the ’ସ by only changing the index of a and b from 1 to 4. ǡwith ƒଵ ǡ ƒଶ ǡ ƒଷ ǡ ƒସ ǡ „ଵ ǡ „ଶ ǡ „ଷ ǡ „ସ the point limit of each boundary respectively. To be more precise if  ՜ λ then ο” ՜ ͲǤ By letting ο” ՜ Ͳ we avoided any error in approximation of the position. The sum of the individual probability of each position becomes an integral for the respective boundary: ୠ ’ଵ ൌ ሾƒଵ ൑ ” ൑ „ଵ ሿ ൌ ‫׬‬ୟ భ ’ሺ”୩ ሻ†” (4) భ This same computation will be doing for ’ଶ Ǣ ’ଷ Ǣ ’ସ. Note that ’ሺ”୩ ሻ has a value 1 in the interval Ͳ ൏ ‫ ݎ‬൏ ͳ. From equation 8, the probability of the cow finding itself in the interval ሾƒଵ ǡ „ଵ ሿ which is the area of the boundary is ’ଵ Ǥ

Figure 3: Boundaries

Figure 4: Entire field where the cow can graze

4.3 Application of CTMP to model the movement patterns of a cow

With a Correlate Random Walk (CRW) [6] it is not always possible to calculate P(r, t) directly, or even to derive a system of differential equations for P(r,t). The probability density function (PDF) was determined via an experiment. In order to find the probability that the cow has to find itself in a certain region we need to compute the integral of the PDF. Let say we need to find the probability that the cow will be in the interval ’ଵ ൌ ሾƒଵ ൑ ” ൑ ୠ „ଵ ሿ , then the computation will be ’ଵ ൌ ሾƒଵ ൑ ” ൑ „ଵ ሿ ൌ ‫׬‬ୟ భ ’ሺ”୩ ሻ†”. భ Our objective was to find which cow has the greater probability to be stolen. We expect at any specific time to find the cow in a certain area based on the PDF. The area of the expectation is where the cow can graze and drink water. The area out of these two places can be assumed as a red zone for the cow which means the system has to alert the farmer.

5. Results and Discussion For our experiment, a wireless node is attached to a cow in order to collect data about the location of the cow each second. We left the cow in a closed field for three hours while the node is attached to its neck. In Figure 4 and Figure 5, the position of the cow at 5 and 10 second time intervals are plotted using Matlab software.

Figure 5 : position of the cow analysed with 5 second interval

Figure 6 : position of the cow analysed with 10 second interval

We mapped the movement data using Google Maps as shown in Figure 7 and Figure 8. Figure 7 shows the movement of the cow within the enclosure, while Figure 8 shows the boundary of the enclosure.

Figure 7 :movement of the cow in the enclosure

Figure 8: enclosure

5.1 Position Analysis

After data has been collected, we analysed it using a Matlab algorithm. We did the analyses on two parameters, namely; the latitude and the longitude in order to find the probability density function (PDF) of each other. Figure 9 shows the latitude data and the PDF which best fit the data is shown in Figure 10. As shown in Figure 10, the PDF that best fits the data is the Gaussian distribution or the normal distribution. This function has a mean equal to 26.2875 (latitude in decimal format) and the variance equal to ͳǤʹͳʹʹ͹Ǥ ͳͲିଽ.

Figure 9: latitude data

Figure 10 : Probability density function of the latitude

A similar analysis was done for the longitude as shown in Figure 11 and Figure 12. The longitude best fit PDF is also a normal distribution function. Thus the position of the cow

is determined with a normal PDF. For the longitude the mean is equal to 28.1965, and the variance is equal to 2Ǥ ͸ͻ͸͹͹Ǥ ͳͲିଽ .

Figure 12 : Probability density function of the longitude

Figure 11 : longitude data

Our model shows that to get the probability in a certain region we need to compute the integral of the PDF in the region of interest. Because we know that our PDF is a normal distribution, then to get any probability we can just compute the normal distribution equation. ଵ

Thus the PDF is ݂ሺ‫ݎ‬ሻ ൌ ఙξଶగ ݁

ି

ሺೝషഋሻమ మ഑మ

where µ is the mean or the expectation of the position.

7KH ı LV WKH VWDQGDUG GHYLDWLRQ WKH ߪ ଶ is the variance. As we can see from Figure 12 the probability to find the cow in the interval [28.1964, 28.1964] is less then in the interval {28.1965, 28,1965]. 5.2 . Speed Analysis

Figure 13 : Speed of the cow during experiment

During the experiment the speed of the cow was continuously collected. The speed of the cow is shown in Figure 13, plotted as a PDF. It can be observed that the cow was not highly agitated and moved at a slow speed. Most of the time it moves at 0.5 km/h. This plot gives us the average speed of the cow. By observing this plot we found that the cow rarely moves with a speed of 2.5km/h. The value 2.5 km/h is therefore used as our treshold to detect any agitation. For speed the probability density function that best fits is a t distrubition.

6. Conclusion We proposed a framework to prevent stock theft by applying the Continuous Time Markov Process to cattle movement. The integration of the probability density function allows us to find the probability RIDQDQLPDO¶VORFDWLRQDQGVSHHGin any region.

We have applied the Markov process to cattle in order to obtained random tendencies for a given probability. In practice, this approach will enable us to see the behaviour of the individual cow specifically and for the herd in general. The behaviour is plotted to enable us to obtain an expected value RIWKHFRZ¶VSRVLWLRQ,QWKLVZD\ZHFDQILQGWKHWKUHVKROGPHDQ for the threat zone. The plots seen in Figures 9-12 provide an idea of which probability is the best fit for our system. By observing the four figures 9, 10, 11 and 12, we have been able to determine the best option for theft prevention is placement in the centre of the field, because the cow spent most of its time close to the centre. The movements that are considered as a threat is the one close to the boundary because the cow GRHVQ¶W spend a significant amount of its time there. Therefore if a cow is at a boundary position with rapid movement, it may indicate stock theft and thus our system can be used to prevent cattle rustling. Our model can be used to explain the cow¶V behaviour. In cases where the cow has a bias to a particular position the Fokker-Planck Equation can be used to resolve the biased movement solution. With the normal PDF we can simulate any cow behaviour. In case the time interval is small only small changes in position can occur, thus the time evolution of a Markov random process can be rewritten as a differential equation. Although the system has been designed to monitor cattle behaviour we can extend its application to implement it on other livestock or wild animals, such as rhino which are highly threatened. The node we built can be used in a game reserve as well. In South Africa, rhinos are threatened of extinction. This node will collect the movement patterns of a rhino and can transmit its current location to a central sink. This kind of implementation will necessitate additional nodes to extend the range of the network. Using a combination of the IEEE 802.15.4 protocol with WiFi or GSM depending on the area of deployment may be a viable solution to this problem.



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