Performance Evaluation and Improvement of Wireless RSSI Based Animal Theft Detection
Main Article Content
Abstract
The use of wireless RSSIs to detect animal theft in an outdoor environment is evaluated. Using simulation experiments, three multilateration techniques for localization are looked at and compared in terms of how accurate they are at detecting things. The last technique is proposed because it requires less computing power. More specifically, the first two techniques are based on setting up an overdetermined set of linear equations with three and two location-related variables, respectively. The proposed technique is the two-variable method simplified when anchor nodes are arranged as a rectangular grid. The investigation of anchor node locations for multilateration is next considered. Finally, a limited motion condition is proposed to reduce the effects of noise in RSSI values on the detection accuracies. Numerical results indicate a trade-off between detection performance improvement and time delay in alarm generation.
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References
P. K. Mashoko Nkwari, S. Rimer and B. S. Paul, “Cattle monitoring system using wireless sensor network in order to prevent cattle rustling,” 2014
IST-Africa Conference Proceedings, Pointe aux Piments, Mauritius, 2014, pp. 1-10.
O. Dieng, B. Diop, O. Thiare, and C. Pham, “A study on IoT solutions for preventing cattle rustling in african context,” Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, Mar. 2017.
O. Dieng, C. Pham, and O. Thiare, “Outdoor Localization and Distance Estimation Based on Dynamic RSSI Measurements in LoRa Networks: Application to Cattle Rustling Prevention,” 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Oct. 2019.
N. L. Scott, B. Hansen, C. A. LaDue, C. Lam, A. Lai, and L. Chan, “Using an active Radio Frequency Identification Real-Time Location System to remotely monitor animal movement in zoos,” Animal Biotelemetry, vol. 4, no. 1, Aug. 2016.
L. Liu, Y. Yao, Z. Cao, and M. Zhang, “DeepLoRa: Learning Accurate Path Loss Model for Long Distance Links in LPWAN,” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, May 2021.
M. Aernouts, R. Berkvens, K. Van Vlaenderen, and M. Weyn, “Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas,” Data, vol. 3, no. 2, p. 13, Apr. 2018.
F. Maroto-Molina et al., “A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd,” Sensors, vol. 19, no. 10, p. 2298, May 2019.
T. Janssen, R. Berkvens, and M. Weyn, “RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell,” Sensors, vol. 20, no. 21, p. 6172, Oct.
L. Nóbrega, A. Tavares, A. Cardoso and P. Gonçalves, “Animal monitoring based on IoT technologies,” 2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany), Tuscany, Italy, 2018, pp. 1-5.
W. Choi, Y.-S. Chang, Y. Jung, and J. Song, “Low Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm,” ISPRS International Journal of Geo-Information, vol. 7, no. 11, p. 440, Nov. 2018.
A. Ur Rehman et al., “Implementation of an Intelligent Animal Monitoring System Using Wireless Sensor Network and IoT Platform,” 2022 International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates, 2022, pp. 1-11.
B. C. Fargas and M. N. Petersen, “GPS-free geolocation using LoRa in low-power WANs,” 2017 Global Internet of Things Summit (GIoTS), Jun. 2017.
T. Hadwen, V. Smallbon, Q. Zhang, and M. D’Souza, “Energy efficient LoRa GPS tracker for dementia patients,” 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2017.
S. Koompairojn, C. Puitrakul, T. Bangkok, N. Riyagoon, and S. Ruengittinun, “Smart tag tracking for livestock farming,” 2017 10th International Conference on Ubi-media Computing and Workshops (Ubi- Media), Aug. 2017.
K. Mekki, E. Bajic, F. Chaxel, and F. Meyer, “A comparative study of LPWAN technologies for large-scale IoT deployment,” ICT Express, vol. 5, no. 1, pp. 1–7, Mar. 2019.
W. Choi, Y.-S. Chang, Y. Jung, and J. Song, “Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm,” ISPRS International Journal of Geo-Information, vol. 7, no. 11, p. 440, Nov. 2018.
M. Anjum, M. A. Khan, S. A. Hassan, A. Mahmood, H. K. Qureshi, and M. Gidlund, “RSSI Fingerprinting-Based Localization Using Machine Learning in LoRa Networks,” IEEE Internet of Things Magazine, vol. 3, no. 4, pp. 53–59, Dec. 2020.
K.-H. Lam, C.-C. Cheung, and W.-C. Lee, “New RSSI-Based LoRa Localization Algorithms for Very Noisy Outdoor Environment,” 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Jul. 2018.
K.-H. Lam, C.-C. Cheung, and W.-C. Lee, “RSSI-Based LoRa Localization Systems for Large-Scale Indoor and Outdoor Environments,” IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 11778–11791, Dec. 2019.
GNU Octave: Scientific Programming Language, octave.org/index.html.
A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.