Animal Health Monitoring and Intrusion Detection System based on LORAWAN

Mohandass, S

doi:10.17762/turcomat.v12i2.2050

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…According to Balsa-Barreiro et al [10], driving behaviour of people in real/world situations requires exhaustive data collection by using large areas of sensors and cameras, potentially leading towards use of driving patterns based on several factors. Mohandass et al [11] proposed an animal-vehicle collision system realized as an Animal Intrusion Detection System with Passive Infrared (PIR) and motion sensors integrated within a LoRaWAN network. CupCarbon Software was used for analysing performance of the system.…”

Section: Related Workmentioning

confidence: 99%

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

Jotanovic,

Jausevac,

Perakovic

et al. 2024

Preprint

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The network of rural roads covers different types of terrain, including forest areas, pastures, arable land and sparsely populated areas. The safety of people and animals is a priority in traffic on these roads. Early detection of pedestrians, animals and other moving objects along the road can significantly reduce the risk of accidents. As part of this research, a sensor system is being developed that can detect characteristics of living things in motion, such as unexpectedly crossing the road without clear signs. Such timely detection of moving objects enables adequate preventive measures and reduces potential traffic accidents. The consequences of traffic accidents of this type can cause serious damage to animals and people property, and road infrastructure. The topicality of this problem at the spatial and seasonal level is emphasized in studies that identify the hotspots of these accidents. Factors such as traffic characteristics and road infrastructure are key to modeling protective systems on rural roads. The presented study investigates the deployment of sensor nodes and LoRAWAN gateways for wildlife detection on rural roads, with the aim of reducing the risk of traffic accidents caused by Wildlife-Vehicle Collisions.

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Section: Related Workmentioning

confidence: 99%

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

Jotanovic,

Jausevac,

Perakovic

et al. 2024

Preprint

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“…The ultrasonic sound generator was activated by Passive Infrared (PIR) Sensor. [12] proposed a Smart Animal Intrusion Detection System that monitors the movement of wild animals by integrating camera, PIR and motion sensor. If any one of the sensor detects animal, the intimation regarding the presence of animal will be sent to the farmer.…”

Section: Literature Surveymentioning

confidence: 99%

SMART REPELLER: A Smart system to prevent Rhesus Macaque Trespassing in Human Settlements and Agricultural Areas

et al. 2024

EAI Endorsed Trans IoT

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Rhesus macaque trespassing is a widespread problem where wild Rhesus macaque monkeys enter human settlements and agricultural areas, causing various issues such as property damage, food theft, and health risks to humans. These primates also cause significant economic losses by raiding crops, damaging plants, and disrupting the natural balance of the ecosystem. To address this problem, a research paper proposes a technology-based solution called Smart Repeller, which uses ultrasonic sound waves and Calcium Carbide Cannon, along with computer vision technology and artificial intelligence to detect the presence of monkeys and activate repelling mechanisms automatically. The proposed device eliminates the need for human intervention, making it efficient and cost-effective. Our paper aims to demonstrate the feasibility and effectiveness of the proposed device through experimental studies and simulations, with the ultimate goal of providing a practical and scalable solution to mitigate the problem of Rhesus macaque trespassing in human settlements and agricultural areas.

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“…ZigBee, RFID, Bluetooth, and Wi-Fi play pivotal roles in the efficient transmission of this data[16].Despite the advancements, certain limitations persist with technologies like ZigBee, which currently supports monitoring only one animal at a time. In contrast, integrating LoRaWAN and NbIoT has shown promise in enhancing data transmission efficiency, demonstrating low operational costs and extended communication ranges, even in complex environments[41]. The amalgamation of NB-IoT and LoRa technologies is particularly advantageous, offering extended transmission distances and reduced operational costs.…”

mentioning

confidence: 99%

Development of a Cloud-Based IoT System for Livestock Health Monitoring Using AWS and Python

Bhaskaran,

Gordon,

Neethirajan

2024

Preprint

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The agriculture industry is currently facing significant challenges in effectively monitoring the health of livestock. Traditional methods of health monitoring are often labor-intensive, inefficient, and insufficiently responsive to the needs of modern farming. As the number of IoT devices in agriculture proliferates, issues of scalability and computational load have become prominent, necessitating efficient and scalable solutions. This research introduces a cloud-based architecture aimed at enhancing livestock health monitoring. This system is designed to track critical health indicators such as movement patterns, body temperature, and heart rate, utilizing AWS for robust data handling and Python for data processing and real-time analytics. The proposed system incorporates Narrow Band IoT (Nb IoT) technology, which is optimized for low-bandwidth, long-range communication, making it suitable for rural and remote farming locations. The architecture’s scalability allows for the effective management of varying numbers of IoT devices, which is essential for adapting to changing herd sizes and farm scales.Preliminary experiments conducted to assess the system’s performance have demonstrated its durability and effectiveness, indicating a successful integration of AWS IoT Cloud services with the deployed IoT devices. Furthermore, the study explores the implementation of predictive analytics to facilitate proactive health management in livestock. By predicting potential health issues before they become apparent, the system can offer significant improvements in animal welfare and farm efficiency. The integration of cloud computing and IoT not only meets the growing technological needs of modern agriculture but also sets a new benchmark in the development of sustainable farming practices. The findings from this research could have broad implications for the future of livestock management, potentially leading to widespread adoption of technology-driven health monitoring systems in agriculture. This would help in optimizing the health management of livestock globally, thereby enhancing productivity and sustainability in the agricultural sector.

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Animal Health Monitoring and Intrusion Detection System based on LORAWAN

Cited by 6 publications

References 5 publications

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

SMART REPELLER: A Smart system to prevent Rhesus Macaque Trespassing in Human Settlements and Agricultural Areas

Development of a Cloud-Based IoT System for Livestock Health Monitoring Using AWS and Python

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