Flock Management through IoT and Multi-Metric Routing in Agricultural Environments

Flock management is a crucial part of the agriculture industry, particularly in livestock safety, reducing economic losses, and enhancing resource utilization. This paper models that each sheep in the flock is given a wearable IoT device that can track its location and communicate with the others. It is also assigned to specific areas for grazing. If any of the tracking sheep goes beyond its designated area, the farmer gets notified. So that the farmer can be notified of any possible dangers, this paper presents a novel algorithm, which is a latency-aware objective function. The default metrics, like hop count or ETX, use RPL objective functions like OF0 and MRHOF, however, overlook latency, mobility, and energy constraints, making them inappropriate for dynamic and mobility-driven agricultural environments. This proposed model uses four key metrics, like latency, link quality, residual energy, and local congestion, to offer a balanced routing to the needs of wearable flock IoT systems. The Cooja simulator is used to run simulations with the proposed algorithm. There is a minimum variation of 10% in the efficiency in terms of PDR and energy consumption of the proposed algorithm compared to the already existing algorithms, which are OF-EC and AHP-OF. This study is an application of implementing IoT in the agriculture sector for tracking and securing the flocks. The proposed method is developed to perform well outdoors and instantly offer monitoring data. The best part of this work is integrating domain-specific constraints, such as mobility models and power-aware routing, into IoT systems for rural real-world livestock movement. Moreover, improves the efficiency of transmitting the packets, aims to lessen the losses in the sheep population, and enhances resource management.