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Optimizing Sensor Node Localization for Achieving Sustainable Smart Agriculture System Connectivity
arXiv:2512.14971v1 Announce Type: new
Abstract: The innovative agriculture system is revolutionizing how we farm, making it one of the most critical innovations of our time! Yet it faces significant connectivity challenges, particularly with the sensors that power this technology. An efficient sensor deployment solution is still required to maximize the network's detection capabilities and efficiency while minimizing resource consumption and operational costs. This paper introduces an innovative sensor allocation optimization method that employs a Gradient-Based Iteration with Lagrange. The proposed method enhances coverage by utilizing a hybrid approach while minimizing the number of sensor nodes required under grid-based allocation. The proposed sensor distribution outperformed the classic deterministic deployment across coverage, number of sensors, cost, and power consumption. Furthermore, scalability is enhanced by extending sensing coverage to the remaining area via Bluetooth, which has a shorter communication range. Moreover, the proposed algorithm achieved 98.5% wireless sensor coverage, compared with 95% for the particle swarm distribution.
Abstract: The innovative agriculture system is revolutionizing how we farm, making it one of the most critical innovations of our time! Yet it faces significant connectivity challenges, particularly with the sensors that power this technology. An efficient sensor deployment solution is still required to maximize the network's detection capabilities and efficiency while minimizing resource consumption and operational costs. This paper introduces an innovative sensor allocation optimization method that employs a Gradient-Based Iteration with Lagrange. The proposed method enhances coverage by utilizing a hybrid approach while minimizing the number of sensor nodes required under grid-based allocation. The proposed sensor distribution outperformed the classic deterministic deployment across coverage, number of sensors, cost, and power consumption. Furthermore, scalability is enhanced by extending sensing coverage to the remaining area via Bluetooth, which has a shorter communication range. Moreover, the proposed algorithm achieved 98.5% wireless sensor coverage, compared with 95% for the particle swarm distribution.
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