🤖 AI Summary
To address insufficient coverage and resource wastage in wireless sensor networks (WSNs) for smart agriculture, this paper proposes a gradient-descent–Lagrangian-multiplier joint optimization method for sensor node deployment. Leveraging grid-based modeling, the approach integrates gradient descent with the Lagrangian multiplier method to simultaneously maximize coverage, minimize node count, and jointly optimize energy consumption and deployment cost. A novel Bluetooth-based short-range extension mechanism is introduced to enhance scalability. Experimental results demonstrate 98.5% coverage—3.5 percentage points higher than particle swarm optimization—while significantly reducing node count, deployment cost, and energy consumption compared to classical deterministic methods. This work establishes the first multi-objective joint optimization framework specifically tailored for agricultural WSNs, uniquely balancing practical applicability with theoretical rigor.
📝 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.