Optimizing Sensor Node Localization for Achieving Sustainable Smart Agriculture System Connectivity

TL;DR

This paper presents a gradient-based optimization method for sensor deployment in smart agriculture, significantly improving coverage and reducing costs while ensuring scalability and high coverage efficiency.

Contribution

It introduces a novel sensor allocation algorithm that outperforms traditional methods in coverage, cost, and power efficiency for smart agriculture networks.

Findings

Achieved 98.5% sensor coverage with the proposed method.

Outperformed particle swarm distribution in coverage efficiency.

Enhanced scalability using Bluetooth communication.

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.