MILP-driven Network Planning Framework for Energy Efficiency and Coverage Maximization in IoT Mesh Networks

TL;DR

This paper presents a MILP-based framework for optimizing IoT network deployment, combining static and mobile nodes to maximize coverage and minimize costs in resource-constrained environments.

Contribution

It introduces three novel MILP formulations for static placement, mobile path planning, and movement minimization, advancing cost-effective IoT network deployment strategies.

Findings

Boundary-optimized static placement achieves 53.06% coverage.

Mobile path planning reaches 97.95% coverage.

Movement minimization reduces traversal cost by 40%.

Abstract

In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized static placement (MILP-Static) achieves 53.06% coverage compared with 33.42% of the random approach. In addition, MILP-Cov for path planning reaches 97.95% coverage, while movement minimization (MILP-Mov) reduces traversal cost by 40%. Our proposed framework outperforms the benchmark approaches to provide a foundational solution for cost-effective global IoT deployment in resource constrained environments.