A Trajectory-Inspired Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things Under Coverage and Maintenance Cost Considerations

Abstract

As a special type of node, solar insecticidal lamps (SILs) require regular maintenance to ensure effective insecticidal performance and accurate collection of pest information. While hiring professionals for management and maintenance is a viable solution, it comes with the drawback of high maintenance costs. An effective approach to reducing these costs is deploying SILs along routes frequently traversed by agricultural workers (AWs), as these tasks can be easily incorporated into their routine. Therefore, inspired by the trajectory of high-density AWs, this article focuses on studying the constrained SIL Deployment Problem under coverage and maintenance cost considerations, referred to as cSILDP-CMC. In this problem, SIL nodes are deployed at a limited set of weighted candidate locations (CLs) situated on the ridges. The objective of cSILDP-CMC is to select a subset of CLs for SIL placement, maximizing coverage while keeping the total maintenance cost within the allocated budget. To begin, we propose a method for quantifying the maintenance cost of each CL and assign a weight to them accordingly. Subsequently, we formulate cSILDP-CMC as a budgeted maximum coverage problem and prove that it is NP-Hardness. We then introduce a two-phase algorithm (TPA) as an approximation algorithm to address the defined optimization problem. Finally, to assess the effectiveness of our design, we conduct theoretical analysis of TPA and perform extensive simulations. The simulation results clearly demonstrate that TPA outperforms three other algorithms in terms of coverage ratio. It achieves a minimum coverage ratio increase of 2% while maintaining the same fixed maintenance cost. Furthermore, TPA also stands out in terms of maintenance costs by reducing them at least 3.9% while maintaining a comparable coverage level.