Improved Coverage and Connectivity via Weighted Node Deployment in Solar Insecticidal Lamp Internet of Things

Abstract

As an important physical control technology, Solar Insecticidal Lamp (SIL) can effectively prevent and control the occurrence of pests. The combination of SILs and Wireless Sensor Networks (WSNs) initiates a novel agricultural Internet of Things (IoT), i.e., SIL-IoTs, to simultaneously kill pests and transmit pest information. In this paper, we study the weighted SIL Deployment Problem (wSILDP) in SIL-IoTs, where weighted locations on ridges are prespecified and some of them are selected to deploy SILs. Different from the existing studies whose optimization objective is to minimise the deployment cost, we consider the deployment cost and the total weight of selected locations jointly. We formulate the wSILDP as the Weighted Set Cover (WSC) problem and propose a Layered Deployment Method based on Greedy Algorithm (LDMGA) to solve the defined optimization problem. The LDMGA is composed of two phases. Firstly, SILs are deployed layer by layer from the boundary to the centre until the entire farmland is completely covered. Secondly, on the basis of three design operations, i.e., substitution, deletion and fusion, the suboptimal locations obtained in the first phase are fine-tuned to achieve the minimum deployment cost together with the maximum total weight for meeting the coverage and connectivity requirements. Simulation results clearly demonstrate that the proposed method outperforms three peer algorithms in terms of deployment cost and total weight. Index Terms-Solar insecticidal lamp internet of things (SIL-IoTs), weighted solar insecticidal lamp deployment problem (wSILDP), weighted set cover (WSC) problem, restricted deployment locations, approximation algorithm, wireless sensor network (WSN).

SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoT With Improved Energy Efficiency

“…The proposed deployment strategy significantly reduces the number of nodes deployed while ensuring full coverage and use in complex agricultural environments. In 2021, weighted deployment [6] and hole-aware node deployment strategies [7] were proposed to improve the connectivity of SIL-IoTs and address the mixed-crop farmland problem. • Adaptive energy management strategy to optimize the energy utilization of SIL-IoTs: Keeping the lamp on to attract pests and releasing high voltage pulse currents to kill pests occupy major energy consumption of a SIL-IoTs node, in which consumed energy comes from the residual energy of the battery and the energy harvesting of the solar panel.…”

Target-Barrier Coverage Improvement in an Insecticidal Lamps Internet of UAVs

“…Furthermore, based on the integration of wireless radio-frequency modules into existing ILs, the concept of IoT-based solar insecticidal lamps (ILs-IoTs) is introduced. In the existing works [4], [39], [40], the authors proposed some methods to deploy the ILs-IoTs node on the ground. It is pointed out that these ILs-IoTs cannot provide a perfect solution to migratory pests with time-varying locations since all ILs-IoTs nodes are fixed on the ground.…”

“…A. Probabilistic sensing model Most existing works [4], [39], [40] adopted the boolean sensing model of a circular region for sensors, where a sensor can detect any event occurring at any point within its sensing region at probability 1 but it cannot detect anything outside the region. However, the boolean sensing model is an ideal model for the insecticidal lamp.…”

Decode-and-Forward Short-Packet Relaying in the Internet of Things: Timely Status Updates

“…The majority of IoT infrastructures are deployed in rich scattering environments, for a wide range of applications in agriculture, industry, medicine, and allied sectors [1], [2]. In a non-line-of-sight scenario, such as a farm full of crops, a heavily built-up urban area, or a crowded corridor, Rayleigh fading is the most applicable model for the radio-frequency signal propagation [41], where the channel coefficient from a transmitter to a receiver is well-modelled as a random variable following a circularly-symmetric complex Gaussian distribu-tion and, hence, the magnitude of the channel coefficient is Rayleigh distributed [42].…”