Adaptive k -coverage contour evaluation and deployment in wireless sensor networks

Abstract

The problem of coverage is a fundamental issue in wireless sensor networks. In this article, we consider two subproblems: k-coverage contour evaluation and k-coverage rate deployment. The former aims to evaluate, up to k, the coverage level of any location inside a monitored area, while the latter aims to determine the locations of a given set of sensors to guarantee the maximum increment of k-coverage rate when they are deployed into the area. For the k-coverage contour evaluation problem, a nonuniform-grid-based approach is proposed. We prove that the computation cost of our approach is at most the square root of existing solutions. Based on our k-coverage contour evaluation scheme, a greedy k-coverage rate deployment scheme (k-CRD) is proposed, which is shown to be an order faster than existing studies for k-coverage rate deployment. The k-CRD can incorporate two different heuristics to further reduce its running time. Simulation results show that k-CRD with these heuristics can be significantly more time efficient without causing much degradation in the coverage rate of final deployment.

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

“…Recent work has shown a spreading factor distribution scheme based on a certain range of distances to the GW. According to [ 22 , 39 ], the spreading allocation is primarily based on the power level of the signal that the GW receives from the devices and the GW sensitivity without taking the device location into account. Consequently, it is not trivial to consider a general coverage contour metric to determine the degree of coverage at any point within the IoT LoRa network while avoiding interference from obstacles.…”

“…It is critical to consider both the likelihood of the GW receiving an uplink from the source LD and the likelihood of the device successfully receiving a downlink. Energy consumption We considered the energy consumption among LoRa devices, which refers to the number of delivered data bits per unit of energy consumed by a LoRa device [ 39 ]. The SFPCR scheme aimed to balance the HSFZ’s energy efficiency by shortening the ToA of the LDs.…”

“…We considered the energy consumption among LoRa devices, which refers to the number of delivered data bits per unit of energy consumed by a LoRa device [ 39 ]. The SFPCR scheme aimed to balance the HSFZ’s energy efficiency by shortening the ToA of the LDs.…”