Reducing Operation Cost of LPWAN Roadside Sensors Using Cross Technology Communication

TL;DR

This paper introduces LOC-LPWAN, a framework that leverages cross-technology communication to reduce operation costs and improve performance of large-scale LPWAN roadside sensor networks by offloading data to passing vehicles.

Contribution

LOC-LPWAN is a novel optimization framework that reduces costs and enhances throughput, fairness, and delay performance in LPWAN roadside sensor networks using vehicle-based data relaying.

Findings

Cost reduced by 50% compared to baseline

Throughput increased by 72.6%

Fairness improved by 65.7%

Abstract

Low-Power Wide-Area Network (LPWAN) is an emerging communication standard for Internet of Things (IoT) that has strong potential to support connectivity of a large number of roadside sensors with an extremely long communication range. However, the high operation cost to manage such a large-scale roadside sensor network remains as a significant challenge. In this article, we propose Low Operation-Cost LPWAN (LOC-LPWAN), a novel optimization framework that is designed to reduce the operation cost using the cross-technology communication (CTC). LOC-LPWAN allows roadside sensors to offload sensor data to passing vehicles that in turn forward the data to a LPWAN server using CTC aiming to reduce the data subscription cost. LOC-LPWAN finds the optimal communication schedule between sensors and vehicles to maximize the throughput given an available budget. Furthermore, LOC-LPWAN optimizes the fairness among sensors by preventing certain sensors from dominating the channel for data transmission. LOC-LPWAN can also be configured to ensure that data packets are received within a specific time bound. Extensive numerical analysis performed with real-world taxi data consisting of 40 vehicles with 24-hour trajectories demonstrate that LOC-LPWAN reduces the cost by 50% compared with the baseline approach where no vehicle is used to relay packets. The results also show that LOC-LPWAN improves the throughput by 72.6%, enhances the fairness by 65.7%, and reduces the delay by 28.8% compared with a greedy algorithm given the same amount of budget.