Cost-Efficient Vehicular Edge Computing Deployment for Mobile Air Pollution Monitoring

Abstract

Vehicular Edge Computing (VEC) emerges as a rem-edy to achieve flexible and fine-grained air pollution monitoring, where vehicles equipped with onboard sensors can sense, process, calibrate and store air pollutants on the drive, and roadside units (RSUs) can be deployed for vehicles to offload data via low-cost vehicle-to-RSU (V2R) communication. However, existing VEC-based air pollution monitoring solution either suffers from high deployment cost, limited V2R communication distance, or degraded data collection latency. To address these challenges, we propose a novel cost-efficient VEC deployment solution for mobile air pollution monitoring, where a set of buses are used to monitor the air pollutants, and selected bus stations are equipped with RSU s for offloading the collected data, considering the effective communication distance and power consumption of V2R. To jointly minimize the VEC deployment cost and data collection latency, we build a multi-objective problem formulation under the constraints of resource, latency, etc. Then we propose a Two-stage Cost-efficient VEC Deployment (TCVD) algorithm based on two heuristic strategies, i.e., the near-equivalence point deployment strategy and the conditioned RSU deployment strategy, with a theoretically-proved worst-case bound. Through extensive evaluations on an open data set of Dublin bus, we verify that TCVD not only reduces the data collection latency by 25.04%, but also reduces the total VEC deployment cost by 30.81 % as compared with existing schemes.