Mobile Edge Computing-Enhanced Proximity Detection in Time-Aware Road Networks

Abstract

Given a set of moving objects as well as their friend relationships, a time-aware road network, and a time threshold per friend pair, the proximity detection problem in time-aware road networks is to find each pair of moving objects such that the time distance (defined as the shortest time needed for two moving objects to meet each other) between them is within the given threshold. The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency, real time proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, (ii) the solutions for road networks focus on static road networks and do not consider time distance and thus cannot be applied in time-aware road networks, and (iii) there are no works aiming to simultaneously reduce the communication cost, the communication latency, and computational cost. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) with the purpose of achieving low communication latency, then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost, and subsequently propose server-side computational cost optimization techniques to reduce the computational cost. Experimental results show that our MEC enhanced proximity detection architecture, our proximity detection method, and the server-side computational cost optimization techniques can reduce the communication latency, the communication cost, and the computational cost effectively. INDEX TERMS Cost optimization, low latency, mobile edge computing, proximity detection, time-aware road networks, time distance.

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

“…For IoT systems, it is vital to have reliable hardware and to predict a user's position in the area with high accuracy in order to be able to differentiate between individuals in a small space. Proximity detection in conjunction with Bayesian filtering aims to perform high accuracy positioning [93,94]. Another example of high-resolution beacon-based proximity detection for dense areas is described in [95].…”

“…Edge computing [93], as a concept, provides local computation and aggregation, and therefore such a paradigm is used to minimize response time and save the bandwidth.…”

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

“…NN queries retrieve points of interest (POI), such as taxis and restaurants, closest to a query user [ 1 , 2 ], and RN queries retrieve POIs within a query distance [ 3 , 4 , 5 ]. Typically, location-based services (LBS), such as taxi-booking and ride-sharing services, use real-time spatial data to locate POIs close to the query user [ 6 , 7 , 8 , 9 , 10 ]. When multiple SP queries reach an LBS server simultaneously at peak times, if the SP queries are processed sequentially, it may not be possible to provide prompt responses to the query users.…”