Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

Analysis of Spatial Data Structures for Proximity Detection

Construction is a dangerous business. According to statistics, in every of the past thirteen years more than 1000 workers died in the USA construction industry. In order to minimize the overall number of these incidents, the research presented in this paper investigates to monitor and analyze the trajectories of construction resources first in a simulated environment and later on the actual job site. Due to the complex nature of the construction environment, three dimensional (3D) positioning data of workers is hardly collected. Although technology is available that allows tracking construction assets in real-time, indoors and outdoors, in 3D, at the same time, the continuously changing spatial and temporal arrangement of job sites requires any successfully working data processing system to work in real-time. This research paper focuses is safety on spatial data structures that offer the capability of realigning itself and reporting the distance of the closest neighbor in real-time. This paper presents results to simulations that allow the processing of real-time location data for collision detection and proximity analysis. The presented data structures and perform-ance results to the developed algorithms demonstrate that real-time tracking and proximity detection of resources is feasible.

Private Proximity Detection for Convex Polygons

Proximity detection is an emerging technology in Geo-Social Networks that notifies mobile users when they are in proximity. Nevertheless, users may be unwilling to participate in such applications if they are required to disclose their exact locations to a centralized server and/or their social friends. To this end, private proximity detection protocols allow any two parties to test for proximity while maintaining their locations secret. In particular, a private proximity detection query returns only a boolean result to the querier and, in addition, it guarantees that no party can derive any information regarding the other party＇s location. However, most of the existing protocols rely on simple grid decompositions of the space and assume that two users are in proximity when they are located inside the same grid cell. In this paper, we extend the notion of private proximity detection, and propose a novel approach that allows a mobile user to define an arbitrary convex polygon on the map and test whether his friends are located therein. Our solution employs a secure two-party computation protocol and is provably secure. We implemented our method on handheld devices and illustrate its efficiency in terms of both computational and communication costs.