Privacy-by-Design Distributed Offloading for Vehicular Edge Computing

Abstract

Vehicular Edge Computing (VEC) is a distributed computing paradigm that utilizes smart vehicles (SVs) as computational cloudlets (edge nodes) by virtue of their inherent attributes such as mobility, low operating costs, flexible deployment, and wireless communication ability. VEC extends edge computing services by expanding computing coverage and further improving quality-of-services (QoS) for devices. Due to limited onboard energy and computation capabilities of SV-mounted cloudlets, a single vehicle might not be able to execute a large number of tasks and guarantee their desired QoS. To address this problem, the overloaded vehicle can fulfill its overwhelming workload by offloading its tasks to other available connected vehicles. However, data privacy and accessibility are of critical importance that need to be considered for offloading. In this paper, we propose privacy-by-design offloading solutions for VEC to facilitate latency requirements of user demands and reduce energy consumption of vehicles.We formulate the Data pRotection Offloading Problem (DROP) as an Integer Program and prove its NP-hardness. To provide computationally tractable solutions, we propose three distributed algorithms by leveraging graph theory to solve this problem. We evaluate the performance of our proposed algorithms by extensive experiments and compare them to the optimal results obtained by IBM ILOG CPLEX. The results demonstrate the flexibility, scalability, and cost efficiency of our proposed algorithms in providing practical privacy-by-design offloading solutions enabling edge services along the cloud-to-thing continuum.