Are Bus-Mounted Edge Servers Feasible?

TL;DR

The study investigates the feasibility of deploying edge servers on public buses to provide elastic, city-scale edge computing for vehicular networks. It leverages real Shanghai trace datasets to quantify bus and base-station coverage, and formulates a max-coverage problem solved by a greedy bus-selection algorithm. Trace-driven simulations demonstrate that bus-mounted edge servers can significantly improve spatiotemporal coverage and complement fixed infrastructure, especially during peak demand. Overall, the work shows that mobile buses can offer meaningful elasticity and coverage in urban edge computing for IoV.

Abstract

Placement of edge servers is the prerequisite of provisioning edge computing services for Internet of Vehicles (IoV). Fixed-site edge servers at Road Side Units (RSUs) or base stations are able to offer basic service coverage for end users, i.e., vehicles on road. However, the server locations and capacity are fixed after deployment, rendering their inefficiency in handling spationtemporal user dynamics. Mobile servers such as buses, on the other hand, have the potential of adding computation elasticity to such system. To this end, this paper studies the feasibility of bus-mounted edge servers based on real traces. First, we investigate the coverage of the buses and base stations using the Shanghai bus/taxi/Telecom datasets, which shows a great potential of bus-based edge servers as they cover a great portion of geographic area and demand points. Next, we build a mathematical model and design a simple greedy heuristic algorithm to select a limited number of buses that maximizes the coverage of demand points, i.e., with a limited purchase budget. We perform trace-driven simulations to verify the performance of the proposed bus selection algorithm. The results show that our approach effectively handles the dynamic user demand under realistic constraints such as server capacity and purchase quantity. Thus, we claim: bus-mounted edge servers for vehicular networks in urban areas are feasible, beneficial, and valuable.

Figures (6)

• Figure 1: Different Coverage of Potential Edge Servers.
• Figure 2: Coverage of Base Stations.
• Figure 3: Coverage of Buses.
• Figure 4: Coverage with Cap under Different Algorithms.
• Figure 5: Coverage under Different Caps.