Edge Nodes Placement in 5G enabled Urban Vehicular Networks: A Centrality-based Approach

Abstract

The next generation vehicular applications in smart cities, including aided self-driving, require intricate data processing and quick message exchanges. A pragmatic approach to address these requirements is to adopt the edge-computing paradigm from 5G architecture, where storage, computing, and networking resources are brought to the edge of the network, i.e., closer to the end-users. Edge nodes (EN) are geographically overlaid across a region, and therefore, the effectiveness of the vehicular applications is directly associated with the proper placement of such nodes. However, the deployment of edge nodes on the roadsides presents a challenge of cost-effectiveness. In this paper, we address the efficient deployment of a limited number of edge nodes in an urban scenario under a restricted budget. To this end, we jointly consider the structural properties of the road network using complex-network based centrality metrics and the vehicular traffic distribution to rank the candidate sites for edge node placement. Thereafter, we formulate the problem of edge node deployment as a 0–1 knapsack problem, which is a classical NP problem and provide a dynamic programming based solution to it. We evaluate the proposed method in an urban scenario with real traffic and present conclusive proof that our proposed scheme yields a practical solution to the defined problem.