A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization

Abstract

Edge caching is being explored as a promising technology to alleviate the network burden of cellular networks by separating the computing functionalities away from cellular base stations. However, the service capability of existing caching scheme is limited by fixed edge infrastructure when facing the uncertainties of users' requests and locations. The vehicular caching, which uses the moving vehicles as cache carriers, is regard as an efficient method to solve the problem above. This paper studies the effectiveness of vehicular caching scheme in content centric networks by developing optimization model towards the minimization of network energy consumption. Particularly, we model the interactions between caching vehicles and mobile users as a 2-D Markov process, in order to characterize the network availability of mobile users. Based on the developed model, we propose an online vehicular caching design by optimizing network energy efficiency. Specifically, the problem of caching decision making is firstly formulated as a fractional optimization model, towards the optimal energy efficiency. Using nonlinear fractional programming technology and Lyapunov optimization theory, we derive the theoretical solution for the optimization model. An online caching algorithm to enable the optimal vehicular caching is developed based on the solution. Finally, extensive simulations are conducted to examine the performance of our proposal. By comparison, our online caching scheme outperforms the existing scheme in terms of energy efficiency, hit ratio, cache utilization, and system gain.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

“…Mobility-aware caching in conventional device-to-device networks has been well studied, e.g., [83], [84], [85], and such methods have recently been extended to vehicular networks. A new type of caching services was explored in [86], [87], where the content cached in vehicles could be requested by moving or static users within the communication range. In this scenario, the relationship between caching vehicles and moving users was the key to design the caching policy.…”

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

“…and applications [4]. According to the forecast report from Cisco, the global monthly mobile data usage in 2021 will be approximately 49 exabytes, and the number of mobile devices will be 11.6 billion, increasing about approximately seven times between 2016 and 2021 [5]. With the explosion of mobile data, mobile phones are increasingly being used for various computation-intensive applications, such as augmented reality; natural language processing; face, hand gestures, and object recognition; and various forms of user configurations used for recommendation [6]; hence, mobile users enjoy a rich experience in the service network.…”

“…However, the limited network bandwidth in traditional cellular networks limits the fast growth of the data transmission rate. In the emerging 5G network, the application of D2D (device to device) communication technology promises to substantially improve the spectrum efficiency to support data transmission between caching vehicles and mobile users [5]. The federal communications commission (FCC) authorized the 75-mhz band for the provision of vehicle-to-vehicle wireless communications as dedicated short-range communications (DSRC).…”

Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing System

“…When considering the actual communication and run-time procedure, such settings will cause heavy communication burden and even infeasibility because the central node needs to collect global information and accurately predict changes in the time-varying network environment. Dividing the time domain into several time slots can solve this problem to a certain extent, Zhang et al [22] proposed the use of a Markov chain to divide the time domain into several time slots, and to predict the network environment then use the centralized algorithm. Sun et al [23] applied Lyapunov and difference-of-convex (DC) technique to decompose the long-term optimization problem into a series of one-slot problems.…”