The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users' future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users' future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users' next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users' future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.

Fog computing has risen as a promising technology for augmenting the computational and storage capability of the end devices and edge networks. The urging issues in this networking paradigm are fog nodes planning, resources allocation, and offloading strategies. This paper aims to formulate a mathematical model which jointly tackles these issues. The goal of the model is to optimize the tradeoff (Pareto front) between the capital expenditure and the network delay. To solve this multi-objective optimization problem and obtain benchmark values, we first use the weighted sum method and two existing evolutionary algorithms, NSGA-II (Non-dominated Sorting Genetic Algorithm II) and Speed-Constrained Multi-Objective Particle Swarm Optimization (SMPSO). Then, inspired by those evolutionary algorithms, the paper proposes a new evolutionary algorithm, named Particle Swarm Optimized Non-dominated Sorting Genetic Algorithm (PSONSGA), which combines the convergence and searching efficiency of the existing evolutionary algorithms. The effectiveness of the proposed algorithm is evaluated by the HyperVolume (HV) and Inverted Generational Distance (IGD) indicators. The performance evaluation results show that the proposed model and algorithms can help the network planners in the deployment of fog networks to complement their existing computation and storage infrastructure.

This paper proposes an exact model for the planning and design problem of fog networks. More precisely, a mathematical model is proposed to simultaneously determine the optimal location, the capacity and the number of fog node(s) as well as the interconnection between the installed fog nodes and the cloud. The goal of the model is to minimize the delay in the network and the amount of traffic sent to the cloud data center. To address this multi-objective optimization problem, three optimization techniques are used: the weighted sum, the hierarchical and the trade-off methods. The weighted sum method aggregates all the lone objective functions into a single objective by applying a weighted vector. The hierarchical method takes a sequential approach by tightly constraining the more important objective function. The trade-off method solves a single objective function and translates all other objective functions into constraints. These methods are then compared in terms of average delay, amount of traffic sent to the cloud and amount of CPU time required to find optimal solution(s). Since we are dealing with a multi-objective optimization problem and that multiple optimal solutions can be found, the fuzzy-based mechanism and the hypervolume indicator have been used. Computational results show that as the problem size increases, the delay and the traffic also increase in a linear form; whereas, the solution time increases in non-polynomial time. The weighted sum method was able to achieve the best trade-off results for the delay and the traffic, whereas the hierarchical method was able to return minimum delay but with worse traffic going to the cloud. As the model considers realistic edge device traffic parameters, constraints, and various topology aspects, it can be helpful for the planning and deployment of fog networks and how they operate within a cloud infrastructure.