Design and analysis of network coding schemes for efficient fronthaul offloading of fog-radio access networks

Abstract

In the era of the Internet of Things (IoT), everything will be connected. Smart homes and cities, connected cars, smart agriculture, wearable technologies, smart healthcare, smart sport, and fitness are all becoming a reality. However, the current cloud architecture cannot manage the tremendous number of connected devices and skyrocketing data traffic while providing the speeds promised by 5G and beyond. Centralised cloud data centres are physically too far from where the data originate (edge of the network), inevitably leading to data transmission speeds that are too slow for delay-sensitive applications. Thus, researchers have proposed fog architecture as a solution to the ever-increasing number of connected devices and data traffic. The main idea of fog architecture is to bring content physically closer to end users, thus reducing data transmission times. This thesis considers a type of fog architecture in which smart end devices have storage and processing capabilities and can communicate and collaborate with each other. The major goal of this thesis is to develop methods of efficiently governing communication and collaboration between smart end devices so that their requests to upper network layers are minimised. This is achieved by incorporating principles from graph theory, network coding and machine learning to model the problem and design efficient network-coded scheduling algorithms to further enhance achieved performance. By maximising end users' self-sufficiency, the load on the system is decreased and its capacity increased. This will allow the central processing unit to manage more devices which is vital, given that more than 29 billion devices will connect to the infrastructure by 2023 \cite{Cisco1823}. Specifically, given that the limitations of the smart end devices and the system in general lead to various communication conflicts, a novel network coding graph is developed that takes into account all possible conflicts and enables the search for an efficient feasible solution. The thesis designs heuristic algorithms that search for the solution over the novel network coding graph, investigates the complexity of the proposed algorithms, and shows the offloading strategy's asymptotic optimality. Although the main aim of this work is to decrease the involvement of upper fog layers in serving smart end devices, it also takes into account how much energy end devices would use during collaborations. Unfortunately, a higher system capacity comes at the price of more energy spent by smart end devices; thus, service providers' interests and end users' interests are conflicting. Finally, this thesis investigates how multihop communication between end devices influences the offloading of upper fog layers. Smart end devices are equipped with machine learning capabilities that allow them to find efficient paths to their peers, further improving offloading. In conclusion, the work in this thesis shows that by smartly designing and scheduling communication between end devices, it is possible to significantly reduce the load on the system, increase its capacity and achieve fast transmissions between end devices, allowing them to run latency-critical applications