A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Proactive content caching in future generation communication networks: Energy and security considerations

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion. However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% in next generation networks. This thesis presents proactive content caching techniques to reduce grid energy consumption by utilizing renewable energy sources to power-up data storage units in helper nodes. The integration of renewable energy sources with proactive caching is a significant challenge due to the intermittent nature of renewable energy sources and investment costs. In this thesis, this challenge is tackled by introducing strategies to determine the optimal time of the day for content caching and optimal scheduling of caching nodes. The proposed strategies consider not only the availability of renewable energy but also temporal changes in network trac to reduce associated energy costs. While proactive caching can facilitate the reduction of peak trac load and the integration of renewable energy, cached content objects at helper nodes are often more vulnerable to malicious attacks due to less stringent security at edge nodes. Potential content leakage can lead to catastrophic consequences, particularly for cache-equipped Industrial Internet of Things (IIoT) applications. In this thesis, the concept of \trusted caching nodes (TCNs) is introduced. TCNs cache popular content objects and provide security services to connected links. The proposed study optimally allocates TCNs and selects the most suitable content forwarding paths. Furthermore, a caching strategy is designed for mobile edge computing systems to support IoT task offloading. The strategy optimally assigns security resources to offloaded tasks while satisfying their individual requirements. However, security measures often contribute to overheads in terms of both energy consumption and delay. Consequently, in this thesis, caching techniques have been designed to investigate the trade-off between energy consumption and probable security breaches. Overall, this thesis contributes to the current literature by simultaneously investigating energy and security aspects of caching systems whilst introducing solutions to relevant research problems

Techno-economic viability of integrating satellite communication in 4G networks to bridge the broadband digital divide

Bridging the broadband digital divide between urban and rural areas in Europe is one of the main targets of the Digital Agenda for Europe. Though many technological options are proposed in literature, satellite communication has been identified as the only possible solution for the most rural areas, due to its global coverage. However, deploying an end-to-end satellite solution might, in some cases, not be cost-effective. The aim of this study is to give insights into the economic effectiveness of integrating satellite communications into 4G networks in order to connect the most rural areas (also referred to as white areas) in Europe. To this end, this paper proposes a converged solution that combines satellite communication as a backhaul network with 4G as a fronthaul network to bring enhanced broadband connectivity to European rural areas, along with a techno-economic model to analyse the economic viability of this integration. The model is based on a Total Cost of Ownership (TCO) model for 5 years, taking into account both capital and operational expenditures, and aims to calculate the TCO as well as the Average Cost Per User (ACPU) for the studied scenarios. We evaluate the suggested model by simulating a hypothetical use case for two scenarios. The first scenario is based on a radio access network connecting to the 4G core network via a satellite link. Results for this scenario show high operational costs. In order to reduce these costs, we propose a second scenario, consisting of caching the popular content on the edge to reduce the traffic carried over the satellite link. This scenario demonstrates a significant operational cost decrease (more than 60%), which also means a significant ACPU decrease. We evaluate the robustness of the results by simulating for a range of population densities, hereby also providing an indication of the economic viability of our proposed solution across a wider range of areas