Flexible Cache-Aided Networks with Backhauling

Caching at the edge is a promising technique to cope with the increasing data demand in wireless networks. This paper analyzes the performance of cellular networks consisting of a tier macro-cell wireless backhaul nodes overlaid with a tier of cache-aided small cells. We consider both static and dynamic association policies for content delivery to the user terminals and analyze their performance. In particular, we derive closed-form expressions for the area spectral efficiency and the energy efficiency, which are used to optimize relevant design parameters such as the density of cache-aided small cells and the storage size. By means of this approach, we are able to draw useful design insights for the deployment of highly performing cache-aided tiered networks.Comment: 5 pages, 5 figures, to be presented at 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC'2017), Sapporo, Japan, 201

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