Study and analysis of mobility, security, and caching issues in CCN

Existing architecture of Internet is IP-centric, having capability to cope with the needs of the Internet users. Due to the recent advancements and emerging technologies, a need to have ubiquitous connectivity has become the primary focus. Increasing demands for location-independent content raised the requirement of a new architecture and hence it became a research challenge. Content Centric Networking (CCN) paradigm emerges as an alternative to IP-centric model and is based on name-based forwarding and in-network data caching. It is likely to address certain challenges that have not been solved by IP-based protocols in wireless networks. Three important factors that require significant research related to CCN are mobility, security, and caching. While a number of studies have been conducted on CCN and its proposed technologies, none of the studies target all three significant research directions in a single article, to the best of our knowledge. This paper is an attempt to discuss the three factors together within context of each other. In this paper, we discuss and analyze basics of CCN principles with distributed properties of caching, mobility, and secure access control. Different comparisons are made to examine the strengths and weaknesses of each aforementioned aspect in detail. The final discussion aims to identify the open research challenges and some future trends for CCN deployment on a large scale

Efficient Traffic Management Algorithms for the Core Network using Device-to-Device Communication and Edge Caching

Exponentially growing number of communicating devices and the need for faster, more reliable and secure communication are becoming major challenges for current mobile communication architecture. More number of connected devices means more bandwidth and a need for higher Quality of Service (QoS) requirements, which bring new challenges in terms of resource and traffic management. Traffic offload to the edge has been introduced to tackle this demand-explosion that let the core network offload some of the contents to the edge to reduce the traffic congestion. Device-to-Device (D2D) communication and edge caching, has been proposed as promising solutions for offloading data. D2D communication refers to the communication infrastructure where the users in proximity communicate with each other directly. D2D communication improves overall spectral efficiency, however, it introduces additional interference in the system. To enable D2D communication, efficient resource allocation must be introduced in order to minimize the interference in the system and this benefits the system in terms of bandwidth efficiency. In the first part of this thesis, low complexity resource allocation algorithm using stable matching is proposed to optimally assign appropriate uplink resources to the devices in order to minimize interference among D2D and cellular users. Edge caching has recently been introduced as a modification of the caching scheme in the core network, which enables a cellular Base Station (BS) to keep copies of the contents in order to better serve users and enhance Quality of Experience (QoE). However, enabling BSs to cache data on the edge of the network brings new challenges especially on deciding on which and how the contents should be cached. Since users in the same cell may share similar content-needs, we can exploit this temporal-spatial correlation in the favor of caching system which is referred to local content popularity. Content popularity is the most important factor in the caching scheme which helps the BSs to cache appropriate data in order to serve the users more efficiently. In the edge caching scheme, the BS does not know the users request-pattern in advance. To overcome this bottleneck, a content popularity prediction using Markov Decision Process (MDP) is proposed in the second part of this thesis to let the BS know which data should be cached in each time-slot. By using the proposed scheme, core network access request can be significantly reduced and it works better than caching based on historical data in both stable and unstable content popularity