Smart Collaborative Caching for Information-Centric IoT in Fog Computing

Abstract

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.

Two-Stage Interference Cancellation for Device-to-Device Caching Networks

“…A comprehensive survey on the state-of-the-art cache-based transport protocols in wireless sensor networks such as cache insertion/replacement policy, cache size requirement, cache location, cache partition, and cache decision was provided in [16]. Recently, caching strategies to support various IoT applications were studied [17][18][19][20]. It was shown in [17] that in-network caching of IoT data at content routers significantly reduces network load by caching highly requested IoT data.…”

“…Energy-harvesting-based IoT sensing has been studied in [18], which analyzes the trade-off between energy consumption and caching. In [19], a cooperative caching scheme that is able to utilize resource pooling, content storing, node locating, and other related situations was proposed for computing in IoT networks. In [20], an energy-aware dynamic resource caching strategy to enable a broker to cache popular resources was proposed to maximize the energy savings from servers in smart city applications.…”

Reliability Analysis for Multipath Communications in Mobile Cloud Computing Architectures

“…Recently a new computing paradigm, known as fog computing and further mobile fog computing, has been proposed as an improvement to the cloud computing. Fog computing expands the cloud services to the edge of cloud networks and makes computation, communication, and storage closer to edge devices and end-users [7]. In the research surveys [8][9][10], the authors overview and survey fog computing model architectures, key technologies, and applications.…”

Optimal Content Caching in Content-Centric Networks

“…A classification of CCN and some important related challenges are presented in [3]. CCNs are designed to integrate with other application domains, e.g., edge computing, vehicular networks, Internet of Things (IoT), which can play supportive roles to augment CCNs in their functionality [4][5][6]. For instance, Li et al proposed fogenabled edge learning for cognitive CCN [4].…”

“…The authors proposed an architecture that employs fog computing to elevate the cognitive ability of the CCN in order to realize context-awareness, timely response, and traffic offloading for 5G applications. In [5], the authors proposed a collaborative caching scheme to enable information-centric ability in IoT, which is beneficial for better connecting the devices and providing cooperative caching in fog computing. The authors in [6] proposed an architecture that combines Mobile Edge Computing with CCN in the context of connected vehicle environments, to achieve less response time for resource intensive services in fast changing networks.…”