An efficient in‐network caching decision algorithm for Internet of things

Abstract

Recently, content-centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named-based routing and in-network caching.However, IoT is characterized by challenging features: small storage capacity of resource-constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness.As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in-network caching decision algorithm that performs a caching strategy in CCN-IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache-first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).

Composition of caching and classification in edge computing based on quality optimization for SDN-based IoT healthcare solutions

“…[ 21 ] presents SD-RF, a replication management system for Content-Centric Networks based on SDN architecture, combining the benefits of SDN and CCN, [ 22 ] introduces RF-VS-SD-CCMN, a replication platform for video streaming in Content-Centric Mobile Networks that increases content distribution, perceived content quality, and network resource usage by considering user mobility and using offline and online methods for storage positioning and replica assignment. [ 23 ] proposes an efficient multifactorial function in the network cache decision algorithm that takes into account content size, hop counts, data latency, and node energy level, enhancing hit rate, reducing data recovery delay, and increasing information reusability in IoT settings. [ 24 ] studies caching strategies for IoT networks and shows that periodic storage reduces retrieval latency and improves cache performance, disseminating information faster and bridging the distance between data and customers.…”

“…DT-MAC is recommended for real-time applications requiring high packet delivery and response time. [ 23 ] proposes an efficient in-network caching decision algorithm for IoT, which utilizes similarity calculation to make optimal caching decisions. These studies provide guidance on selecting the best similarity measures for specific healthcare tasks and highlight the importance of choosing the right measure for accurate and effective analysis of IoT healthcare data.…”

Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

“…LFU [137] Hybrid Modified-LRU [81] LRFU [118] Content-based MDMR [66] Centrality-based NICE [75] Caching decision strategies Traditional CEE/LCE [73,163] LCD [162] Probabilistic Static Prob(p) [68,165] Dynamic BRR-CVR [42] MACD [50] pCASTING [68] CSCM [103] ProbCache [116] Vural et al [142] Topology/Centrality-based Betw/EgoBetw [41] BRR-CVR [42] Doan Van & Ai [50] PCS [100] CSCM [103] ProbCache [116] Shekhawat et al [132] Sun [135] Zeng & Hong [161] Zhang et al [164] LCC [170] BNC [172] Cooperative Explicit EM3C [76] Liu et al [92] PCS [100] Implicit…”

“…Li & Simon [89] Saha et al [126] TCCN [134] Zeng & Hong [161] Edge-only ELC [17] Edge Caching [55] Consumer-Cache [95] Freshness-based FDC [12] Doan Van & Ai [50] pCASTING [68] Quevedo et al [121] LCC [170] Popularity-based BRR-CVR [42] TCCN [134] Vural et al [142] Off-path…”

“…Such approaches may, for instance, use some combination of node battery level and cache occupancy in their calculations, along with some information about the incoming content, e.g. its freshness [50] or its popularity [42], whether the content is already cached in a neighbouring node [164], and/or topological information such as hop count [50,164] or the caching node's centrality [42,103].…”