In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey

被引:16
|
作者
Zhang, Zhe [1 ]
Lung, Chung-Horng [2 ]
Wei, Xin [1 ]
Chen, Mingkai [1 ]
Chatterjee, Subhajit [3 ]
Zhang, Zhicai [4 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Commun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China
[2] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
[3] Ericsson, Serv Assurance, Kolkata 700135, India
[4] Shanxi Univ, Sch Phys & Elect Engn, Taiyuan 030006, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Caching decision policy; caching replacement policy; Information-centric networking-based Internet of Things (ICN-IoT); machine learning (ML); INFORMATION-CENTRIC NETWORKING; NAMED DATA NETWORKING; POPULARITY PREDICTION; CONTENT PLACEMENT; TRANSIENT DATA; NAMING SCHEME; INTERNET; EDGE; THINGS; STRATEGY;
D O I
10.1109/JIOT.2023.3274653
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Things (IoT) has already emerged as one of the most popular directions in today's information and communication technology (ICT) domain. With its advancement over different application areas, such as smart home, smart healthcare, industry 4.0, etc., a huge amount of data has been generated by billions of IoT devices, which aggravates the shortcomings of the network layer (IP)-based networks, such as limited expressiveness of IP addressing, inefficient support for mobility, and in-network caching. Building IoT on top of information-centric networking (ICN) is believed to be a promising solution to tackle the above challenge, especially the in-network caching of ICN can significantly benefit IoT in terms of reducing data and saving IoT devices' energy. However, caching IoT data is more challenging than caching traditional Internet content, e.g., video, because IoT data are usually valid within a certain period of time, and IoT devices are typically constrained with battery. Hence, in this survey, we first review the current implementation proposals of ICN-based IoT (ICN-IoT). Next, we present the conventional caching decision policies and replacement policies which could be adopted to mitigate the aforementioned challenges, e.g., reducing IoT traffic, saving energy, and reducing data retrieval latency. Further, since leveraging machine learning (ML) techniques have the potential to further improve the caching efficiency by dealing with uncertainties, e.g., predicting unknown information, adaptively interacting with the environment, we also demonstrate the recently proposed ML-based caching schemes for ICN-IoT. In addition, we outline the open research issues and point out the future opportunities of caching in ICN-IoT.
引用
收藏
页码:14595 / 14620
页数:26
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