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.

The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users' future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users' future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users' next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users' future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.

As the rpl became the standard for routing in the iot networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in rpl’s standard, mainly because there was no implementation for these features in any iot operating systems yet. A partial implementation of rpl’s security mechanisms was presented recently for the Contiki operating system (by Perazzo et al.), which provided us with an opportunity to examine rpl’s security mechanisms. In this paper, we investigate the effects and challenges of using rpl’s security mechanisms under common routing attacks. First, a comparison of rpl’s performance, with and without its security mechanisms, under four routing attacks (Blackhole, Selective-Forward, Neighbor, and Wormhole attacks) is conducted using several metrics (e.g., average data packet delivery rate, average data packet latency, average power consumption, etc.). This comparison is performed using two commonly used Radio Duty-Cycle protocols. Secondly, and based on the observations from this comparison, we propose two techniques that could reduce the effects of such attacks, without having added security mechanisms for rpl. An evaluation of these techniques shows improved performance of rpl under the investigated attacks, except for the Wormhole attack.

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep $Q$ -networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).

The considerable increase of multimedia services, such as video-on-demand (VoD) services, is a significant contributor to the total Internet traffic. Software-defined networking (SDN) and information-centric networking (ICN) are two promising technologies that can be combined to facilitate video delivery and to reduce network delays. In this paper, we first formulate the caching decision problem as a 0-1 integer linear programming (ILP) problem. Second, in contrast to existing approaches that solve the formulated ILP problem by assuming all future video requests are known, we consider the impact of the time scale, which transforms the static 0-1 ILP problem into a dynamic problem. By solving the dynamic 0-1 ILP problem, we find more accurate optimal solutions compared to existing approaches. Third, since the formulated 0-1 dynamic ILP problem is NP-hard, we leverage the in-network caching of ICN and the global view of the SDN controller to propose a novel SDN-based caching decision policy. Finally, extensive evaluations are performed, and the results demonstrate that the proposed SDNbased caching decision policy provides solutions that are close to the optimum in substantially less computation time. The SDNbased caching decision policy also outperforms existing practical ICN caching decision policies in terms of the cache hit ratio and the average number of hops, which are directly related to the video delivery latency. Moreover, the SDN-based caching decision policy can substantially reduce the number of generated and broadcasted interest packets, which is a shortcoming of the current ICN.