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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.

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As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.