With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant communication, caching, and computing resources. As the two typical services types in smart city, delay-tolerant and delay-sensitive traffic requires very different quality of service (QoS)/quality of experience (QoE), and could be delivered through the routes with different features to meet their QoS/QoE requirements with the lowest costs. In this paper, we propose a novel vehicle network architecture in the smart city scenario, mitigating the network congestion with the joint optimization of networking, caching, and computing resources. Cloud computing at the data centers as well as mobile edge computing at the evolved node Bs and on-board units are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from the software-defined networking paradigm has been introduced into this architecture to facilitate the system optimization and resource integration. With the careful modeling of the services, the vehicle mobility, and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process to minimize the system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks, 2) poor efficiency of consensus mechanism in blockchain, and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this paper, we integrate mobile edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of consensus process. Meanwhile, the weighted system cost including the energy consumption and the computation overhead are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.

Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: K-means -clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

Recently, the rise of the Internet of Vehicles (IoV) has driven the broad development of intelligent transportation and smart cities. In order to promote the computing power of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is recognized as a promising computational paradigm and used in vehicular networks. However, there are some essential issues to be considered: 1) privacy and authenticity of data transmission in IoV, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is introduced and adopted to ensure the accuracy and reliability of data transmission and interaction. Meanwhile, we develop an intelligent framework of resource allocation about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the weighted consumption costs of energy consumption and computation overheads can be decreased, and the transactional throughput of the blockchain can be increased. Moreover, due to the continuity and dynamic of the available resources of mobile vehicles and computing servers, the optimization problem is modeled as a Markov decision process (MDP). Facing the large-scale and dynamic characteristics of the system, the asynchronous advantage actor-critic (A3C) approach is introduced to deal with the optimization problem. Simulation results show that our proposed scheme achieves significant advantages over other comparison schemes, such as the total reward of the proposed scheme is about 14 $\%$ higher than that of the deep $Q$ -network based scheme.

Federated learning, leveraging distributed data from multiple nodes to train a common model, allows for the use of more data to improve the model while also protecting the privacy of original data. However, challenges still exist in ensuring privacy and security within the interactions. To address these issues, this paper proposes a federated learning approach that incorporates blockchain, homomorphic encryption, and reputation. Using homomorphic encryption, edge nodes possessing local data can complete the training of ciphertext models, with their contributions to the aggregation being evaluated by a reputation mechanism. Both models and reputations are documented and verified on the blockchain through consensus process, which then determines the rewards based on the incentive mechanism. This approach not only incentivizes participation in training, but also ensures the privacy of data and models through encryption. Additionally, it addresses security risks associated with both data and network attacks, ultimately leading to a highly accurate trained model. To enhance the efficiency of learning and the performance of the model, a joint adaptive aggregation and resource optimization algorithm is introduced. Finally, simulations and analyses demonstrate that the proposed scheme enhances learning accuracy while maintaining privacy and security.

Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud–edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.