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.

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.

The trajectory design for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks has become a hot research topic. In the UAV-assisted MEC scenario, the UAV is required to frequently adjust its flight trajectory due to dynamic factors such as time-varying task offloading requirements, user mobility, and transmission environment variation. In this paper, with consideration of the constraint induced by the UAV flight dynamics, the dynamic trajectory design challenge within the blockchain-based multi-UAV-assisted MEC framework is investigated. An intelligent algorithm that integrates multi-agent deep deterministic policy gradient (MADDPG), linear quadratic regulator (LQR), and CVXPY solver, named MADDPG-LC, is proposed to achieve real-time joint optimization of dynamic trajectory control and resource allocation with respect to minimizing weighted energy consumption and delays. Numerical simulation results demonstrate the efficacy of the proposed MADDPG-LC algorithm in addressing the UAV flight dynamics constraint, which has generally overlooked in existing works

This paper examines the cache-enabled high-low-altitude-platforms integrated network (CHLIN), which consists of multiple high-altitude platforms (HAPs) and cacheable low-altitude platforms (LAPs). CHLIN aims to leverage the edge caching, the flexibility of LAPs and the broad coverage and stability of HAPs to realize multi-ground-user content transmission. Considering the low endurance, dynamics, and limited storage capacity of LAPs, a combined optimization of content caching policies, offloading decisions, and HAP-servers and LAP-servers selection is designed to reduce the delay of content transmission while fulfilling users' demand for the quality of service. We transform the complex non-convex optimization problem with highly coupled variables into an equivalent convex problem. Afterward, a genetic-algorithm-embedded distributed alternating direction method of multipliers (GA-DADMM) is proposed, which adopts a distributed architecture for alternating iteration and introduces a genetic algorithm to derive the multi-dimensional and coupled local variables. Simulation results show that GA-DADMM achieves better convergence than the comparison algorithm, which is proper for large-scale optimization problems. The superiority of the proposed edge caching scheme in transmission delay reduction is also validated.

Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.