The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the $Q$ -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the $Q$ -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

This paper presents an efficient and secure chaotic S-Box based image encryption algorithm. Firstly, by cryptanalyzing a multiple chaotic S-Boxes based image encryption algorithm, we successfully cracked the cryptosystem by using chosen-plaintext attack (CPA). Secondly, we put forward a new image encryption scheme based on a novel compound chaotic map and single S-Box. In the new scheme, a novel discrete compound chaotic system, Logistic-Sine system (LSS), is proposed, which has wider chaotic range and better chaotic properties. And a new S-Box is constructed by using LSS, which has satisfactory cryptographic performance. Based on the new S-Box and the chaotic key stream, the new image encryption algorithm is designed, which consist of a round of permutation and two rounds of substitution process. The permutation and substitution key sequences are related to the plaintext image content, this strategy enables the cryptosystem to resist CPA. The simulation results and security analysis verified the effectiveness of the proposed image encryption scheme. Especially, the new scheme has obvious efficiency advantages, showing that it has better application potential in real-time image encryption.

The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Furthermore, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including “Customized”, “Partial”, and “Flexible”, known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times.

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the Device-to-Device (D2D) User Equipments (DUEs) multiplexing Cellular User Equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication Heterogeneous Networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the energy harvesting constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum resource block allocation for the D2D links, which is a non-convex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum resource block allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.

In this paper, a new one-dimensional fractional chaotic map is proposed and an image encryption scheme based on parallel DNA coding is designed by using the chaotic map. The mathematical model of the new chaotic system combines a sine map and a fraction operation. Compared with some traditional one-dimensional chaotic systems, the new chaotic system has a larger range of chaotic parameters and better chaotic characteristics, which makes it more suitable for applications in information encryption. In addition, an image encryption algorithm based on parallel DNA coding is proposed, which overcomes the shortcoming of common DNA coding-based image encryption algorithms. Parallel computing significantly increases the speed of encryption and decryption algorithms. The initial key of the cryptosystem is designed to be related to the SHA-3 hash value of the plaintext image so that the algorithm can resist a chosen-plaintext attack. Simulation experiments and security analysis results show that the proposed image encryption scheme has good encryption performance and less time overhead, and has strong robustness to noise and data loss attacks, which indicates that the proposed image encryption scheme has good application potential in secure communication applications.