Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

The rapid development of artificial intelligence (AI) has brought about revolutionary changes to industrial wireless networks. Meanwhile, these AI models have also incurred a more complex security environment. This article will investigate the new situations that may arise in the industrial radio security under the support of AI technologies. First, typical radio threats and the uniqueness of industrial wireless networks are introduced. We then review existing industrial wireless physical-layer security schemes based on various AI models from the perspective of countering these radio threats. From the attackers' perspective, three typical case studies are introduced, in which AI technologies will aid in jamming, spoofing, and eavesdropping attacks. Finally, we discussed the openness issues and potential solutions in industrial radio security. This article is of significant in understanding the current status of AI-based industrial radio security, as well as the main problems and challenges. This investigation can promote the healthy development of smart factories and future industries.

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms.

With the rapid growth of Internet traffic and smart mobile terminals, ultradense networks are adopted as the key technology of the fifth generation to enhance resource utilization and content distribution while causing serious energy efficiency problem. Mobile edge computing has recently drawn great attention for its advantages in reducing transmission delay and network energy consumption by implementing caching and computing abilities at the edge of mobile networks. To improve network energy efficiency and content transmission, in this paper, we propose a novel energy-efficient hierarchical collaborative scheme by considering the in-network caching, request aggregation, and joint allocation of caching, computing, and communication resources in a layered heterogeneous network including mobile users, small base stations, macro base stations, and the cloud. We formulate the energy consumption problem as a queuing theory-based centralized model, where the same content requests can be aggregated in the queue of each base station. Then, the optimal solution is analyzed based on the distribution characteristic of content popularity at the base stations. Simulation results show that the performance of our proposed model is much better than the existing cloud-edge cooperation solutions without considering the deployment of caching resource and request aggregation policies.