Recent breakthroughs in Transformer-based large models, have driven widespread tasks, yet their reliance on centralized cloud deployment raises significant privacy risks due to sensitive data exposure.While edgebased collaborative inference offers a privacypreserving alternative, existing methods face critical limitations: static model partitioning cannot adapt to dynamic edge resource fluctuations, and rigid multi-head attention handling overlooks semantic-critical prioritization and parallelism.We propose EdgeFormer, a latency-aware framework for distributed Transformer inference in resource-constrained edge networks.EdgeFormer dynamically allocates model blocks across devices via efficiencystorage trade-off optimization and introduces collaborative Multi-Head Attention (cMHA), which distributes semantic-critical attention heads across devices while pruning redundant ones under real-time constraints.We further develop LiScore, a composite metric integrating attention diversity and latency costs, alongside a similarity-based retrieval method to reduce recomputation overhead.Extensive experiments demonstrate that EdgeFormer achieves up to 2.01× inference acceleration over state-of-theart baselines with ≤1.06% accuracy loss, maintaining robustness under varying edge conditions.

Recent years have witnessed an increasing popularity of the Internet of Things (IoT), the application of which on manufacturing further triggers the emergence of Industrial IoT (IIoT), a key technology for Industry 4.0. IIoT applications tend to be computation-intensive and latency-sensitive, while IIoT devices (IIDs) are typically equipped with limited computing and energy resources. Edge computing has been envisioned as a promising paradigm to bridge the gap between IIoT applications’ demands and IIDs’ capabilities, by offloading computation-intensive tasks to edge servers. Hence, the development of efficient computation-offloading approaches for IIoT has attracted numerous attention recently, and many important algorithms have been proposed. However, most of the existing computation-offloading algorithms in IIoT are developed based on either centralized scheduling or fully-distributed scheduling, which could face intractable troubles in large-scale IIoT. To address the issue, this paper proposes a semi-distributed computation-offloading approach, primarily based on the designed centralized scheduling and distributed execution framework and the proposed state propagation algorithm, so that global state collection and transmission are no longer required. Experimental results show that the proposed approach could achieve similar performance to that of centralized approaches and more satisfactory performances than those of baseline approaches in various IIoT scenarios.

Mobile devices (MDs) have undergone a booming development, yet are still capacity limited in computation and energy resources and thus could face troubles when serving computation-intensive and delay-sensitive applications. Mobile-edge computing (MEC) has been proposed to accommodate MDs with both satisfactory latency and acceptable resources, by offloading MDs’ tasks to near-deployed edge servers (ESs). Whereas, offloading tasks solely to ESs are difficult to meet distinct requirements of various applications, which leads to the emergence of end–edge–cloud orchestrated computing (EECOC). However, studies on EECOC are still insufficient, most existing of which do not consider MDs’ dynamical movements partly due to the intractability of associating optimal ESs with moving MDs. To address the issue, a novel deep reinforcement learning (DRL)-based mobility-aware (MA) EECOC scheduling approach is proposed in this article. With the goal of minimizing maximal task latency, we first formulate and transform the optimization problem into a Markov decision problem (MDP). Then, we enable DRL with elaborately designed reward functions and integrate it with NoisyNet to obtain near-optimal solutions. Furthermore, a MA component based on ConvLSTM is developed to extract MDs’ temporal–spatial distribution features and predict their movements, which are further utilized to facilitate the decision making of computation-offloading and resource-allocation actions. Extensive experimental results indicate the promising performance improvements of our approach against the state-of-the-art approaches in various scenarios.

Due to the high maneuverability and flexibility, unmanned aerial vehicles (UAVs) have been considered as a promising paradigm to assist mobile edge computing (MEC) in many scenarios including disaster rescue and field operation. Most existing research focuses on the study of trajectory and computation-offloading scheduling for UAV-assisted MEC in stationary environments, and could face challenges in dynamic environments where the locations of UAVs and mobile devices (MDs) vary significantly. Some latest research attempts to develop scheduling policies for dynamic environments by means of reinforcement learning (RL). However, as these need to explore in high-dimensional state and action space, they may fail to cover in large-scale networks where multiple UAVs serve numerous MDs. To address this challenge, we leverage the idea of “divide-and-conquer” and propose HT3O, a scalable scheduling approach for large-scale UAV-assisted MEC. First, HT3O is built with neural networks via deep RL to obtain real-time scheduling policies for MEC in dynamic environments. More importantly, to make HT3O more scalable, we decompose the scheduling problem into two-layered subproblems and optimize them alternately via hierarchical RL. This not only substantially reduces the complexity of each subproblem, but also improves the convergence efficiency. Experimental results show that HT3O can achieve promising performance improvements over state-of-the-art approaches.

Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs’ tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery ratio. In this paper, we propose a novel P2P Query algorithm based on Betweenness Centrality Forwarding (PQBCF), for opportunistic networks. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social networks, to quantify the active degree of nodes in the networks. In PQBCF, nodes with higher BC are preferable to serve as relays, leading to higher inquiry success ratio and lower inquiry delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success ratio and query delay, and approaches the performance of Flooding with much less resource consumption.

This paper proposes a clustering method, group-driven process in the sociology of the clustering process simulation, whose clustering process simulates the driven process of colony formation in sociology. Clustering experiments show that the clustering accuracy and clustering speed of the algorithm in complex network are superior to the classic optimization Fast-Newman clustering algorithm.