Collaborative edge computing (CEC) enables multiple edge servers (ESs) to cooperatively process computation-intensive tasks generated by resource-constrained industrial internet of things devices. A challenge arises when offloading similar tasks from devices to ESs triggers duplicate computation, resulting in severe resource waste. To address this, computation reuse has recently been proposed to reduce response delay and energy consumption by caching task results at the edge. However, existing studies either overlook the time validity of task results or ignore the result retrieval cost, resulting in decreased practicality in real-world scenarios. In this paper, we propose a computation reuse-enabled CEC framework, where reusable task results are cached in edge networks and can be accessed if they are within the validity and the similarity of task inputs exceeds the preset thresholds. To minimize the long-term system cost comprising weighted task delay and energy consumption under the resource constraints, we formulate a joint task offloading, resource allocation, result retrieval and result caching (T3R) problem. Then, we propose a diffusion model-based graph reinforcement learning (DMGRL) algorithm to fully exploit the graph structural information of the edge network and optimize T3R policy. Extensive experimental results demonstrate that compared to baseline algorithms, the DMGRL algorithm achieves 37.4% maximum cost reduction and faster convergence speed.

Achieving high-precision interference recognition for autonomous aerial vehicle (AAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of AAV swarm communication. Although deep learning-based interference recognition methods for AAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labeled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this article proposes a data-knowledge-driven method for AAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed AAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments.

The deployment of multi-stream fusion strategy on behavioral recognition from skeletal data can extract complementary features from different information streams and improve the recognition accuracy, but suffers from high model complexity and a large number of parameters. Besides, existing multi-stream methods using a fixed adjacency matrix homogenizes the model’s discrimination process across diverse actions, causing reduction of the actual lift for the multi-stream model. Finally, attention mechanisms are commonly applied to the multi-dimensional features, including spatial, temporal and channel dimensions. But their attention scores are typically fused in a concatenated manner, leading to the ignorance of the interrelation between joints in complex actions. To alleviate these issues, the Front-Rear dual Fusion Graph Convolutional Network (FRF-GCN) is proposed to provide a lightweight model based on skeletal data. Targeted adjacency matrices are also designed for different front fusion streams, allowing the model to focus on actions of varying magnitudes. Simultaneously, the mechanism of Spatial-Temporal-Channel Parallel Attention (STC-P), which processes attention in parallel and places greater emphasis on useful information, is proposed to further improve model’s performance. FRF-GCN demonstrates significant competitiveness compared to the current state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120 and Kinetics-Skeleton 400 datasets. Our code is available at: https://github.com/sunbeam-kkt/FRF-GCN-master.

The computing process of tasks in Industrial Internet of Things (IIoT) environments is becoming increasingly complex due to the development of 5G and artificial intelligence. Leading devices are increasingly relying on heterogeneous platforms that integrate different types of processing units, such as CPUs, GPUs, and other resources, to meet the requirements of delay-sensitive and computing-intensive tasks. However, compared to conventional general-proposed CPU computing, CPU–GPU heterogeneous computing typically involves three processes, i.e., task preprocessing, hybrid computing, and result aggregation. These processes are associated with particular computing resources, which increases the difficulty of task offloading and computing resource allocation under task-specific resource and delay constraints. In this article, we first propose a three-stage heterogeneous computing (TSHC) model to practically describe the computing process of parallelizable tasks. Considering the heterogeneous computing resources, device queue backlogs, and collaboration of multiple edge servers, the joint task offloading and heterogeneous resource allocation (JCOHRA) problem is formulated to minimize the long-term average delay of tasks. Then, the Lyapunov optimization method is adopted to simplify the long-term queue stability constraint to a single-slot dynamic optimization problem, which is then modeled as a Markov decision process (MDP). Owing to the tight coupling between decision variables and enormous action space, we propose the multihead proximal policy optimization (MH-PPO)-based JCOHRA algorithm, which is enabled by elaborate constraint transformation and reward function design. Simulation results demonstrate that the JCOHRA scheme achieves better performance than baseline methods in minimizing the long-term average delay of tasks.

This paper presents a feasibility study of a novel multiple access technique called Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) for the next generation communication-positioning integration system. Different from the traditional positioning signals which are mostly Time Division Multiple Access with communication signals and are broadcast to all users, MS-NOMA supports continuous positioning waveform and flexible configurations for different positioning users to obtain higher ranging accuracy, lower positioning latency, less resource consumption and better signal coverage. Our major contributions are: Firstly, we present the MS-NOMA waveform and evaluate its performances by theoretical and simulation analyses. The results show it is feasible to use the MS-NOMA waveform to achieve high positioning accuracy and low Bit Error Rate with little resource consumption simultaneously. Secondly, to achieve optimal positioning accuracy and signal coverage, we model the power allocation problem for MS-NOMA as a convex optimization problem satisfying the Quality of Services requirement and other constraints. Then, we propose a novel Communication and Positioning Performances constrained Positioning Power Allocation (CP4A) algorithm which allocates the power of all P-Users iteratively. The theoretical and numerical results show our proposed MS-NOMA waveform with CP4A algorithm has great improvements of ranging/positioning accuracy than traditional Positioning Reference Signal in cellular network.

As a new Internet of Things (IoT) paradigm where smart devices work socially by exploiting social ties with adjacent devices, the Social IoT can effectively meet the real-time data sharing demands in Industrial IoT scenario, with the inter-device social relations being incentives. Besides, precaching on device level can potentially combat the backhaul capacity bottlenecks. Considering the limited cache memory, we may not use the whole capacity for caching, but leave a fraction for buffering data packets. In this article, we investigate how to maximize the quality of experience while minimizing the energy consumption. First, we design a proactive cache placement scheme for cost minimization. Next, we conceive the content sharing procedure with the framework of tripartite graph and propose a ternary stable matching algorithm to let devices self-organize the content sharing. Finally, we prove that inconspicuous manipulation with domino effect can further improve the system performance.

For the impact of the bitrate change of video streaming services according to the available bandwidth on user satisfaction, in this paper, we propose a spatial and temporal feature-based reduced reference (RR) quality assessment for rate-varying videos in wireless networks called STRQAW. First, simulating the orientation selectivity mechanism of the human visual system (HVS), the histogram of the orientation selectivity-based visual pattern in each frame is extracted as the spatial feature. The histogram similarity between the rate-varying video and the original video is computed as the spatial metric. Second, we extract the temporal variation of the DCT coefficients of the consecutive frame differences as the temporal feature. The temporal variation similarity between the rate-varying video and the original video is calculated as the temporal metric. Finally, we take into account the recency effect and assess the overall quality by combining the temporal and spatial metric. The experimental results using the Laboratory for Image and Video Engineering (LIVE) mobile video quality assessment (VQA) database show that STRQAW is consistent with the subjective assessment results, which means it reflects human subjective feelings well and it provides an evaluation for adjusting compression-coding rates in real time. STRQAW can be used to guide video application providers and network operators working towards satisfying end-user experiences.

The Internet of Things (IoT) is expected to bring great benefits to users, operators, and manufactures in different application scenarios. Given that smart objects which can exploit their own social networks and share contents via device-to-device (D2D) communications, the combined social IoT promises to collect information as well as to provide services more efficiently. This application scenario requires lower latency and higher reliability, and then we adopt D2D-based caching to reduce the downloading latency while guaranteeing the reliable delivery. To achieve this joint optimization objective, we conceive the interdependence with the framework of hierarchical bipartite graph. In this way, this combinatorial problem can be decoupled into a content sharing problem and a resource allocation problem. To solve the first one, we propose a content sharing-oriented matching algorithm with projecting social characters onto physical links. To solve the second one, we formulate this resource allocation problem as equivalent to a many-to-one matching game with peer effect. We then design a novel distributed algorithm with rotation-swap, which can converge to a stable state with limited number of iterations. Formulating the convergence procedure as another NP-hard problem, we further design a coloring-based heuristic algorithm to find a near-optimal solution. We conduct extensive simulations to demonstrate that our proposed schemes can achieve a better tradeoff between performance and complexity than other benchmarks.

Device-to-device (D2D) communications allow direct transmissions between two adjacent user devices, which can improve system performance in cellular networks. Considering full-duplex (FD) relaying outperforms half-duplex (HD) relaying in spectrum and energy efficiency, we apply the FD relaying-based D2D communication scheme in heterogeneous cellular network, which allows D2D links to underlay cellular downlink by assigning D2D transmitters as FD relays to assist cellular downlink transmissions. Although the scheme can improve the utilization of spectrum resource dramatically, the unreasonable resource sharing of D2D users will increase the inter-user interference. Therefore, in this paper, we try to optimize the throughput of D2D users on the premise of ensuring the quality of service (QoS) of cellular users. To this end, we first propose a D2D transmit power-allocation scheme and use Gale-Sharpley (GS) algorithm in matching theory to solve the resource allocation problem. Next, a Cheating algorithm based on cooperative game theory is proposed to further improve the throughput of D2D users. More importantly, due to the NP-hardness of finding the optimal solution of Cheating algorithm, we propose a heuristic algorithm based on depth first search (DFS) which using different colors to represent the different statements of D2D users to find a near-optimal solution. The simulation results show that the proposed algorithm compared with GS algorithm can significantly improve the total throughput of D2D users, and it also has lower complexity and better throughput performance against existing Cheating algorithm.

Cyber-physical Internet of things system (CPIoTS), as an evolution of Internet of things (IoT), plays a significant role in industrial area to support the interoperability and interaction of various machines (e.g., sensors, actuators, and controllers) by providing seamless connectivity with low bandwidth requirement. The fifth generation (5G) is a key enabling technology to revolutionize the future of industrial CPIoTS. In this paper, a communication framework based on 5G is presented to support the deployment of CPIoTS with a central controller. Based on this framework, multiple sensors and actuators can establish communication links with the central controller in full-duplex mode. To accommodate the signal data in the available channel band, the resource allocation problem is formulated as a mixed integer nonconvex programming problem, aiming to maximize the sum energy efficiency of CPIoTS. By introducing the transformation, we decompose the resource allocation problem into power allocation and channel allocation. Moreover, we consider an energy-efficient power allocation algorithm based on game theory and Dinkelbach's algorithm. Finally, to reduce the computational complexity, the channel allocation is modeled as a three-dimensional matching problem, and solved by iterative Hungarian method with virtual devices (IHM-VD). A comparison is performed with well-known existing algorithms to demonstrate the performance of the proposed one. The simulation results validate the efficiency of our proposed model, which significantly outperforms other benchmark algorithms in terms of meeting the energy efficiency and the QoS requirements.

This paper focuses on the cheating algorithm for device‐to‐device (D2D) pairs that reuse the uplink channels of cellular users. We are concerned about the way how D2D pairs are matched with cellular users (CUs) to maximize their sum rate. In contrast with Munkres’ algorithm which gives the optimal matching in terms of the maximum throughput, Gale‐Shapley algorithm ensures the stability of the system on the same time and achieves a men‐optimal stable matching. In our system, D2D pairs play the role of “men,” so that each D2D pair could be matched to the CU that ranks as high as possible in the D2D pair’s preference list. It is found by previous studies that, by unilaterally falsifying preference lists in a particular way, some men can get better partners, while no men get worse off. We utilize this theory to exploit the best cheating strategy for D2D pairs. We find out that to acquire such a cheating strategy, we need to seek as many and as large cabals as possible. To this end, we develop a cabal finding algorithm named RHSTLC, and also we prove that it reaches the Pareto optimality. In comparison with other algorithms proposed by related works, the results show that our algorithm can considerably improve the sum rate of D2D pairs.

In In-band Full-duplex (IBFD) wireless networks, the RTS/CTS mechanism is unable to establish an asymmetric dual link and to recognize the transmission mode of communication nodes to capture more opportunities of IBFD transmission, which limits total network throughput. In this paper, we propose a novel distributed IBFD MAC mechanism to establish symmetric/asymmetric dual link in wireless networks. Here we fully consider the two modes of asymmetric dual transmission. By medium access, the neighbors of communication nodes can clearly know network transmission status, which will provide extra opportunities of asymmetric IBFD dual communication. Finally, we develop a Markov model to characterize the non-saturation throughput of our proposed mechanism in IBFD wireless networks. The numerical results show that the throughput of IBFD network with our scheme nearly doubles that of HD network with RTS/CTS. Moreover, the non-saturation degree of the network has little influence on the throughput of our mechanism.

By current medium access control mechanisms designed for Half-duplex (HD), a node in distributed In-band Full-duplex (IBFD) wireless networks cannot identify the HD or IBFD transmission modes of the other nodes. This will decrease IBFD transmission opportunities by preventing simultaneous transmission in asymmetric dual link. In this paper, we propose a novel in-band Full-duplex Distributed Medium Access (FD-DMA) mechanism for wireless networks. Using this mechanism, both symmetric dual link and asymmetric dual link can be established by only one channel access. Moreover, all the neighbor nodes of primary transmitter and primary receiver can know exactly the IBFD transmission modes, which will increase the opportunity of IBFD communication and solve hidden nodes problem. The performance analysis and simulations show that the throughput of IBFD networks with FD-DMA mechanism nearly doubles that of the HD networks with RTS/CTS mechanism, and is much higher than that of IBFD networks with RTS/CTS mechanism.