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.

Unmanned aerial vehicle (UAV) can serve as aerial base station to quickly restore the communication coverage of the disaster area, but finite energy poses a crucial challenge to UAV emergency communications. To maximize the average energy efficiency of UAV emergency communications, we propose the energy-efficient spectrum allocation and deployment (ESAD) scheme, which covers all disaster users and guarantees the quality of service (QoS) for users by deploying multiple UAVs. Based on the QoS requirement of user and graph coloring method, we propose the interference avoidance algorithm, which avoids interference among users served by the same UAV and interference among UAVs through user spectrum resource allocation and UAV spectrum management, respectively. Then, the ESAD scheme is proposed to achieve the maximum average energy efficiency of UAV while covering all disaster users. Simulation results show that compared with traditional schemes, the proposed scheme effectively reduces the number of deployed UAVs and improves the average energy efficiency of UAV emergency communications.

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes.

Due to maneuverability and Line-of-Sight (LoS) path, unmanned aerial vehicle (UAV) can serve as aerial base station to provide communication coverage and data collection for emerging Internet of Things (IoT) in the hotspot. However, limited spectrum resource and different Quality-of-Service (QoS) requirement impose critical challenges for UAV-aided IoT communications to cover massive IoT equipments (IEs). In this article, we propose the QoS-guaranteed multi-UAV coverage (QMC) scheme with interference management, which determines UAV deployment and spectrum resource allocation, to cover all ground IEs that have different QoS requirements. First, the interference management-based spectrum resource allocation (IMSA) algorithm, which utilizes the tabu search method of graph coloring, is proposed to avoid the interference between UAVs that have same IEs in their coverage. Then, we obtain the QoS-guaranteed single UAV placement (QSUP) algorithm to maximize the capacity of UAV while satisfying different QoS requirements for ground IEs. Finally, based on the IMSA and QSUP algorithms, the QMC scheme with interference management, which serves all ground IEs, is proposed to optimize the deployment of multiple UAVs for maximization of average UAV capacity. Simulation results demonstrate that compared with traditional schemes, the proposed QMC scheme achieves smaller UAV number and higher average UAV capacity due to the efficient interference management.

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.

In public safety networks (PSNs), it is an important issue how reliable data transmission recovers when some base stations (BSs) are damaged by natural disasters. An unmanned aerial vehicle (UAV) is used as a temporal relay station to transmit data of ground users (GUs) to an undamaged BS. In this paper, we consider a swarm of UAVs and introduce the following three roles for its management: (1) Relay UAVs (RUs) sacrifice their coverage capabilities to preserve the network connectivity; (2) Air BS UAVs (BUs) perform a covering task; (3) Standby UAVs (SUs) remain inactive. Then, we formulate an optimal coverage problem where we assign a role to each UAV to maximize the number of GUs that can transmit their data to the undamaged BS. First, we transform the problem into an exact potential game (EPG) whose utility function is designed based on the number of GUs served by each UAV. Next, we propose a learning algorithm to obtain an optimal role assignment and utilize the Fiedler eigenvalue, which represents the algebraic connectivity of the network topology of the swarm, to update the strategy selection probabilities. Finally, by simulation, it is shown that the proposed algorithm can strike a better balance between coverage and connectivity preservation than other benchmark algorithms.

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.

As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the social-aware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the “no pain no gain” principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize “win-win” situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

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.

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.