With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm raises concerns over communication overhead and privacy. Federated learning (FL) offers an alternative at the network edge, but practical deployment in wireless networks remains challenging. This paper proposes a lightweight FL (LTFL) framework integrating wireless transmission power control, model pruning, and gradient quantization. We derive a closed-form expression of the FL convergence gap, considering transmission error, model pruning error, and gradient quantization error. Based on these insights, we formulate an optimization problem to minimize the convergence gap while meeting delay and energy constraints. To solve the non-convex problem efficiently, we derive closed-form solutions for the optimal model pruning ratio and gradient quantization level, and employ Bayesian optimization for transmission power control. Extensive experiments on real-world datasets show that LTFL outperforms state-of-the-art schemes.

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance communication between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cramér-Rao Lower Bound (CRLB) for localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of MUs by approximately 37.65% with only a 3.69% reduction in the communication rate between the SU and RUs, thereby effectively protecting the SU location privacy.

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers. The results validate that the proposed framework consistently achieves superior performance compared to Proximal Policy Optimization (PPO), Soft Actor Critic (SAC), and ablated baselines in terms of reward, failure rate, and migration overhead, demonstrating the effectiveness of the proposed spatio-temporal modeling and hybrid reinforcement learning approach in dynamic satellite edge environments.

With the integrated development of global marine exploitation and 6G technology, building an all-domain marine wireless network has become crucial for supporting marine activities. However, the unique communication environment, varying collaboration of heterogeneous devices, and dynamic network changes pose technical bottlenecks for balancing real-time and efficient resource competition. To overcome those challenges, this paper proposes a novel integrated marine wireless network with multi-type unmanned device clusters across space-surface-submarine media. To address heterogeneous resource allocation, we consider channel capacity and device connection, modeling it as a three-sided matching framework with size constraints and cyclic preferences (TMSC). Building on this, we propose the satellite-prioritized restricted double-TMSC (SPR-DT) algorithm to solve optimal matching in quasi-static networks, aiming to maximize total backhaul revenue. To handle rapid dynamic network changes, we initialize the proximal policy optimization (PPO) with the stable solution of SPR-DT, thus addressing the challenge of acquiring real training data while accelerating algorithm convergence. Then, we propose a PPO-assisted multi-slot matching algorithm to enhance solution efficiency in large-scale dynamic scenarios. The simulation results show that the proposed algorithm achieves an optimal effect of 94.6% in quasistatic scenarios, with a complexity reduced to 3.2%. In dynamic scenarios, the results are 87.2% and 28.7%, respectively.

Due to the open nature of unmanned aerial vehicles (UAVs) communication, UAV applications face severe challenges in preserving location privacy. In open-space environments, illegitimate malicious nodes (MNs) can estimate the position of the source UAV (SU) through analysis of the signals they receive, which facilitates further attacks. Therefore, while ensuring efficient communication between UAVs, it is crucial to protect the location privacy of the SU. To address this issue, this work designs a scheme utilizing virtual partition of Active Reconfigurable Intelligent Surface (ARIS) to improve the communication rate of legitimate links while simultaneously reducing the localization accuracy of MNs with controllable artificial noise (AN) sources. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for the illegitimate localization model based on received signal strength (RSS), and formulate the corresponding joint optimization problem. Finally, the optimal division of ARIS elements and power are derived. Meanwhile, we propose dedicated reflection matrix optimization algorithms for ARIS. Simulation results validate that the proposed scheme drastically reduces the localization accuracy of MNs, while preserving communication efficiency and reliability.

Underwater acoustic sensor networks (UASNs) play a key role in ocean resource exploration and complex underwater tasks. However, the open acoustic channel makes them vulnerable to malicious node attacks. Therefore, accurately identifying attack nodes in harsh channels and adapting to their mobility presents a significant challenge. To address these issues, we adopt a meandering ocean current mobility model to describe node movement and construct a hidden Markov model (HMM) along with link transmission loss to characterize the unstable underwater acoustic channel. By monitoring the forwarding behavior of neighboring nodes and combining HMM state inference, we propose a trust model based on a subjective logic framework with dynamic topology updates to detect malicious nodes. It considers variable weights to assess improper node behavior and dynamically updates trustworthiness based on both historical trust and arrival strategies of new and old nodes. Simulation results indicate that the proposed method effectively identifies malicious nodes with attack intensities exceeding 0.38, and for intensities above 0.6, it achieves over 90% identification accuracy and adapts well to dynamic environmental mobility.

Cooperative multi-Unmanned Aerial Vehicle (UAV) systems for dynamic tasks, such as target tracking, face challenges in maintaining efficient coordination when agents become inactive upon task completion. This dynamic behavior introduces heterogeneous input streams to centralized state evaluation components (Critics) in multi-agent reinforcement learning frameworks, impairing coordination and increasing network resource demands, such as bandwidth and latency. This work proposes a novel Learned Inactive Feature Mixture-of- Experts (LIF-MoE) Critic to address the above issue, which jointly learns a compact inactive representation and applies expert-based specialization to diverse agent inputs. LIF-MoE replaces uninformative inactive observations with a learnable feature vector to provide meaningful representations for inactive states, while employing per-agent MoE processing with sparse routing to enable specialized handling of heterogeneous inputs. This approach enhances state representation for accurate value estimation, thus facilitating efficient coordination of the UAV swarms. Simulation results validate that LIF-MoE significantly improves task performance and reduces mission times compared to baselines, with pronounced advantages in complex scenarios.

The rapid advancement of technologies such as the Internet of Things (IoT), autonomous driving, and smart manufacturing has led to a massive increase in data generation at the edge of networks. This necessitates effective machine learning (ML) methods that address challenges like communication overhead and privacy concerns. Federated learning (FL) has emerged as a promising solution for distributed model training, but the increasing complexity of ML models limits its communication efficiency. To address these challenges, we propose an ultra energy-efficient FL framework (FedUEE). FedUEE utilizes model pruning-based compressive sensing, outage compensation, and joint optimization of learning and resource configurations to comprehensively reduce energy consumption. We develop analytical models that quantify the energy impact of each proposed mechanism, ultimately providing an optimized solution for communication efficiency in edge FL environments.

Collaborative underwater target hunting, facilitated by multiple autonomous underwater vehicles (AUVs), plays a significant role in various domains, especially military missions. Existing research predominantly focuses on designing efficient and high-success-rate hunting policy, particularly addressing the target's evasion capabilities. However, in real-world scenarios, the target can not only adjust its evasion policy based on its observations and predictions but also possess eavesdropping capabilities. If communication among hunter AUVs, such as hunting policy exchanges, is intercepted by the target, it can adapt its escape policy accordingly, significantly reducing the success rate of the hunting mission. To address this challenge, we propose a covert communication-guaranteed collaborative target hunting framework, which ensures efficient hunting in complex underwater environments while defending against the target's eavesdropping. To the best of our knowledge, this is the first study to incorporate the confidentiality of inter-agent communication into the design of target hunting policy. Furthermore, given the complexity of coordinating multiple AUVs in dynamic and unpredictable environments, we propose an adaptive multi-agent diffusion policy (AMADP), which incorporates the strong generative ability of diffusion models into the multi-agent reinforcement learning (MARL) algorithm. Experimental results demonstrate that AMADP achieves faster convergence and higher hunting success rates while maintaining covertness constraints.

Unmanned aerial vehicles (UAVs), which can leverage their integrated sensing, computation, and communication (ISCC) capabilities to enable distributed intelligence at the network edge, play a critical role in next-generation wireless networks. However, UAVs face significant challenges in decentralized model training, including limited computational resources, energy constraints, and inefficient communication. This paper introduces SFL-ISCC, a novel framework that combines model splitting with federated learning (FL)-supported UAV ISCC systems, designed to train a global machine learning (ML) model with minimal energy consumption across multiple UAVs. To our knowledge, this is the first attempt to integrate model splitting into FL-supported UAV ISCC systems. Within the framework, we first theoretically explore the effects of UAV deployment strategies, split layer selection, and client-side aggregation frequency on model convergence performance. Then, we formulate a joint optimization problem to minimize UAV energy consumption while guaranteeing target model convergence accuracy, and propose a low-complexity solution. Moreover, we incorporate semisupervised learning into the SFL-ISCC framework to handle the sparsity of labeled data in UAV networks. Experimental results show that our proposed scheme significantly outperforms baseline schemes in both energy efficiency and model convergence.

Recently, with the users' growing demand for communication rate and capacity in wireless networks, Unmanned Aerial Vehicles (UAVs) have attracted widespread attention due to their mobility, flexibility, and robust line-of-sight communication links. By equipping UAVs with multiple communication payloads, we can construct an aerial wireless network with three-dimensional coverage. However, due to the limitations of UAV onboard energy and communication resources, the lifetime and performance of UAV-assisted wireless networks are significantly constrained. This paper mainly focuses on equipping UAVs with mobile base stations to enhance wireless communication coverage and capacity. We propose a Joint Optimization of 3D Trajectory and Resource Allocation (JOTRA) scheme to maximize energy efficiency in complex scenarios with multi-user mobility and diverse requirements (e.g., UAV-assisted post-disaster search and rescue). Specifically, we apply Dinkelbach's iterative method and Block Coordinate Descent (BCD) method to solve the formulated multivariable and non-convex maximization problem. The algorithm's convergence has been analyzed. According to the simulation, the proposed algorithm can converge faster while maximizing energy efficiency in complex wireless communication scenarios.

Low-earth orbit (LEO) satellite-assisted marine communication networks have become a research focus with the growth of marine activities. However, establishing communication links between underwater devices and maritime satellites is a challenge. Additionally, dynamic environments and multidomain media pose significant challenges in allocating resources effectively within this network. To address these issues, this paper constructs a Space-Surface-Submarine Unmanned Network (3SUN) incorporating LEO satellites, unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs). We formulate the resource allocation problem in the 3SUN as a satellite revenue maximization problem. We propose a satelliteprioritized restricted three-sided matching algorithm to solve the match within a single time slot. Additionally, we incorporate deep reinforcement learning (DPL), using the previous stable matching results as training initialization to tackle dynamic connections across multiple slots. Simulation results show that our algorithm achieves satellite revenue closer to the optimal solution compared to other methods while maintaining lower time complexity.

Unmanned Aerial Vehicles (UAVs) are critical in modern emergency response due to their high mobility. However, limited computing resources and energy supplies necessitate the use of UAV networks for collaborative inference. UAV intelligent tasks are often Deep Neural Networks (DNN)-based, with DNN partitioning enabling collaborative inference. However, executing DNN partitioning in a highly dynamic UAV network faces two challenges that have not been addressed in existing research: the time gap between the state sampling and the execution of the corresponding action based on that state, and the unknown trajectories in advance. The time gap requires predictive action decision-making. To address this, we model DNN partitioning and edge offloading with hybrid action decisions in dynamic, energy-harvesting UAV networks as a Predictive Markov Decision Process (P-MDP). The rapidly changing and previously unknown network topology significantly impacts channel and data transmission energy consumption, affecting DNN partitioning decisions. To better solve the action prediction problem, we use the Transformer module to extract motion features from recent time slots in the proposed Transformer-enhanced Multi-Agent Hybrid Action Proximal Policy Optimization (TE-MHAPPO) framework. Simulation results show that TE-MHAPPO reduces the reward which comprehensively considers task delay and energy consumption, by at least 12.1% compared to the state-of-theart MHAPPO. Additionally, its reward performance degradation with the increase in prediction time is at most 55.2% of that observed in the baseline.

Emerging real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning. Federated learning (FL) enables on-device training without raw data sharing, yet remains challenging in resource-constrained environments due to energy-intensive computation and communication, as well as limited and non-i.i.d. local data. We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks. FedDPQ integrates diffusion-based data augmentation, model pruning, communication quantization, and transmission power control to enhance training efficiency. It expands local datasets using synthetic data, reduces computation through pruning, compresses updates via quantization, and mitigates transmission outages with adaptive power control. We further derive a closed-form energy–convergence model capturing the coupled impact of these components, and develop a Bayesian optimization (BO)-based algorithm to jointly tune data augmentation strategy, pruning ratio, quantization level, and power control. To the best of our knowledge, this is the first work to jointly optimize FL performance from the perspectives of data, computation, and communication under unreliable wireless conditions. Experiments on representative CV tasks show that FedDPQ achieves superior convergence speed and energy efficiency.

Underwater target hunting (UTH) is a critical and complex mission involving the search, monitoring, and hunting of targets in an underwater environment. However, the unpredictable trajectories and flexibility of these targets, along with complex underwater environments, significantly impede the efficiency and success of traditional schemes that depend solely on unmanned underwater vehicles (UUVs). Consequently, this paper presents the “3U network”, a novel heterogeneous framework integrating unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and UUVs for UTH. Within this framework, a UAV searches and monitors the target, a USV acts as a communication relay, and a swarm of UUVs hunts the target. Moreover, to improve the timeliness of target search, we propose the age of information (AoI)-based UAV search strategy. Additionally, we construct a constrained multi-objective optimization problem aiming to minimize energy consumption and mission duration by optimizing vehicles' trajectories, considering mobility limitations, safety, and connectivity constraints. To tackle this problem, we design an AoI- and energy-aware deep reinforcement learning (DRL) algorithm to optimize control policies for heterogeneous vehicles. The experimental results demonstrate that the proposed scheme outperforms the baseline schemes in terms of energy consumption, and mission duration and success rates.

To meet requirements for real-time trajectory scheduling and distributed coordination, underwater target hunting task is challenging in terms of turbulent ocean environments and dynamic adversarial environment. Despite the existing research in game-based target hunting area, few approaches have considered dynamic environmental factors, such as sea currents, winds, and communication delay. In this article, we focus on a target hunting system consisted of multiple unmanned underwater vehicles (UUVs) and a target with high maneuverability. Besides, differential game theory is leveraged to analyze adversarial behaviors between hunters and the escapee. However, it is intractable that UUVs have to deploy an adaptive scheme to guarantee the consistency and avoid the escape of the target without collision. Therefore, we conceive the Hamiltonian function with Leibniz's formula to obtain feedback control policies. In addition, it proves that the target hunting system is asymptotically stable in the mean, and the system can satisfy Nash equilibrium relying on the proposed control policies. Furthermore, we design a modified multiagent reinforcement learning (MARL) to facilitate the underwater target hunting task under the constraints of energetic flows and acoustic propagation delay. Simulation results show that the proposed scheme is superior to the typical MARL algorithm in terms of reward and success rate.

With the growth of the magnitude of multiagent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, the convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs toward an absolute probability sequence $\pi $ is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multiagent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions and extended to nonconvex objectives that satisfy Polyak-Lojasiewicz (PL) conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework.

Federated learning (FL) has emerged recently as an appealing and promising technique to deal with distributed learning issues in the sixth generation (6G) communication systems. Recent studies focus on developing FL schemes for terrestrial radio networks, where the variation in transmission data rates caused by transmission distance changes is negligible over one communication round. However, this variation has considerable influences for underwater acoustic channels. In this paper, we propose an underwater dynamic federated learning (UDFL) scheme by jointly considering characteristics of underwater acoustic channels and moving behavior of autonomous underwater vehicles. Moreover, an energy consumption minimization problem is formulated based on the scheme. To meet the challenges of transmission latency and FL performances, we consider them separately and provide closed-form solutions to the two individual problems. Specifically, we theoretically characterize the connections between transmission power and FL performances, and derive the optimal transmission policy given transmission latency constraints. Based on the two solutions, a dynamic programming based online power control algorithm is proposed to determine the transmission power across all time slots. Numerical simulations are conducted to demonstrate that the designed scheme is effective and the proposed online algorithm can achieve latency constrained energy-efficient UDFL.

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments.

The Autonomous Underwater Vehicle (AUV)- assisted Underwater Internet of Things (UIoT) has received much attention due to its potential to develop marine resources with big data analysis. Given the low data rates and distributed data, decentralized federated learning (DFL) emerges as a promising avenue, enabling artificial intelligence integration and collaborative training within the underwater environment. In this paper, we combine DFL with the underwater scenario for the first time. A novel DFL algorithm with decaying aggregation weight is proposed to achieve fast convergence rate under non-independent and identically distributed (non-IID) data. In addition, the DFL algorithm is tailored to underwater acoustic channels, and we integrate adaptive compression, device connectivity, and time-varying topology considerations to enable practical deployment. We provide convergence analysis under convexity and connectivity assumptions. Simulation experiments validate the performance using the MNIST dataset, highlighting its effectiveness for practical UIoT applications.

Recently, Federated Learning (FL) has revolutionized the processing and analysis of vast volumes of data generated by wireless devices, effectively overcoming the traditional cloud computing constraints within Internet of Things (IoT) networks. However, practical challenges arise as data on edge devices dynamically changes, necessitating continuous learning capabilities known as Federated Continual Learning (FCL). One key challenge in FCL is the issue of catastrophic forgetting, which refers to preserving the training performance on old data while training on new data. While common strategies involve retaining a subset of old data to mitigate the issue, privacy concerns limit this approach, and the balance between emphasis on new and old data during the training process remains inadequately studied. To address the above challenges, we propose an Adaptive Federated Continual Learning (AdapFCL) method in heterogeneous environment, which eliminates the need for episodic memory in federated settings. Specifically, the server employs a Deep Convolutional Generative Adversarial Network (DCGAN) model with a data-free knowledge distillation technique, which enables the server to learn representations of old data and generate synthetic data involving only global model. Then clients perform local training by utilizing new data and synthetic data instead of storing old data. Furthermore, we quantify the degree of forgetting on old data for each client, allowing for adaptive adjustment of emphasis weights for old and new data during the training process. Simulation results validate that the proposed method can achieve superior average test accuracy while maintaining communication efficiency compared with baselines, especially in highly heterogeneous data scenarios.

Integrated sensing and communication (ISAC) technology is envisioned as a pivotal component for the next-generation communication networks. Similarly, underwater acoustic ISAC (UWA-ISAC) holds promising prospects for enhancing future UWA sensor networks due to its efficient communication and sensing capabilities. However, the design of UWA-ISAC waveforms and receivers suitable for multi-user scenarios faces formidable challenges, primarily arising from the complex UWA channel and multi-access interference (MAI). In this paper, we propose a UWA-ISAC waveform design scheme based on generalized sinusoidal frequency modulation (GSFM). The proposed waveform provides satisfactory communication and sensing performance and exhibits excellent orthogonality. Furthermore, we design a MAI suppression receiver, leveraging successive interference cancellation based on two-factor compensation and turbo equalization to improve interference suppression capabilities and enhance communication performance. Simulation results validate that the proposed UWA-ISAC waveform has an approximate thumbtack ambiguity function and comparable cross-correlation properties with GSFM under the defined parameters. Moreover, the designed receiver with low training sequence overhead is robust against Doppler, and can iteratively improve the MAI suppression performance.

Autonomous underwater vehicles (AUVs) are increasingly utilized across various domains, employing diverse machine learning (ML) algorithms to enhance functionality. However, the dynamic underwater environment, characterized by high temporal and spatial variability, makes offline-trained models on static datasets inadequate for practical AUV operations. This necessitates the integration of online learning capabilities that can adapt to changing conditions in real time. The quality of training data plays a crucial role in the performance of these models, and the ability to utilize distributed data from multiple AUVs can be beneficial. Nonetheless, the challenge lies in the limited communication resources available underwater. The typical acoustic communication rates, which are just in the tens of kilobits per second, pose a significant barrier to implementing centralized ML strategies that require extensive data sharing among AUVs. To overcome these hurdles, we propose an underwater federated learning (UFL) framework that incorporates model pruning and gradient quantization. This approach aims to establish a communication-efficient distributed learning paradigm. Furthermore, we have derived a closed-form expression to quantify the upper bound of the convergence error, which highlights the impact of pruning and quantization on the federated learning (FL) convergence. Additionally, we utilize a heuristic algorithm to optimize the pruning and quantization strategies, aiming to minimize the convergence error while adhering to delay constraints. The effectiveness of our proposed framework is demonstrated through its application in a cooperative navigation task involving multiple AUVs, showing significant resource conservation and enhanced operational efficiency.

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

This paper focuses on the design of intelligent game strategy for multi-autonomous underwater vehicle (multi-AUV) underwater network system. The challenge lies in ensuring the coordination and stability between AUVs in complex underwater environments. To meet underwater data sharing requirements, we formulate an intelligent game strategy incorporating communication delays by formulating the problem as a partially observable Markov decision process (POMDP). Additionally, to address the issue of sparse rewards during exploration in multi-agent reinforcement learning (MARL) models and improve the coordination among AUVs, we propose a heuristic and artificial potential field (APF)-assisted multi-agent proximal policy optimization (HA-MAPPO) algorithm. Our proposed scheme addresses the issue of sparse rewards in MARL by using APF as path planner and subsequently utilizes heuristic algorithm for task scheduling to achieve optimal goal allocation. Simulation results demonstrate that our proposed HA-MAPPO algorithm outperforms current mainstream MARL algorithms regarding convergence speed while maximizing the winning rates.

Unmanned underwater vehicles (UUVs) face challenges such as high hardware costs, security concerns, a lack of training data in the actual development and debugging. Creating a simulation platform for simulation verification, training, and learning presents a potential solution to address these challenges. However, this area has seen limited prior work, and existing underwater platforms lack accuracy, user-friendliness, and intelligence. Therefore, this paper introduces an intelligent simulation platform “UUVSim” based on the robot operating system and Gazebo. UUVSim modular integrates basic modules such as high-precision simulation scenarios, dynamic models, sensors and controllers, while reserving programming interfaces. In addition, UUVSim provides reinforcement learning environment for UUV intelligent learning, supplemented with scenario transfer training, multi-agent reinforcement learning, offline reinforcement learning techniques to realize efficiently training for complex tasks, multi-robot coordination, and simulation to reality (sim2real) deployment. Further, we validate these technologies through underwater target tracking benchmarks and sim2real experiments, demonstrating the platform’s practicality.

Autonomous underwater vehicles (AUVs) play a crucial role in data collection for underwater acoustic sensor networks (UWASNs). The limited capacity of individual AUV and the need for low-latency data collection necessitate the deployment of AUV swarms to achieve efficient and secure cooperative data collection. However, most existing works assume prior knowledge of sensor node locations, which is impractical in real-world AUV networks. Additionally, continuous data collection needs to be considered due to the sustained operation of sensors and cluster head replacement. To address these challenges, we propose a target uncertainty map assisted data collection scheme for AUV swarms based on the multiagent proximal policy optimization (MAPPO) algorithm. Specifically, the target uncertainty map is established by leveraging current and past search and collection results, guiding the AUV swarm to prioritize areas with higher probabilities of containing sensor nodes. Moreover, a digital pheromone mechanism incorporating repulsive and attractive pheromones is designed to establish an artificial potential field for adjusting the target uncertainty map. To further enable a comprehensive exploration of unknown environments, we introduce the Age of Information (AoI) as an indicator. Additionally, we consider the energy consumption associated with data collection to strike a balance between collection and energy efficiency, and derive a lower bound on the policy improvement achieved by the MAPPO algorithm. Simulation results have validated that the proposed scheme has a superior performance compared to the baselines, achieving an approximately 15% increase in the collection rate while reducing the energy consumption of data collection and AoI as well.

Unmanned aerial vehicle (UAV) assisted data collection has been extensively employed in various application scenarios, e.g., nonterrestrial networks for disaster management, agricultural crop protection, environmental monitoring. However, data collection and transmission model in different applications are not universal, and the timeliness of large-scale data collection and transmission also has been remained as a challenge. To address this issue, artificial intelligence (AI)-empowered intelligent search algorithms for path planning in UAV-assisted data collection networks are investigated in this article. With the constraints, including energy consumption, transmission distances, and full coverage of sensors, a data collection model using UAV in hovering mode is first established for minimizing the flight distances of UAVs, and an adaptive full coverage algorithm (AFCA) is proposed to optimize the Quality of Service through using the model. Subsequently, for optimizing the path planning of UAVs, an intelligent path planning algorithm (IPPA) is proposed through considering the loop and noncrossing characteristics presented by the optimal paths. In six testing cases with different sensor sizes, the experimental results have been shown to demonstrate that the proposed solution outperforms the traditional algorithms.

Autonomous underwater vehicles (AUVs) can function as sensor nodes in Internet of Underwater Things (IoUT), contributing to ocean exploration and monitoring by collecting and transmitting data to the base station. Most of the routing algorithms applied to IoUT require the participation of stationary nodes and seldom consider the fluctuations of network topology, which cannot be directly applied to the IoUT composed of AUVs. The focus of this research is to examine the problem of packet routing in a dynamic AUV-assisted IoUT, with the ultimate goal of ensuring the effective transmission of underwater information. We analyze the transmission pattern of underwater acoustic signals and the consequent communication disruption between AUVs, which helps establish the Age of Information (AoI) and bit error rate (BER) of data through modeling. A routing algorithm that utilizes the branch-and-bound (BB) technique has been suggested, alongside the introduction of the Value of information (VoI) to enable the joint optimization of the AoI and BER. We describe two nearly optimal heuristic algorithms for networks with a high number of AUVs. The AFA-ACO-BB strategy is designed based on the above algorithms and the influence of AUV motion on link reliability is considered. Moreover, we have developed a power regulation mechanism that can effectively minimize the occurrence of network packet loss and energy waste. The simulation results demonstrate that the proposed scheme outperforms certain classically related schemes in terms of AoI and BER, while simultaneously maintaining superior packet loss rate (PLR) and energy consumption.

In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.

The distributed subgradient (DSG) method is a widely used algorithm for coping with large-scale distributed optimization problems in machine-learning applications. Most existing works on DSG focus on ideal communication between cooperative agents, where the shared information between agents is exact and perfect. This assumption, however, can lead to potential privacy concerns and is not feasible when wireless transmission links are of poor quality. To meet this challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of the data. Compared with perfect data, quantization poses fundamental challenges to maintaining data accuracy, which further impacts the convergence of the algorithms. To overcome this problem, we propose a DSG method with random quantization and flexible weights and provide comprehensive results on the convergence of the algorithm for (strongly/weakly) convex objective functions. We also derive the upper bounds on the convergence rates in terms of the quantization error, the distortion, the step sizes, and the number of network agents. Our analysis extends the existing results, for which special cases of step sizes and convex objective functions are considered, to general conclusions on weakly convex cases. Numerical simulations are conducted in convex and weakly convex settings to support our theoretical results.

Unexploded Ordnance (UXO) buried in the seafloor poses a serious threat to the environment and human safety. Removing UXOs in the ocean presents a tricky challenge due to the greater difficulty in controlling the damage caused by explosions compared to those on land. Therefore, this study designs a method for detecting seafloor-buried UXO using the transient electromagnetic (TEM) approach and proposes a new forward model for UXO characteristic responses in the ocean by modeling the marine environments as a two-layer medium. We first numerically solve the time-harmonic equations of the primary magnetic field using Sommerfeld integrals and Hankel transforms. Then, we derive the characteristic responses of UXO in seawater based on the three-dimensional magnetic dipole model and the TEM method. Finally, we simulate the characteristic responses of six typical UXOs and two interfering targets, comparing them with those in free space and analyzing the effects of type, measured distance, and buried attitude on identification. The results show that the characteristic responses in seawater delay 2-12 ms with target size compared to free space. The errors of the characteristic response measurement depend not only on the measured distance but also on the buried attitude of the target. The errors reach the maximum when the target is vertical, the minimum when horizontal at the same distance, and disappear when the measured distance is longer than twice the target size. These findings establish a crucial foundation for accurately identifying the type, burial state, and location of UXO during marine demining.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in ocean target tracking and image acquisition for processing. Due to the limited energy of the UAV and the high computational complexity associated with image processing tasks, a lightweight energy-saving target tracking scheme is designed for the UAV, and the unmanned surface vehicle (USV) based mobile edge computing (MEC) networks are adopted to share the computing load of the UAV. Due to the randomness of the environment, we formulate data processing, computation offloading, resource allocation, and target-tracking as a joint stochastic optimization problem. This paper investigates a two-stage optimization scheme to address the problem. Firstly, we employ a Lyapunov-based approach to convert the stochastic optimization problem into a deterministic per-time slot problem under communication and computing resources constraints. Then, we develop a real-time target tracking scheme for the UAV based on the Elman neural network. Numerical results validate that the designed tracking scheme can effectively minimize propulsion energy consumption while maintaining a high success rate in tracking. Furthermore, the proposed method balances data-related energy consumption, image detection accuracy, and stability of the data storage queue.

Federated learning has been combined with graph learning for modeling spatial-temporal data while maintaining data confidentiality and safety. However, there are still several issues: 1) In practical usage, some clients may be unable to participate in the model inference due to poor network signal, malicious attacks, etc. 2) In the communication process, the uploaded information is easily disturbed by noise. The performance of the graph model will be seriously affected by its low robustness. Additionally, the assumption of identical distribution between the training and testing domain does not hold in practical scenarios, resulting in overfitting and poor generalization ability of the trained models. 3) The relations that exist among clients may change dynamically over time and manually constructing the graph structure of clients may not accurately represent the relations among clients. In this paper, we address all the above limitations by proposing a robust hierarchical split-federated graph model named DCSFG. Specifically, DCSFG combines split-federated learning and spatial-temporal graph model to better capture the spatial-temporal dependencies. We propose a Dropclient method and introduce the uncertainty estimation to enhance the robustness and generlization ability of the model. We also design a dual-sub-decoders structure for clients so that they can perform predictions locally and independently when they are unable to participate in the inference process. A novel hierarchical graph message passing structure is proposed to enable each client to perceive the global and local information. The extensive experimental results demonstrate the effectiveness of DCSFG.

Since the introduction of Bitcoin in 2008, blockchain technology has found widespread applications across various domains. While blockchain offers convenience and immense research value, it also raises privacy and security concerns among users and society at large. Notably, numerous studies have demonstrated the vulnerability of blockchain anonymity. Existing solutions based on bloom filters and SGX(Software Guard Extensions) may safeguard users' access patterns but remain susceptible to novel attacks, including protocol-level and side-channel attacks. To address these issues, we propose a Tree-based Oblivious Random Access Protocol (Tree-ORAP) that not only provides access pattern protection in privacy-preserving blockchain systems but also preserves the original blockchain performance. Furthermore, we design a Tree-ORAP State Version Controller to manage state synchronization across nodes in a multi-client blockchain network. We also analyze the system's security and implement a Tree-ORAP prototype, conducting a series of experiments to demonstrate its efficiency and technical feasibility. In summary, our protocol offers enhanced protection for blockchain systems against a wider range of attacks compared to previous methods, all while maintaining superior security performance and equal or better efficiency.

The development of Unmanned Aerial Vehicles (UAVs) offers new prospects for emerging applications in the Industrial Internet of Things (IIoT) networks. With the assistance of Digital Twin (DT), a real-time understanding of physical entities can be constructed for dynamic perception and decision-making. However, DT modeling requires distributed data aggregation, resulting in privacy disclosure and communication burden. Therefore, we propose the digital twin edge network by integrating the DT technology and edge computing, which leverages an over-the-air computation enabled federated learning architecture for an efficient and secure DT model construction. Specifically, we propose a heterogeneity-aware and energy-conscious device scheduling mechanism, considering the update importance, channel condition, and computation capacity based on a probabilistic scheduling framework. To enhance energy efficiency, we introduce a virtual queue to track the difference between the cumulative energy consumption and budget. Additionally, we design a low-complexity scheduling algorithm to solve the optimization problem. Simulation results validate the superiority of our proposed mechanism in improving the test accuracy and energy efficiency in a heterogeneous and energy-constrained environment. Moreover, the proposed mechanism demonstrates significant advantages when employed to highly heterogeneous datasets, and exhibits a certain level of robustness to mapping errors arising from the utilization of DT technique.

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.

Over-the-air computation (AirComp) is considered a transformative enabler for next-generation artificial intelligence (AI) services and wireless data aggregation via the electromagnetic waveform-superposition property of wireless multi-access channels (MAC). However, the conventional distortion metric, minimum square error (MSE), is imperfect and not universally applicable in specific AirComp scenarios in a low-signal-to-noise ratio (SNR) regime and under power budget constraint. Conversely, the average discriminant gain is studied for task-oriented AirComp AI services like classification but with inaccurate performance indication. To solve these problems, this work establishes a novel framework for AirComp systems from the mutual information (MI) perspective. First, we categorize the AirComp model into two distinct classes based on the source (sensing) data independence, namely diverse-targets (DT) AirComp and homogeneous-target (HT) AirComp. Both categories with different inputs like classical Gaussian and classification-based Gaussian mixture model (GMM), can be unified and assessed via MI criterion. Next, for the DT AirComp system, we introduce a novel MI-aided AirComp beamforming scheme employing majorization-minimization (MM) relaxation. As for the HT AirComp, we present a heuristic successive approximation (SA)-based beamforming method considering complex GMM inputs. We also provide the feedback and update protocol for AirComp tracking. Simulations validate the superior performance on AirComp throughput and task-oriented metrics such as classification accuracy with our proposed MI-aided beamforming schemes.

With the rapid development of intelligent vehicles, heterogeneous in-vehicle networks (HetIVNets) applying heterogeneous access technologies in terms of cellular and in-vehicle WiFi, are widely employed to provide stable and ubiquitous network environments for intelligent vehicles and their passengers. Most existing studies on the optimization of heterogeneous networks (HetNets) focus on user association, channel and power allocation. Additionally, these studies typically employ a single task metric to characterize the quality of service (QoS) requirements of the devices. In order to achieve green and energy-efficient intelligent vehicles, our work considers a HetIVNet composed of WiFi and cellular networks, and further optimizes the bandwidth allocation and energy consumption of WiFi access point (AP). Furthermore, to achieve more accurate resource allocation for different tasks in HetIVNet, we establish the QoS requirement model of various tasks for in-vehicle devices. Since the proposed optimization problem is non-convex and NP-hard, we formulate the objectives and constraints of user association and resource allocation (UARA) in HetIVNets as a markov decision process (MDP) and propose a proximal policy optimization (PPO) algorithm for in-vehicle intelligent resource allocation to uniformly schedule network resources. Simulation results validate that the proposed algorithm can achieve high task success rates under low-energy consumption conditions for WiFi AP. We also compare our algorithm with the state-of-art baselines to highlight its efficiency and stability.

Recent years have envisioned the widespread adoption of machine learning (ML) in unmanned aerial vehicle (UAV) swarms for task execution. However, it is hard for the traditional centralized ML approaches to be applied in UAV swarms due to the large latency, communication cost, and privacy disclosure when transmitting the raw data. As an alternative, over-the-air computation (AirComp)-enabled federated learning (FL) is expected as a communication-efficient solution by harnessing the interference. However, the benefit of AirComp is at the cost of compromised training performance due to the channel distortion caused by the fading channel and noise and straggler issues resulting from the aligned parameters. In addition, the limited energy budget and dynamic environment incorporating the mobility characteristic of UAVs and time-varying channel conditions make these issues more complex. To solve problems aforementioned, this work proposes an energy-and-communication-efficient device scheduling scheme for AirComp-enabled FL system in the UAV swarms. Specifically, we first derive the optimality gap to characterize the impact of channel distortion and device selection on training performance. Then based on this result, we formulate an optimization problem to minimize the optimality gap by scheduling an appropriate number of competent following UAVs in each round, considering the transmission and computation energy consumption as well. Simulation results validate that the proposed scheme can achieve superior training performance in terms of test accuracy compared with baselines, and shows robustness with the increasing scale of UAV swarm.

With the rapid development of intelligent vehicles in recent years, in-vehicle heterogeneous networks (HetNets) incorporating base stations (BSs) and vehicle access points (VAPs) have been widely deployed to support the ever-emerging diverse vehicular applications. However, most of the existing works focused on the HetNets covering multiple vehicles and considered the needs of all in-vehicle users as a holistic entity to maximize the overall performance of networks, which inevitably deviates from the local in-vehicle HetNet quality that most users are concerned about. Additionally, improving the energy efficiency (EE) of mobile devices in vehicles to extend their battery life is another significant issue, which has not been well addressed. To address the above issues, an intelligent in-vehicle power control and spectrum allocation mechanism is proposed in this work to maximize the EE of devices in the cabin while satisfying their dynamic traffic demands. Since this optimization problem has a non-convex mixed integer programming form, which is difficult to solve with traditional optimization methods, we further transform the optimization problem into a Markov Decision Process (MDP) and utilize a Proximal Policy Optimization (PPO) algorithm combined with the vehicle's location and historical channel state information (CSI) to achieve optimization objectives. Simulation results validate that the proposed algorithm can satisfy the dynamic traffic requirements of devices with high EE. Further comparison with baselines highlights the robustness of the proposed scheme under different device quantities and ratios.

In recent years, mobile edge computing (MEC) has been proposed as a promising technique to alleviate the challenges faced by delay and computation-intensive applications. However, users in remote and mountainous areas continue to face difficulties obtaining reliable computation services. To overcome this obstacle, unmanned aerial vehicles (UAVs) equipped with MEC servers have emerged as a popular solution. In such a multi-UAV network, the coverage areas of the UAVs might overlap, which would result in resource wastage and interference. To address this issue, we investigate a collaborative UAV-assisted MEC system for both aerial users (AUs) and ground users (GUs) in this work. Specifically, each user is covered by multiple UAV servers, and the resources of UAVs are dynamic over time. The main objective of this work is to reduce the average delay and improve the service success rate by jointly designing the UAV server-user association, bandwidth, and computing resource allocation strategy. To address the non-convex optimization problem mentioned above, we formulate a multi-agent extension of Markov decision processes (MDPs) for the system and design a cooperative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach for each UAV server to make decisions using a centralized training approach with distributed execution. Simulation results validate that the proposed approach can achieve a superior success service rate with a lower delay compared with baselines.

In recent years, autonomous underwater vehicles (AUVs) have been widely applied to collect data in underwater acoustic sensor networks (UWASNs). Limited by the capacity of a single AUV, as well as the low-latency requirement of data collection, the intelligent swarm consisting of multiple AUVs is expected to execute the secure and efficient data collection tasks in a cooperative manner. However, most of the existing works assumed that the locations of sensor nodes are already known, which is impractical in a real AUV network. In addition, the security issues are not well considered in underwater searching and transmission tasks. To improve the searching efficiency in an unknown underwater area where locations of sensor nodes cannot be obtained precisely, this work proposes a data collection scheme via a target uncertainty map based multi-agent reinforcement learning algorithm for AUV swarms. Specifically, the target uncertainty map is established based on the current and past searching and collection results, which can guide the AUV swarm to search the areas with higher probabilities to find sensor nodes waiting for data collection. Moreover, to mitigate the potential security risk of data leakage, we design a multi-agent deep deterministic policy gradient (MADDPG) algorithm for each AUV in the swarm to make its searching and data collection strategies through a manner of centralized training with distributed execution. Simulation results validate that the proposed scheme can achieve a high collection rate with low energy consumption. In addition, the security referring to data protection can be also guaranteed in AUV swarms.

It is significant to design a task scheduling scheme for the multi-objective task of autonomous underwater vehicle (AUV) network for target hunting and environmental exploration. Due to the limited communication and detection conditions, it is difficult for each individual AUV in the network to accurately obtain all environmental information without a central control node. Therefore, most centralized scheduling schemes are infeasible to a fully distributed AUV network. To address the aforementioned issues, a distributed multi-agent proximal policy optimization (DMAPPO) scheme is proposed in this work, where AUVs are efficiently scheduled to achieve target hunting and environmental exploration. The distributed scheduling scheme is able to adjust the number of AUVs for each task according to practical requirement. In addition, we design an intra-network cooperative multi-AUV environmental exploration method by introducing the age of information (AoI). Simulation results validate that the proposed algorithm can achieve an effective task scheduling in the distributed AUV network.

In this article, we aim to design a task scheduling scheme for the underwater multiobjective task of target hunting and environmental search. A distributed autonomous underwater vehicle (AUV) network is deployed to perform the task, where AUVs equipped with sensors can cooperatively search the environment and hunt the target by sharing local information. To achieve efficient exploration of the overall environment by the AUV network, we design an intranetwork cooperative searching approach based on the Age of Information (AoI). Besides, it is critical to conceive an energy-efficient mechanism due to the energy constraints of AUVs and the difficulty of sustainable energy supply. To address the aforementioned issues, we propose an energy-efficient distributed multiagent proximal policy optimization (DMAPPO) scheme to perform real-time AUV target hunting and environment searching in underwater turbulent fields. The proposed scheme can adjust the number of AUVs assigned to each objective according to practical requirement and residual energy. Distributed AUVs can make decisions autonomously and cooperatively complete the task efficiently through limited information interaction. In addition, we derive a lower bound on the policy improvement of MAPPO. Moreover, our simulation results demonstrate that the proposed scheme outperforms the standard algorithms in terms of hunting efficiency, degree of searching, and network energy efficiency.

To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions.

Vehicular edge computing (VEC) is an effective method to increase the computing capability of vehicles, where vehicles share their idle computing resources with each other. However, due to the high mobility of vehicles, it is challenging to design an optimal task allocation policy that adapts to the dynamic vehicular environment. Further, vehicular computation offloading often occurs between unfamiliar vehicles, how to motivate vehicles to share their computing resources while guaranteeing the reliability of resource allocation in task offloading is one main challenge. In this paper, we propose a blockchain-enabled VEC framework to ensure the reliability and efficiency of vehicle-to-vehicle (V2V) task offloading. Specifically, we develop a deep reinforcement learning (DRL)-based computation offloading scheme for the smart contract of blockchain, where task vehicles can offload part of computation-intensive tasks to neighboring vehicles. To ensure the security and reliability in task offloading, we evaluate the reliability of vehicles in resource allocation by blockchain. Moreover, we propose an enhanced consensus algorithm based on practical Byzantine fault tolerance (PBFT), and design a consensus nodes selection algorithm to improve the efficiency of consensus and motivate base stations to improve reliability in task allocation. Simulation results validate the effectiveness of our proposed scheme for blockchain-enabled VEC.

Artificial intelligence (AI) is expected as a revo-lutionary technology to be widely used in Internet-of- Things (IoT) networks for computationally intensive tasks. However, the traditional centralized training framework imposes large latency, network burdens and high risk of privacy disclosure. As a promising distributed solution, federated learning involves the collaborative model training among edge devices, with the orchestration of a server to carry the capacities of low-latency and privacy preservation for AI -driven networks. To further improve the communication efficiency, over-the-air computation (AirComp) is capable of computing while transmitting data by exploiting the superposition property of wireless channels to harness the interference. However, gradient aggregation suffers from channel distortion induced by channel fading and noise, which may degrade the training performance. Moreover, it is beneficial to schedule the informative edge devices in federated learning under limited energy resources. In this work, we propose a dynamic device scheduling scheme for AirComp enabled federated learning systems. In this scheme, a proper number of qualified edge devices with channel inversion based power control are scheduled to participate the model training, where local updates diversity, channel condition and energy consumption are exploited jointly. Inspired by the Lyapunov drift-plus-penalty method, we formulate the optimization problem to attain the device selection strategy. Simulation results validate that the proposed scheme can achieve a close-to-optimal test accuracy with fast convergence rate, and present good performance of robustness under different channel conditions.

With the development of marine exploitation, underwater acoustic sensor networks (UWA-SNs) have become a hot research field. However, the harsh environment poses a threat to the security of underwater communication, as most routing protocols ignore the curve transmission of acoustic wave, which are more susceptible to transmission interference with higher transmission delay. To cope with these problems above, this work exploits a model under the assumption that the sound curve propagation relies on positive sound speed gradient. In order to find the path with the shortest delay, we design a routing scheme inspired by artificial fish swarm (AFS) and ant colony optimization (ACO) algorithms. Furthermore, we establish the path comprehensive benefit (PCB) to make a tradeoff between transmission delay and the lifetime of network. The simulation results validate that the algorithm proposed in this work is capable of improving the system performance compared to the benchmark algorithms in terms of both transmission delay and load-balance, and meanwhile ensuring paths reliability and security of the entire network.

This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz’s formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.

In the face of deeply exploring and exploiting marine resources, the Internet of Underwater Things (IoUT) networks have drawn great attention considering its widely distributed low-cost and easy-deployment smart sensing nodes. However, given the hostile underwater environment, it is critical to conceive energy-efficient information collection because of limited underwater energy supply and inefficient artificial recharge methods. Characterized by high flexibility and maneuverability, autonomous underwater vehicles (AUVs) are regarded as a promising solution for information collection in the IoUT relying upon delicate AUVs’ trajectory and information collection strategy design with the spirit of balancing their energy consumption and information processing capability. In this article, we propose a heterogeneous AUV-aided information collection system with the aim of maximizing the energy efficiency of IoUT nodes taking into account AUV trajectory, resource allocation, and the Age of Information (AoI). Moreover, based on the particle swarm optimization (PSO), we obtain the trajectory of AUVs with low time complexity. Additionally, a two-stage joint optimization algorithm based on the Lyapunov optimization is constructed to strike a tradeoff between energy efficiency and system queue backlog iteratively. Finally, simulation results validate the effectiveness and superiority of our proposed strategy.

Relying on offloading computation tasks to the network edge, ultra dense networks (UDNs) are capable of providing delay-aware service to nearby users. Meanwhile, software defined networking (SDN) is deemed as an effective technology to ease the management of infrastructure plane and control plane in UDNs, which is termed as SDN-based ultra dense networks. Specifically, the centralized SDN controller is capable of managing the whole network globally. With the increasing demands for various applications as well as the limitation of computation, storage and communication resource, how to allocate spectrum resource appropriately is imperative. In this article, we mainly show solicitude for spectrum sharing and edge computation offloading problems in SDN-based ultra dense networks, constituted of various macro base stations (MBSs), small-cell base stations (SBSs) and user equipments (UEs). To address this issue, we propose a second-price auction scheme for ensuring the fair bidding for spectrum rent, which enables the MBS edge cloud and SBS edge cloud to occupy the channel in cooperative and competitive modes. Moreover, the MBS edge cloud is termed as the buyer, and the SBS edge clouds are the sellers who sell the offloading resource to the MBS edge cloud. To be specific, the spectrum sharing and computation offloading scheme is executed in the SDN controller, and the controller is responsible for distributing spectrum allocation instructions to the infrastructure plane. Finally, experimental results validate the effectiveness of our proposed scheme in SDN-based ultra dense networks.

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

The autonomous underwater vehicle (AUV) has gradually become an important platform for performing various underwater tasks. Due to the shortcomings resulting from a single AUV's poor detection, information processing and moving capabilities, more and more tasks are completed in a cooperative manner by multiple AUVs. However, most of the existing works do not consider security factors in the process of multi-AUV cooperation. In this paper, we propose a novel cooperative tracking scheme towards an underwater moving target, performed by an intelligent AUV swarm. In this scheme, a cooperative multi-agent reinforcement learning (MARL) based tracking algorithm is proposed following a centralized training with distributed execution (CT-DE) manner. After centralized training in the designed secure private network, no information sharing is required during the mission execution. This feature ensures the security of the whole system, especially in a complex confrontation scenario. In addition, we build models of the AUV underwater dynamics and the target sonar detection, which make the algorithm applicable to real target tracking enabled AUV swarms. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we design an end-to-end AUV control algorithm. Simulation results validate that the proposed algorithm can achieve competitive performance in tracking success rate and tracking stability against baselines, while ensuring the security of the entire system.

Autonomous underwater vehicles (AUVs) have been envisaged as a key enabler for empowering the Internet of Underwater Things (IoUT) networks to address the challenge of ever-increasing demand of ocean exploration. However, the energy constraint of AUVs' movement makes it challengeable to obtain full-space movement and extravagant information exchange considering complex underwater environment and hostile acoustic channel characteristics. For the sake of enhancing the sustainability of the power supply, it is significant to design a green underwater information collection scheme for beneficially utilizing the maneuverability of AUVs. In this paper, we propose a heterogeneous multi-AUV aided underwater information collection scheme for optimizing the unit energy consumption under the constraint of the age of information (AoI). Moreover, the limited service M/G/1 vacation queueing system is used to model the process of information exchange, where the steady-state distribution and the waiting time of queue are derived. Finally, simulation results show the effectiveness of our proposed scheme and low-complexity solution, which outperform single-AUV scheme in terms of both energy efficiency and AoI.

In recent years, unmanned aerial vehicles (UAV) have been widely adopted to support complex target tracking tasks for military and civilian applications, especially in open and unknown environments. In practical cases, the moving trajectory of the target cannot be known to the UAVs in advance, which brings great challenges to UAVs to realize real-time and effective tracking. In addition, the limited tracking ability of a single UAV can hardly meet the requirements of a high tracking success rate. To deal with these problems above, this paper establishes a multi-UAV cooperative target tracking system. Besides, a deep reinforcement learning (DRL) based algorithm is designed to enable UAVs to make flight action decisions intelligently to track the moving air target, according to the past and current position information of the target only. To further increase the detection coverage of the UAV network when tracking, spatial information entropy is introduced to the reward designing in this algorithm. Simulation results validate that the proposed algorithm yields impressive target tracking performances, and significantly outperforms several common DRL baselines in terms of the tracking success rate. The convergence of the algorithm is also verified by the simulations.

Vehicular fog computing (VFC) has been expected as a promising paradigm that can improve the computational capability of vehicles, where vehicles can share their idle computing resource among each other. Considering the limited computational capability of a single vehicle and the short vehicle-to-vehicle (V2V) link duration, binary task offloading may suffer from the long execution time and the V2V link interruption, which may not be appropriate for some computation-intensive tasks. V2V partial computation offloading is expected to be a promising solution where tasks are divided into several parts and executed in multiple neighboring vehicles. However, due to the high-dynamic vehicular environment, it is challenging to design a scheme that can determine the service vehicles and the computing resource allocation both in local on-board CPU and in service vehicles for offloading tasks. To deal with these problems above, this paper develops a novel V2V partial computation offloading scheme and evaluates the service availability of neighboring vehicles in terms of their idle computing resource and the vehicle mobility. Moreover, the V2V partial offloading problem is formulated as a sequential decision making problem and solved by our proposed algorithm based on deep reinforcement learning (DRL). Finally, simulation results validate the effectiveness of our proposed mechanism.

In this paper, we propose a hierarchical information acquisition system composed of a marine stationary sensor layer and an autonomous underwater vehicle (AUV) motion layer. Specifically, in the sensor layer, we design an energy-efficient clustering protocol based on the improved K-Means algorithm (ECBIK), which can implement uniform classification and select the cluster head dynamically according to energy awareness. Compared with the traditional K-Means and LEACH algorithm, our method achieves lower energy consumption and higher node survival rate, which can balance the energy load effectively to extend the life of the network. Additionally, in the AUV motion layer, we define the rotation-angle of AUV and analyze its influence quantitatively for the AUV information collection. Meanwhile, a novel Ant Colony (ACO) algorithm based on Markov Reward Process (MRP) is proposed for AUV path planning. As the simulation experiments indicate, our algorithm can achieve shorter distance, smaller angle, and faster convergence speed in path optimization.

This paper considers value based information collection for underwater acoustic sensor networks (UWASNs). In the considered system, the sensor nodes collect, store and update monitoring information with an initial value related to associated events. The value of information (VoI), however, decays with time. An autonomous underwater vehicle (AUV) is dispatched to retrieve data from the sensor nodes through acoustic communication. Our objective is to find the optimal traversal path for the AUV to maximize the VoI of the whole network. To achieve this goal, we first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environment, based on which the expression of the total VoI is derived. Then, we formulate the problem as a combinatorial optimization problem. We provide an optimal solution for this problem based on the branch and bound (BB) method, in which the lower bound (LB) and upper bound (UB) calculation strategies are specifically designed. A near-optimal heuristic algorithm based on the ant colony method is also adopted for further reducing computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

We examine the problem of Value of Information (VoI) based underwater information collection, which is the essence of many underwater applications such as depth surrounding oil platforms, monitoring of algal blooms and so on. Even if the information collection work has been carried out much in the terrestrial scenario, due to complicated physical, technological and economic differences between the terrestrial and underwater cases, it is not feasible to simply apply the existing terrestrial tricks. The existing cooperative Autonomous Underwater Vehicle (AUV) working paradigms are limited to omniscience of communication channel information among the AUVs, which is yet not practical in such a harsh communication environment. Therefore, we propose a contract based model which has little restriction on the communication channel to overcome information asymmetry and jointly optimizes energy consumption and VoI. Besides, we provide a concrete theoretical proof of the contract items and carry out a performance simulation which shows the mechanism we design is operative. At last, we summarize our work and give an insight of the future research directions.

As a promising technology to improve the computation experience for mobile devices, mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous increasing computation demands. In this paper, a mobile edge computing system consisting of multiple mobile devices with energy harvesting and an edge server is considered. Specifically, multiple devices decide the offloading ratio and local computation capacity, which are both in continuous values. Each device equips a task load queue and energy harvesting, which increases the system dynamics and leads to the time-dependence of the optimal offloading decision. In order to minimize the sum cost of the execution time and energy consumption in the long-term, we develop a continuous control based deep reinforcement learning algorithm for computation offloading. Utilizing the actor-critic learning approach, we propose a centralized learning policy for each device. By incorporating the states of other devices with centralized learning, the proposed method learns to coordinate among all devices. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves a better performance compared with discrete decision based deep reinforcement learning methods.

Despite the benefits of electric and autonomous vehicles, current in-vehicle networks lack a robust and feasible security framework that considers the authentication, confidentiality, and integrity for the communication of Electronic Control Units (ECUs). Although a centralized key management mechanism offers an efficient solution, the security fully relies on this centralized unit, which leads to a single point of failure problem. In this paper, we present a semi-centralized key management framework to secure in-vehicle networks. It provides a decentralized and dynamic key distribution during the vehicle's operation and considers different aspects such as ECU's broadcast communication, ECU manufacturing process and ECU authentication without the use of certificates from external third parties. Finally, the implementation and the simulation of our framework validate the feasibility and practical use of our approach.

For the next generation of communication systems, low latency is an urging requirement to satisfy the increasing computation requires. In response, mobile edge computing (MEC) with energy harvesting (EH) is a promising technology to achieve sustained improvement of the computation experience. However, the frequently varied harvested energy, coupled with variable computing tasks and changing computation capacity of servers, results in the high dynamics of the computation offloading problem. In order to get satisfactory computation quality for such a high dynamic offloading problem, devices should learn to make multiple continuous and discrete actions when optimizing the system performance, such as latency, energy efficiency, etc. In this paper, we propose a continuous-discrete hybrid decision based deep reinforcement learning algorithm for dynamic computation offloading. Specifically, the actor outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server. On the other hand, the critic outputs the discrete action (server selection) while also evaluates the performance of the actor for neural network updating. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves better performance compared with the discrete decision based deep reinforcement learning methods.

Vehicular computation offloading is a promising paradigm that improves the computing capability of vehicles to support autonomous driving and various on-board infotainment services. Comparing with accessing the remote cloud, distributed vehicle-to-vehicle (V2V) computation offloading is more efficient and suitable for delay-sensitive tasks by taking advantage of vehicular idle computing resources. Due to the high dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design an effective task offloading mechanism to efficiently utilize vehicular computing resources. In this paper, we investigate the computation task allocation among vehicles, and propose a distributed V2V computation offloading framework, in which wireless channel states and variation of idle computing resources are both considered. Specially, we formulate the task allocation problem as a sequential decision making problem, which can be solved by using deep reinforcement learning. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, we thus propose a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The performance of designed task allocation mechanism is validated by simulation results which reveal the effectiveness of our mechanism compared to the other algorithms.

The Internet of Underwater Things (IoUT) shows great potential in realizing the smart ocean. Underwater acoustic sensor networks (UWASNs) are the main existing form of IoUT but face with reliable data transmission problems. To tackle this issue, this article considers using the autonomous underwater vehicle (AUV) as a mobile collector to construct a reliable hierarchical information collection system while the Value of Information (VoI) is used as a main metric to measure the Quality of Information (QoI). We first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environments. Then, to construct a hierarchical architecture, we design a sink node (SN) selection scheme by jointly considering VoI conservation and energy load balancing. After that, we focus on AUV path planning with the objective of maximizing the VoI of the total network. We formulate the problem as a combinatorial optimization problem and provide an integer linear programming (ILP) model for this problem. An optimal algorithm based on the branch-and-bound (BB) method is proposed for seeking for the optimal solution, in which the lower bound and upper bound calculation strategies are specifically designed. Two near-optimal heuristic algorithms based on the concepts of the ant colony algorithm (ACA) and the genetic algorithm (GA) are also provided for further reducing the computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

Mobile-edge computing (MEC) with energy harvesting (EH) is becoming an emerging paradigm to improve the computation experience for the Internet-of-Things (IoT) devices. For a multidevice multiserver MEC system, the frequently varied harvested energy, along with changeable computation task loads and time-varying computation capacities of servers, increase the system's dynamic. Therefore, each device should learn to make coordinated actions, such as the offloading ratio, local computation capacity, and server selection, to achieve a satisfactory computation quality. Thus, the MEC system with EH devices is highly dynamic and face two challenges: 1) continuous- discrete hybrid action spaces and 2) coordination among devices. To deal with such problem, we propose two deep reinforcement learning (DRL)-based algorithms: 1) hybrid-decision-based actor-critic learning (Hybrid-AC) and 2) multidevice hybrid-AC (MD-Hybrid-AC) for dynamic computation offloading. HybridAC solves the hybrid action space with an improvement of actor-critic architecture, where the actor outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server, and the critic evaluates the continuous actions and outputs the discrete action of server selection. MDHybrid-AC adopts the framework of centralized training with decentralized execution. It learns coordinated decisions by constructing a centralized critic to output server selections, which considers the continuous action policies of all devices. Simulation results show that the proposed algorithms achieve a good balance between consumed time and energy, and have a significant performance improvement compared with baseline offloading policies.

Relying on the computation offloading technology, edge computing has shown potential in countless tasks processing in the industrial Internet of Things (IIoT), which is composed of multiple edge clouds and multiple IIoT devices. Nevertheless, with increasing demands for computation service, how to design reliable transmission mechanism and allocate proper computation resource has become bottlenecks. In this article, we propose a computation offloading mechanism based on two-stage Stackelberg game to analyze the interaction between multiple edge clouds and multiple IIoT devices. To be specific, the edge clouds are denoted as leaders who set the appropriate price for their computation resource. Besides considering the payment cost, the IIoT devices which are termed as the followers formulate their utility function by considering the social interaction information from the potential IIoT devices. The existence and uniqueness of the Stackelberg equilibrium are analyzed considering two possible cases, i.e., complete information and incomplete information. Moreover, two dynamic iterative algorithms are invoked for solving both problem models, respectively. Finally, experimental results show that our proposed scheme is conducive to seeking the appropriate price and computation requirement. Besides, social interaction information plays an important role in achieving a reasonable computation requirement for IIoT devices.

Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.

The tremendous increase of computation-heavy applications has posed great challenges in terms of enhanced service coverage and high-speed data processing in the Fifth Generation (5G) networks. As responding, the integrated fog and cloud computing (FCC) system has been expected as an efficient approach to support low-latency and on-demand computing services. This work considers the computing resource market in an FCC system operated by one cloud computing service provider (CCP) and multiple fog computing service providers (FCPs), in which the CCP shares its cloud computing resource among FCPs and itself to serve users with computational tasks. To facilitate the resource trading between the CCP and FCPs, a Stackelberg differential game based resource sharing mechanism is proposed. In this mechanism, performance discrepancy is introduced as a penalty factor to denote the mismatch between the resource supply and demand, which will encourage all computing providers (CPs) to make their trading decisions that can truthfully reflect their resource capacity and requirements. In addition, an evolutionary game based replicator dynamics is established to analyze the users' service selection among CPs. Based on the established hierarchical game framework, interactions between user selection and computing resource sharing are investigated. The performance of the designed resource sharing mechanism is validated in the simulations, which also reveal the convergence and equilibrium states of user selection, resource pricing and resource allocation.

In ultra-dense networks, the increasing demand for wireless services has led to severe energy consumption problem. In this paper, we mainly focus on green communication and computation offloading in ultra-dense networks, constituted of different macro base stations and small-cell base stations. This paper jointly considers edge energy consumption and delay under the limited network resource for multiple users.To address this issue, we propose an efficient computation offloading scheme in multi- user multi-task scenario, and a cuckoo search algorithm is invoked for solving the computation offloading problem. To be specific, the global convergence analysis presents the validity of this computation offloading scheme. Finally, experimental results validate that our proposed scheme is conducive to improving the efficiency of entire system in ultra-dense networks.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. However, some of them are incompetent in tackling with computation-intensive tasks due to limited processing capability and battery life. In this paper, we combine the mobile edge computing and traditional cloud computing techniques for offloading the tasks from multi-UAV systems. Specifically, we jointly optimize the task scheduling and resource allocation in the heterogeneous cloud architecture, where we strike a power-delay trade-off of the system relying on the queue theory and Lyapunov optimization, followed by its optimal strategy analysis in each time slot. Moreover, we conceive an iterative algorithm with a closed-form solution at each iteration round in order to reduce the computational complexity. Finally, numerical results demonstrate both the feasibility and effectiveness of our proposed scheme. This paper validates that the heterogeneous cloud structure can be the beneficial for improving quality-of-service performance of multi-UAV systems.

Satellite image is an important resource for weather forecast. It can indicate the evolution of weather systems and is beneficial in terms of guiding people to make accurate weather forecasting. However, the use of satellite images is encountered with the dilemma of such as small data volume and of poor real-time performance. Hence it is important to make accurate prediction for satellite images. The goal of satellite image prediction is to predict the next few images of the image sequence. Essentially, it is a a spatiotemporal sequence prediction problem, where the prediction of satellite images is difficult due to its large-scale observation area. In this paper, we propose a generative adversarial networks-long short-term memory (GAN-LSTM) model for the satellite image prediction by combining the generating ability of the GAN with the forecasting ability of the LSTM network. For evaluation, we conduct our experiments on the FY-2E satellite cloud maps. In addition, we use a score correct rate (CR) to measure the degree of similarity between predictions and ground truth. Experiment results show that the proposed GAN-LSTM network is capable of efficiently capturing the evolution rules of weather systems, which outperforms the traditional autoencoder-LSTM.

In this paper, we propose a distributed detection system for hierarchical information acquisition based on autonomous underwater vehicle (AUV) and underwater fixed sensor networks. Different from the previous information collection systems, where the AUV traverses each node to obtain information, we propose a layered network architecture in this work, which is composed of an underwater fixed sensor networks layer and an AUV information acquisition layer. Such information acquisition system does not need to modify the original underlying fixed sensor networks, resulting from its flexible deployability. Additionally, because of the power sensitivity of sensor nodes in underwater fixed sensor networks, an improved algorithm based on classical low energy adaptive clustering hierarchy (Leach) algorithm is proposed in this work. Simulation results validate that the proposed algorithm can effectively improve the life cycle of sensor networks. At the same time, for the AUV information acquisition layer, we propose an angle optimization path planning algorithm based on the ant colony algorithm, which effectively takes the angle and path length as joint optimization objects. Experiments show that introducing the angle optimization jointly not only helps to optimize the AUV rotation angle, but also contributes to improving the convergence of the algorithm.

Recently, increasingly heterogeneous wireless networks are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing technology paradigms. By achieving an efficient spectrum sharing among heterogeneous networks (HetNets), traffic offloading is a promising solution for boosting the capacity of traditional macro-cell networks. In this paper, a cognitive spectrum sharing and traffic offloading mechanism is proposed to realize the cooperation and competition between the macrocell base station (MBS) and small-cell base stations (SBSs). Under the cooperation mode, the MBS stops occupying a corresponding channel, and a selected SBS helps offload the traffic from the MBS by exclusively using this channel. To facilitate the offloading negotiation between the MBS and SBSs, we design a secondprice auction mechanism, which presents positive allocative externalities, i.e., other uncooperative SBSs can benefit from the cooperation between the MBS and the SBS performing offloading. Meanwhile, the unique optimal biding strategies for different SBSs to achieve the symmetric Bayesian equilibrium are derived and obtained in this paper. The performance of the proposed cognitive traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MBS to achieve the maximum utility.

Recently, caching techniques have been regarded as efficient approaches to alleviate the data traffic loaded over backhaul channels, which can reduce the transmission delay and improve the quality and experience of video services. This work investigates a small-cell based caching system composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, different VSPs have their caching requirements, and the MNO, who manages and operates its small base stations (SBSs), will assign these SBSs' storage to VSPs for placing videos. Considering different video popularities and MUs' preferences of VSPs, the caching service brings different utilities to VSPs, as well as that providing caching service to different VSPs causes distinct costs to the MNO. However, such privacy information of utility and cost cannot be aware of among VSPs and the MNO. In addition, malicious VSPs may break the fairness of caching systems by requesting undeserved caching resource. Concerning these problems above, this paper designs a secure caching mechanism based on double auction, which can encourage both the MNO and VSPs to truthfully report their acceptances and requirements of caching resource, respectively. Moreover, the proposed caching mechanism ensures the efficient operation of market by maximizing the social welfare. The performance and economic properties of the designed caching mechanism are validated with simulation results.

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs' storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs' caching utilities and MNO's caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

The unprecedented growth of the volume of mobile data calls for novel approaches that improve the sharing of data allowances among mobile users with diverse needs. Specifically, the Wi-Fi hotspot function of current smartphones allows mobile-to-mobile offloading, but requires fast and efficient transactions between mobile users. Thus we propose an auction-based approach to allow the transfer of data allowances between mobile users with excess and deficits of data allowances, together with a cognitive approach to access the needed information about the system. The objective is to optimize the income of “sellers” and satisfy the needs of the other mobile users. Analytical and simulation results are presented, showing that by taking advantage of mobile users’ behaviors, and of varying demands of data allowance selling and buying, the cognitive auction and data allocation mechanism can significant improve the overall performance of the mobile data allowance transaction system.

Currently, The unprecedented increasing of mobile data traffic challenges the performance of current cellular networks. To meet this explosive demands of mobile traffic, the mobile data offloading technology has been proposed to alleviate the traffic load by moving traffic load of cellular networks to other wireless networks provided by infrastructures such as small-cell base stations. In this work, an infrastructure-free offloading method is proposed, which realizes the data transaction among mobile users by applying the hotspot function of smartphones. In this transaction, mobile users with redundant data perform as accessible Wi-Fi hotspots, and sell their mobile data to users with data requirements. Considering the scenarios with multiple data sellers, a networked auction model is introduced to model the process of data transaction. Additionally, high efficient data allocation mechanisms are designed in this work, which decide how to schedule the data transaction in different time slots, based on the establish edauction model. Simulation results indicate that introducing the prediction information of user behaviors can effectively improve the performance of data allocation, and achieve a high efficient data transaction operation.

Recently, hybrid satellite-terrestrial networks (H-STNs) are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing and interference control technology paradigms. By achieving an efficient spectrum sharing among H-STN, traffic offloading is a promising solution for boosting the capacity of traditional cellular networks. In this paper, a software-defined network-based spectrum sharing, and traffic offloading mechanism is proposed to realize the cooperation and competition between the ground base stations (BSs) of the cellular network and beam groups of the satellite-terrestrial communication (STCom) system. Assume that all BSs are operated by the same mobile network operator (MNO). Under the cooperation mode, all the BSs stop occupying a corresponding channel, and a selected beam group of the satellite helps offload the traffic from the BSs by exclusively using this channel. To facilitate the offloading negotiation between the MNO and satellite, we design a second-price auction mechanism which presents positive allocative externalities, i.e., other uncooperative beam groups of the satellite can benefit from the cooperation between BSs and the beam group performing offloading. Meanwhile, the unique optimal biding strategies for different beam groups of the satellite to achieve the symmetric Bayesian equilibrium as well as the expected utility of the MNO are derived and obtained in this paper. The performance of the proposed traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MNO to achieve the maximum expected utility.

In this paper, we consider a scenario where the satellite-terrestrial network is overlaid over the legacy cellular network. The established communication system is operated in the millimeter wave (mmWave) frequencies, which enables the massive antennas arrays to be equipped on the satellite and terrestrial base stations (BSs). The secure communication in this coexistence system of the satellite-terrestrial network and cellular network through the physical-layer security techniques is studied in this paper. To maximize the achievable secrecy rate of the eavesdropped fixed satellite service, we design a cooperative secure transmission beamforming scheme, which is realized through the satellite's adaptive beamforming, artificial noise, and BSs' cooperative beamforming implemented by terrestrial BSs. A non-cooperative beamforming scheme is also designed, according to which BSs implement the maximum ratio transmission beamforming strategy. Applying the designed secure beamforming schemes to the coexistence system established, we formulate the secrecy rate maximization problems subjected to the power and transmission quality constraints. To solve the nonconvex optimization problems, we design an approximation and iteration-based genetic algorithm, through which the original problems can be transformed into a series of convex quadratic problems. Simulation results show the impact of multiple antenna arrays at the mmWave on improving the secure communication. Our results also indicate that through the cooperative and adaptive beamforming, the secrecy rate can be greatly increased. In addition, the convergence and efficiency of the proposed iteration-based approximation algorithm are verified by the simulations.

Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users' psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user's choice of security setting. In this paper, a game theoretic framework is established to model users' interactions that influence users' decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users' strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users' relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.

We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, socially- aware mobility models are also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.

In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this paper, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations and small-cell base stations (SBSs). We explore a scenario where SBSs' capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations.

In this paper, we considers the resource allocation problems for video transmission in space based information networks. The queueing system analyzed in this work is composed of multiple users and a single server. To minimize both of the time average cost and delay of the system, and subject to the constraint that the queues in the system must be stable, we introduce a predictive backpressure algorithm into the consideration of resource allocation to make decision on which packets to be served first. Meanwhile, a multi-resolution wavelet decomposition based backpropagation neural network for the prediction of video traffic is designed in this paper. Performances of the proposed video traffic prediction system and resource allocation scheme are analyzed in the simulations. Results indicate that the prediction accuracy for the video traffic is improved according to the proposed prediction system, and the delay of the queueing system can be reduced through this prediction based resource allocation.

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.

In space-based networks, the data relay satellites can assist low-earth-orbit satellites in relaying data to other satellites or the ground station and improve the real time system throughput. To take full advantage of transmission resource of the cooperative relays, this paper proposes a multiple access and resource allocation strategy, in which relays can receive and transmit simultaneously according to channel characteristics of space-based systems. Based on the queueing theoretic formulation, the stability of the proposed protocol is analyzed and the maximum stable throughput region is derived, which would provide the appropriate guidance for the design of the system optimal control. Simulation results exhibit multiple factors that affect the stable throughput and verify the theoretical analysis.