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.

It is significant to employ multiple autonomous underwater vehicles (AUVs) to execute the underwater target tracking task collaboratively. However, it's pretty challenging to meet various prerequisites utilizing traditional control methods. Therefore, we propose an effective two-stage learning from demonstrations training framework, FISHER, to highlight the adaptability of reinforcement learning (RL) methods in the multi-AUV underwater target tracking task, while addressing its limitations such as extensive requirements for environmental interactions and the challenges in designing reward functions. The first stage utilizes imitation learning (IL) to realize policy improvement and generate offline datasets. To be specific, we introduce multi-agent discriminator-actor-critic based on improvements of the generative adversarial IL algorithm and multi-agent IL optimization objective derived from the Nash equilibrium condition. Then in the second stage, we develop multi-agent independent generalized decision transformer, which analyzes the latent representation to match the future states of high-quality samples rather than reward function, attaining further enhanced policies capable of handling various scenarios. Besides, we propose a simulation to simulation demonstration generation procedure to facilitate the generation of expert demonstrations in underwater environments, which capitalizes on traditional control methods and can easily accomplish the domain transfer to obtain demonstrations. Extensive simulation experiments from multiple scenarios showcase that FISHER possesses strong stability, multi-task performance and capability of generalization.

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.

Integrated sensing and communication (ISAC) technology is crucial for next-generation underwater networks. However, covering multiple users and targets and balancing sensing and communication performance in complex under-water acoustic (UWA) environments remains challenging. This paper proposes an interleaved orthogonal frequency division multiplexing-based MIMO UWA-ISAC system, which employs a horizontal array to simultaneously transmit adaptive waveforms for downlink multi-user communication and omnidirectional target sensing. A multi-objective optimization framework is formulated to maximize the product of communication rate and range (PRR) while ensuring sensing performance and peak-to-average power ratio (PAPR) constraints. To solve this mixed-integer nonconvex problem, a two-dimensional grouped random search algorithm is developed, efficiently exploring subcarrier interleaved patterns and resource allocation schemes. Numerical simulations under real-world UWA channels demonstrate the designed system’s superiority and effectiveness: our algorithm achieves 90% faster convergence than conventional exhaustive search with only a marginal 0.5 kbps•km PRR degradation. Furthermore, the proposed resource allocation scheme maintains robustness beyond the baseline allocation schemes under stringent PRR and PAPR constraints.

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.

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.

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 underwater vehicles (UUVs) have been widely used in various ocean applications, such as underwater exploration and data collection. And the underwater pursuit-evasion game (UPEG) is the key to efficient implementation of other tasks, holding significant research value. However, testing the UPEG task in real ocean environment is both costly and risky, and currently, UUV control algorithms that rely on specific environmental models struggle to complete the complicated UPEG task. To address above challenge, we propose UPEGSim, an UUV simulator specifically designed for the UPEG task. Built through Gazebo and robot operating system, UPEGSim provides a reinforcement learning (RL) environment to train UUVs for improving the intelligent performance in the UPEG task. Furthermore, we propose an efficient UPEG training framework (ETFDU), which includes multiagent decentralized training and execution techniques, scene transfer training methods, and offline RL techniques based on decision transformer, to facilitate efficient UUV training. Through training on the UPEG task in UPEGSim, we validate the effectiveness and feasibility of the proposed UPEGSim simulator and the ETFDU training framework.

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.

Integrated sensing and communication (ISAC) technology is a promising contender for the future Internet of Underwater Things (IoUT). However, the complexity of underwater acoustic (UWA) channels and the randomness of ISAC signals may pose challenges to underwater communication and sensing. To address this issue, this paper investigates a novel communication-assisted bi-static sensing scheme capable of facilitating underwater multi-node collaboration using orthogonal frequency division multiplexing (OFDM), which refers to as UWA-OFDM-ISAC. Moreover, two efficient receivers are designed based on compressed sensing to enhance communication and sensing performance. In this paper, we first portray the UWA-OFDM-ISAC system model and emphasize that the Doppler and symbols estimated at the communication side are beneficial in enhancing the bi-static sensing performance. To estimate doubly spread UWA channels, an orthogonal matching pursuit-based interference cancellation channel estimation method is developed, which decouples Doppler and delay in OFDM signals and significantly reduces the parameter search dimension. Furthermore, we propose an enhanced detection algorithm based on matching pursuit, which can exploit sparse multipath information of echoes to improve target detection performance under doubly spread channels. The detection probability is improved by more than 30% at the 10−2bit error rate level compared with the energy detector. Finally, simulation results illustrate the effectiveness of the proposed UWA-OFDM-ISAC and demonstrate that the designed receivers have significant advantages relative to various existing algorithms.

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.

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.

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) 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 recent years, the Internet of Underwater Things (IoUT) has garnered significant attention owing to its potential in ocean exploration and monitoring. However, environmental erosion and limited energy can cause node failures, leading to routing voids, communication congestion, and even IoUT breakdowns. Addressing these challenges, this work considers a node repair scheme for multiple autonomous underwater vehicles (AUVs) to search and repair faulty nodes to ensure the stable operation of the IoUT networks. Moreover, AUVs should adapt automatically to the unknown environment, working in cooperative or separative modes to balance repair efficiency and coverage. We propose a multiagent reinforcement learning-based AUV-assisted node repair (RANR) scheme, which considers limited underwater communication and scheduling between AUVs. To further enhance work efficiency, we introduce area information entropy to reduce redundant coverage among AUVs. Simulation results demonstrate that the RANR scheme is highly applicable to different working conditions.

In this article, we investigate the pursuit-evasion game of multiple autonomous underwater vehicles (AUVs) in a complex ocean environment. The pursuer AUVs need to optimize their trajectories to avoid obstacles and dangerous vortex regions in the environment in order to pursue the escaper AUV. Both the pursuer and escaper can sense each other with limited detection capabilities for further pursuit or escape. As the underwater pursuit-evasion (UPE) game is a high-dimensional NP-hard problem, we innovatively transform it into a finite-horizon Markov game process and propose a decentralized training and decentralized execution efficient training framework based on the offline reinforcement learning. During the training process, we propose multiagent independent soft actor–critic to facilitate policy improvement and generate the offline data set, and propose multiagent independent decision transformer for model training in the UPE game. Extensive simulations demonstrate the scalability and generalization ability of our proposed training framework, which can achieve excellent performance in the UPE games under different conditions and environments with only a few AUVs participating in policy improvement to generate the high-quality offline data set.

Considering the wide-area distribution and limited transmission power of sensing devices in the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles (AUVs) to collect data is considered a promising solution. While most existing AUV-assisted data collection schemes primarily focus on enhancing data collection throughput and identifying the shortest path, they often overlook the influence of the underwater environment on AUV and the timeliness of data collection. In this article, we design a multi-AUV-assisted data collection system, in which AUVs select their own target devices to collect data according to the data upload urgencies of IoUT devices. Considering the disturbance of turbulent ocean environment and the limited energy of AUV, we propose an environment- and energy-aware AUV-assisted data collection scheme. This scheme aims to conduct path planning for multiple AUVs based on perceived environmental information, including turbulent fields and device statuses. The primary goals are to maximize the sum data collection rate and total data throughput, minimize AUV energy consumption, reduce the average data overflow times. To solve this high-dimensional NP-hard problem, we first model the problem as a Markov decision process, and propose a multiagent independent soft actor–critic to solve it. Extensive simulations validate the effectiveness and adaptability of our approach.

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient FL scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL’s convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.

Underwater covert communication (UCC) technology can prevent legitimate transmission from being intercepted upon by potential eavesdroppers while ensuring a certain rate at the receiver under the condition of underwater acoustic channels. Previous studies have focused on UCC designs that rely on fixed transmitters and receivers, with limited attention given to dynamic moving senders, such as the widely-used autonomous underwater vehicle (AUV). Therefore, the establishment of a secure link between the mobile AUV and the receiver remains unexplored. In this paper, we construct an AUV-aided UCC architecture. Specifically, leveraging the unique characteristics of the underwater environment i.e., time-variant channel, severe attenuation, and ambient noise, the AUV plans its trajectory from the settled start point to the destination, adjusting its transmission power for covert communications. Accounting for both green energy consumption and communication security, we develop a novel multi-objective deep deterministic policy gradient (MODDPG) framework for jointly optimizing AUV’s diving energy consumption as well as effective throughput under the covertness constraint. Moreover, we propose an active-trust mechanism at the receiving side to pose an extra safe guard. To handle this, an evolutionary game model between the receiver and eavesdropper is built. Simulations and numerical results demonstrate that our proposed method can achieve a Pareto-optimal solution for covert communications with rapid convergence speed. The evolutionary stable strategy (ESS) enables the receiver to attain superior benefits and security compared to other strategies.

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.

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.

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.

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.

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices’ wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.

Given the increasing attention paid to the security of underwater communications, covert communication system has been envisaged as a key enabler for empowering the marine information networks to address the challenge of ever-increasing demand of anti-eavesdropping. However, dynamic underwater hydrology environment and ambient noise make it substantially difficult to reduce the decoding error probability as much as possible on the premise of meeting the concealment requirements. In this paper, we first build up underwater covert communication (UCC) system and analyze its secrecy performance at the physical (PHY) layer. Moreover, we propose a dynamic power-threshold based bargaining game model to preserve the receiver’s concealment, while ensuring high receiver-side signal-to-interface-noise ratio (SINR). The simulation results show the detection probability at the equilibrium of our model is optimal, and thus it is capable of both overcoming the dynamic changes and of increasing the system’s stability significantly by analyzing the time discount factor.

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.

Given the proliferation of the massive machine type communication devices (MTCDs) in beyond 5G (B5G) wireless networks, energy harvesting (EH) aided next generation multiple access (NGMA) systems have drawn substantial attention in the context of energy-efficient data sensing and transmission. However, without adaptive time slot (TS) and power allocation schemes, NGMA systems relying on stochastic sampling instants might lead to tardy actions associated both with high age of information (AoI) as well as high power consumption. For mitigating the energy consumption, we exploit a pair of sleep-scheduling policies, namely the multiple vacation (MV) policy and start-up threshold (ST) policy, which are characterized in the context of three typical multiple access protocols, including time-division multiple access (TDMA), frequency-division multiple access (FDMA) and non-orthogonal multiple access (NOMA). Furthermore, we derive closed-form expressions for the MTCD system’s peak AoI, which are formulated as the optimization objective under the constraints of EH power, status update rate and stability conditions. An exact linear search based algorithm is proposed for finding the optimal solution by fixing the status update rate. As a design alternative, a low complexity concave-convex procedure (CCP) is also formulated for finding a near-optimal solution relying on the original problem’s transformation into a form represented by the difference of two convex problems. Our simulation results show that the proposed algorithms are beneficial in terms of yielding a lower peak AoI at a low power consumption in the context of the multiple access protocols considered.

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.

At present, the development and exploration of the ocean are blossoming, but the maritime communication coverage still remains limited. By deploying unmanned aerial vehicle (UAV) mounted relay nodes between shore base stations and vessel users, the off-shore communication coverage and transmission efficiency can be substantially enhanced. Considering the specific transmission characteristics of air-sea and of air-shore channels and time-varying traffic of maritime information services, we formulate a minimum-maximization optimization problem of link capacity, where both the deployment of UAV-mounted relay node and the configuration of communication resources are optimized. To address this non-convex problem, we propose a particle swarm based algorithm, which is capable of three-dimensional position, antenna direction and time slot allocation scheme joint optimization. The simulation results demonstrate the high efficiency and reliability of our proposed algorithm in diverse offshore relay scenarios with different coastal environments, vessel distributions and network traffic.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

Nowadays the privacy issue arising in data mining applications has attracted much attention. In the context of distributed data mining, a major concern of the participant is that its privacy may be disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally causes a negative effect on the mining result. Thus there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. We propose a game theoretical approach to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.

The Link-a-Pix puzzle, which is also known as Piczle, PathPix, Pictlink, Number Net, or Paint by Pairs, is a popular picture logic puzzle game where the player paints a grid by linking the hint points with number-color labels to obtain the solution as a pixel art picture. In this article, we propose a joint depth first searching and linear programming aided algorithm for the sake of automatically solving the Link-a-Pix puzzle. The experiments implemented on 40 puzzles with various types verify the effectiveness and feasibility of our proposed solver, which is conducive to both designing and solving the Link-a-Pix puzzles and related applications.

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.

Deploying unmanned aerial vehicles (UAVs) as flying base stations (BSs) is a promising solution to alleviate the burden of communication infrastructure during the peak-traffic hours. However, when a UAV is deployed as a flying BS to serve the vehicle users in the hotspot, and the vehicle-to-vehicle (V2V) communication is introduced to further improve the network capacity, the performance analysis and optimization problems have not gained well investigated. In this paper, aforementioned problems are carefully studied from a statistical point of view, where the system performance is captured by the users' successful service probability. Specifically, we first derive the successful service probability for the UAV-to-vehicle (U2V) transmission. Meanwhile, taking those important factors, i.e., vehicle mobility and social proximity, into account, we estimate the successful service probability for the V2V transmission. The average successful service probability for the considered scenario is then derived. Based on the mathematical analysis results, we further improve the system performance by adjusting the UAV's altitude position, where the UAV deployment problem is formulated as a service probability maximization problem. To find the optimal solution, a particle swarm optimization algorithm is proposed. Finally, numerical simulations are conducted to verify the theoretical analysis and the efficiency of our proposed scheme.

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.

Recently, the technology of underwater wireless sensors networks (UWSNs) has received more attention on the exploitation of marine resources. However, underwater acoustic communication is still the only reliable means of ocean communication, which is entirely different from the terrestrial scene. In this paper, we propose Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Moreover, we propose an anti-void mechanism to solve the void region dilemma. Simulation results show that QLACO outperforms Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR) and the depth-based protocol (DBR) in terms of PDR, energy consumption and latency.

Multi-access edge computing (MEC) is viewed as a promising technology to improve the real time video service in vehicular networks. However, in the traditional vehicular networks, the road side units (RSUs) are usually only equipped with communication modules, and the unmanned aerial vehicles(UAVs) are seldom used. In this paper, a new UAV-aided time-varying vehicular network is introduced for vehicle users (VUEs) to obtain better experience, where the RSUs and the UAV are equipped with MEC servers for the real time video transcoding. Considering that the video service always lasts for a period of time, we investigate the user association policy from a long-term perspective. Specifically, to characterize the time-varying features of communication links and the heterogeneity of available resources, we theoretically derive the achievable video chunks and link reliability based on the vehicle mobility model and content caching model. Then, the user association problem is formulated as the utility optimization problem, where both the VUE’s quality of experience (QoE) and handover cost are taken into consideration. Furthermore, we propose an improved Dijkstra algorithm to solve the original NP-hard problem after it is transformed to a shortest path selection problem. Finally, by numerical results, we verify that the proposed scheme outperforms existing schemes in terms of the VUE’s QoE and the handover numbers.

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.

Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

This article presents the comprehensive framework for a hybrid-driven underwater vehicle-manipulator system (HD-UVMS) to grasp marine products on the seabed. The purpose of the proposed hybrid-driven propulsion system is to improve the swimming ability of the HD-UVMS by using thrusters and enhance the stability of its pose adjustment mechanism via two unique long fin propulsors. The control mode for the thrusters and long fin propulsors is based on a fuzzy logic control method. Subsequently, a lightweight manipulator is developed to grasp marine products. The open-closed angle and current controls for the gripper help to avoid damaging marine products. A vision system is installed to enable the HD-UVMS to gradually approach marine products with the aid of monocular vision and grasp them with the aid of binocular vision. A detailed method for monocular passive ranging and stereo matching, in accordance with real-time metrics, is elaborated. Finally, relevant experiments are conducted in an indoor pool and under real sea condition to assess the effectiveness of the proposed framework. Note to Practitioners-The motivation behind this article is the design of an underwater vehicle-manipulator system that can grasp marine products on the real seabed and perform other underwater intervention tasks. Currently, the predominant method of fishing for marine products relies on human divers, which has disadvantages for human divers' health due to the long periods of time spent working underwater. In order to further study the problem, this article develops a hybrid-driven underwater vehicle-manipulator system (HD-UVMS) to work in a real seabed environment. A hybrid-driven motion control framework is presented using the thrusters to achieve effective cruising and searching for marine products and long fin propulsors for the fine pose adjustment required to grasp marine products. The proposed lightweight underwater manipulator can grasp marine products on the seabed with the aid of a vision system. A series of experiments suggests that the HD-UVMS is practical and valid.

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed Q-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed Q-learning based power allocation algorithm.

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.

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.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

In order to beneficially exploit the scarce wireless spectral resources, spectrum sharing between communication and radar systems has become a promising research topic. However, traditional network association strategies may not result in efficient hybrid communication and radar systems. We circumvent this problem by formulating a partially observable Markov decision processes (POMDP) aided network association scheme, where the radar user acts as the primary user (PU), while the cognitive communication user is the secondary user (SU). For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, the communication user is configured for adaptively selecting its underlay or overlay access mode. Moreover, a low-complexity near-optimal reinforcement learning algorithm is proposed for the co-design by considering both its complexity and feasibility. Finally, we quantify the performance of our proposed POMDP based network association scheme.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. Recent researches on UAV systems feature in the dramatic augment of the variety and number of equipped sensors, which results in such an issue that multiple UAVs cannot afford to handle the big data generated by a range of sensors in the air. Considering this practical problem, in this paper, we propose a cloud-based UAV system which incorporates the computing capability of the terrestrial cloud into the UAV systems. Relying on proposed cloud-based UAV system, one critical theoretic issue is how to acquire the big data generated by the sensors while guaranteeing a stable operation state of the system. First, we analyze the cloud-based system's on-demand service ability as well as its impact on UAVs' control procedure. Second, the UAV cloud control system is modeled as a network control system. Moreover, the stable condition of the UAV cloud control system is derived, which reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of our theoretical analysis.

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme.

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

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.

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become an imperative focus. In this paper, we consider a maritime relay multicast system including a shore-based base station and several offshore relay nodes, and propose an energy efficient relay design scheme. Specifically, we formulate the relay design problem as a power minimization problem under users' quality-of-service (QoS) constraints, and the problem is approximated and solved using the feasible point pursuit successive convex approach. Furthermore, an iterative algorithm is proposed with exponential complexity. In order to reduce the computational complexity, a low-complexity distributed algorithm is conceived and its closed-form solution is derived. Finally, simulation results show that our proposed scheme is beneficial in terms of achieving a higher communication rate as well as of yielding a better energy efficiency.

Large scale network systems such as Internet, smart grids and social networks become an indispensable part of our daily life. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. In such attack-defense scenarios, it is particularly significant to make the best use of defenders' limited resources and capability. In this paper, we propose a networked Colonel Blotto game, where the attackers and defenders allocate the limited resources on network nodes, and their utility depends on certain network performance metrics, which are defined for evaluating the performance of the whole network system. Furthermore, considering the complexity of the equilibrium analysis in large scale network systems, a co-evolution based algorithm is proposed for obtaining the practical action sets as well as achieving the mixed-strategy Nash equilibrium. Finally, relying on three real- world network systems, i.e., computer networks, Internet of vehicles and online social networks, simulation results show the effectiveness and feasibility of our proposed model, which is conducive to the design, management and maintenance of real-world network systems.

Unmanned aerial vehicles (UAVs) cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which is beneficial in terms of both providing the seamless coverage as well as of improving the capacity for the users. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

Relying on mini wearable or implantable biosensors, the wireless body area network (WBAN) is capable of efficiently collecting as well as of analyzing human physiological information. It has shown great potential in terms of beneficially improving healthcare quality. However, due to stringent resource constraints of biosensors, traditional security schemes, i.e. the encryption and the authentication, may not do well in countering security threats. Moreover, they are not competent in protecting the network from inside attacks and deny of service (DoS) attacks. In this paper, we propose a sink node assisted lightweight intrusion detection mechanism for WBAN, where the sink node can periodically monitor the packet transmission and record the abnormality for further analysis. Our lightweight mechanism results in a very high true positive rate and an ultra-low false positive rate. Extensive analysis and simulations based on Castalia are conducted and verify the validity and efficiency of our proposed mechanism.

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.

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.

In view of the compelling applications in both military and civilian fields, wireless sensor networks (WSNs) have attracted an unprecedented focus on their easy configuration and low cost. Due to the openness of wireless media and constrained resources of WSNs, it is of paramount importance to timely discern the malicious intrusion and unauthorized manipulation. In this paper, we engage in providing an intrusion detection mechanism relying on a novel multi-criteria game. In our model, the interaction between potential attackers and defenders is formulated as a two-player non-zero-sum multi-criteria game, where multiple objectives, i.e. the information security, reputation and energy consumption, are considered when searching for the Pareto equilibrium. Moreover, a light weighting strategy is proposed in order to construct the payoff vector. Finally, simulation results and theoretical analysis show the effectiveness and feasibility of our proposed mechanism.

The wireless body area network (WBAN) is a new-type wireless sensor network which has a steep demand for improving energy efficiency and reducing packet delay. However, in a multi-priority environment, the current IEEE Std. 802.15.6 MAC protocol for WBAN may result in excess transmission delay and power consumption due to the selfishness of high-priority sensor nodes. To overcome the deficiency, in this paper, a contention-oriented node sleeping MAC protocol is proposed. The MAC protocol utilizes a contention orientation mechanism between different contention levels to achieve a fair resource allocation. Furthermore, the sleeping scheme of redundant nodes yields energy efficiency. Finally, simulation results show that our proposed protocol outperforms 802.15.6 MAC, AD-MAC as well as DTD-MAC protocols in terms of both packet delay and energy efficiency.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

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.

Mobile communication technology provides more service paradigms to social networks, allowing the development of mobile social networks (MSNs). An important scenario of MSNs is crowdsensing, which takes advantage of simple sensing and computation abilities on the portable devices of ordinary people, and fuses the sensing results to accomplish large-scale tasks. In crowdsensing, the integration of individual sensing data from users is of great significance, yet highly depends on the goal of tasks. In this paper, we propose a high-level distributed cooperative environmental state inference scheme based on non-Bayesian social learning, which can be applied to various crowdsensing tasks, e.g., traffic monitoring, air quality monitoring, and weather forecasting. In the proposed scheme, users exchange information with their neighbors and cooperatively infer the hidden state, which is the goal of the crowdsensing task but cannot be measured directly. We prove theoretically that every user is able to asymptotically learn the hidden state, even when the users locations and relationships keep changing, and when some users cannot observe signals or provide their own inferences. We also optimize the weight matrix in the fusion step by maximizing the learning speed.

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.

The proliferation of information technologies results in a complex network evolution of online social networks. Traditional model driven aided description cannot be appropriate for the dynamic evolution of social networks. However, in this paper, relying on the big data collected from a range of real-world online social networks, we try to explore the underlying evolution for online social networks. Firstly, we define a pair of big data driven similarity based utility models (U- models), i.e. the undirected U-model as well as the directed U-model, which can effectively reflect the statistical characteristics of online social networks. Secondly, we analyze the small-world property, scale-free property and high clustering coefficient property of our proposed U-models which consider nodes' similarity, popularity and asymmetry in a network. Finally, relying on three real-world big datasets, i.e. Sina Weibo, Tencent Weibo and Twitter, sufficient experiments show that the U-models outperform the traditional models in portraying the evolution statistical characteristic of online social networks.

Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. With the increasing complexity of application scenarios, the coordination of multiple UAVs has become a hot topic. However, the limited capability of UAVs make it hard to achieve stable and reliable control. Considering this practical problem, we propose a cloud-based UAV system. It extricates the computing and data storage from UAVs and utilizes the cloud to process the sensor data and to maintain the stable operation of multi-UAV systems. Firstly, we analyze the cloud-based system's on-demand service ability and its impact on UAVs' control procedure. Secondly, we propose a UAV cloud control system (CCS) which serves as a network control system. Moreover, the stable condition of the UAV cloud control system is derived. It reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of previous theoretical analysis.

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. Data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. Interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the classification accuracy and the preserved privacy.

How to improve the information diffusion coverage rate in socially-aware mobile networks has drawn great attention. To address this issue, the concept of the tie strength, the partial strength and the value strength were proposed in order to achieve a superior criterion for information diffusion. However, the previous works did not consider the existence of various patterns among the nodes in socially-aware mobile networks. In this paper, we propose the asymmetric normalization forms of the partial strength as well as the value strength. Moreover, we explore the essence of asymmetry and its influence on information diffusion relying on analyzing the characteristics of graph structures as well as information local traps. Simulation results on the real-world social network and on the mobile network verify that our proposed asymmetric normalization forms are beneficial to promoting information diffusion.

Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically, we commence with analyzing the topological role of the social strengths, i.e., tie strength, partial strength, value strength, and their corresponding symmetric as well as asymmetric forms. Then, we define two critical points for the cascade information diffusion model, i.e., the information coverage critical point (CCP) and the information heat critical point (HCP). Furthermore, based on the two real-world datasets, the proposed two critical points are verified and analyzed. Our work may be beneficial in terms of analyzing and designing the information diffusion algorithms and relevant control strategies.

How to enhance security and stability of the cyber physical systems (CPSs) becomes a critical issue. In this paper, we propose a solution from the perspective of users' information diffusion process in CPCs. Relying on the virus propagation model, we conceive an idle-carrier-idle (ICI) model to characterize the information diffusion. Moreover, `effective diffusion rate' is defined in order to benchmark the efficiency of users' information diffusion. Based on the complex network theory, the threshold of the information diffusion both in homogenous networks and in free-scale networks is derived. Furthermore, a pair of game models are proposed for efficiently controlling the information diffusion and then for enhancing the security of the system. Furthermore, the equilibriums of two proposed games are demonstrated. Finally, essential numerical analysis and simulations show the effectiveness and feasibility of our proposed models.

Device-to-device (D2D) communication is a promising technology to improve spectrum efficiency, energy efficiency, and transmission delay due to proximity of user equipments (UEs). With cooperative D2D communications, a UE needs to select an optimal peer to act as a relay to forward data to the base station (BS). Therefore, it is essential to take into account the social relationships among UEs during peer selection so as to enhance data privacy. In this paper, we investigate a dynamic social-aware peer selection problem by formulating it as a dynamic optimization problem and proposing the drift-plus-penalty ratio algorithm to solve it. Simulation results show that the proposed peer selection scheme outperforms other existing schemes while keeping the handover frequency lower than others.

Earth satellite networks are playing an increasingly important role in observation, surveillance and reconnaissance of specific targets or areas with dramatic growing of the demand for such services. The harsh and vulnerable space environment makes satellite nodes susceptible to a variety of attacks and prompts us to protect satellite networks by securing them with efficient defending strategy. In this paper, we propose a satellite scheduling problem in a vulnerable space environment. A set of complex operational constraints is imposed to make the best defending strategy between task requests and satellites under protection. To reduce the complexity, we decompose the original problem into two subproblems of satellite scheduling and satellite protection. A joint optimization algorithm is adopted to find near optimal solution. The results of extensive computational experiments show its effective and superior scheduling performance.

Relying on clustering and the cluster head selection algorithms, vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) based vehicular ad hoc networks (VANETs) play a critical role in intelligent transport system (ITS). However, the existing clustering and cluster head selection algorithms did not consider the influence of the vehicles' communication contents and their correlations. Specifically, the power-law characteristics of vehicle content demands are beneficial in terms both of achieving efficient clustering algorithm and selecting optimal cluster heads. In order to simulate the real vehicular communication scenarios, we commence with the mobility model design in this paper. Moreover, a novel clustering algorithm relying on content demands is proposed, which attracts vehicles to adopt V2V network through price advantage. Furthermore, based on the Fermi rule, i.e., one of the stochastic evolutionary strategies in complex networks, and evolution game, our cluster head selection algorithm is capable of representing more realistic vehicles' features, including selfishness, fairness and bounded rationality. Finally, the effectiveness and feasibility of our proposed algorithms are verified.

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 a cooperative network, the user equipment (UE) shares information for cooperatively achieving a common goal. However, owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on modeling the UEs' decision making behavior, namely as to whether they are willing/able to share genuine information, even though this directly affects the information credibility across the network. Hence, we propose a game theoretic framework for the associated information credibility modeling by taking into account the users' information sharing strategies and utilities. This framework is investigated under both a homogeneous model and a heterogeneous model. The spontaneous information credibility equilibria of both models are derived and analyzed, including the closed-form analysis of the homogeneous model based on a sophisticated evolutionary game model and on the reinforcement learning-based analysis of the heterogeneous model. Moreover, a credit mechanism is designed for encouraging the UEs to share genuine information. Experimental results relying on real-world data traces support our utility function formulation, while our simulation results verify the theoretical analysis and show that all the UEs are encouraged by the proposed algorithm to share genuine information with a probability of one, when a credit mechanism is invoked. The proposed modeling techniques may be applied in diverse cooperative networks, including classic wireless networks, vehicular networks, as well as social networks.

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.

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.

In a cooperative network, users share information with each other to achieve a common target. Due to the concerns of privacy and cost, users may be reluctant to share genuine information with each other, which incurs the information trustworthiness problem. Most of the existing research attempts have proposed various mechanisms targeting to enhance the information credibility in various scenarios. However, the users' information sharing inclinations have not been well considered and modeled, which closely determine the information credibility in the network. In this paper, we study a trustworthy situation in cooperative networks and utilize the concept of reputation to model users' behaviors. Specifically, we propose two reputation learning methods based on both the peer-to-peer Bayesian learning and the social non-Bayesian learning models, and also propose a trustworthiness learning method based on the posterior estimation. Finally, simulations are conducted to verify the correctness and effectiveness of our theoretical analysis.

The last decade has witnessed a booming era of wireless vehicular networks, supporting diverse road traffic services and applications. Information dissemination/sharing among vehicles is the fundamental goal of vehicular networks. Although diverse information dissemination/sharing mechanisms have been proposed in the existing literature, the physical layer outage performance of information sharing has not been analyzed. Against this background, in this paper, we study the outage probability of road traffic information sharing in underlay cognitive vehicular networks under both a general scenario and a specific highway scenario. The general scenario relies on the Nakagami-m channel, while the highway scenario is its special case associated with the Rayleigh fading channel. Moreover, we also invoke a real-world dataset containing the locations of Beijing taxis to conduct simulations, the results of which verify the accuracy of our theoretical analysis.

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.

The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless network, new infrastructure deployment for Super Wi-Fi is required. The fast developing Energy Harvesting (EH) techniques receive global attentions for their potential of solving the above power supply problem. It is a critical issue, from the user's perspective, how to make efficient network selection and access strategies. Unlike traditional wireless networks, the battery charge state and tendency in EH based networks have to be taken into account when making network selection and access, which has not been well investigated. In this paper, we propose a practical and efficient framework for multiple base stations access strategy in an EH powered Super Wi-Fi network. We consider the access strategy from the user's perspective, who exploits downlink transmission opportunities from one base station. To formulate the problem, we used Partially Observable Markov Decision Process (POMDP) to model users' observations on the base stations' battery situation and decisions on the base station selection and access. Simulation results show that our methods are efficacious and significantly outperform the traditional widely used CSMA method.

In a cooperative network the user equipment (UE) share information with each other for cooperatively achieving a common goal. While owing to the concerns of privacy or cost, UEs may be reluctant to share genuine information, which raises the information credibility problem addressed. Hence diverse techniques have been proposed for enhancing the information credibility in various scenarios. However, there is a paucity of information on the UEs' information sharing inclination, even though this directly affects the information credibility across the network. In this paper, we propose a general framework for the information credibility modelling of cooperative networks by taking into account the users' information sharing inclinations. Specifically, the utility functions of sharing both genuine and false information are defined. Based on this utility formulation and its closed-form analysis, the spontaneous information credibility equilibrium is derived. Our simulation results verify the accuracy of our theoretical analysis.

Self-managed smart devices with growing intelligence can optimize their own performances but pose potential negative impacts on others. The concept Cyber Physical Social System (CPSS), originated from Cyber Physical System (CPS), takes the social dimension into considerations and provides a new paradigm for social interactions between self-managed smart devices. In this paper, we focus on modeling the social interaction among smart devices, and conduct a case study in the autonomous WiFi scenario. Specifically, we propose an active interference measurement methodology reflecting both in-range interference and hidden terminal interference, and a coordinated power control based on the Nash bargaining is further formulated for interference reductions. A four-AP SWP testbed is implemented and deployed in a real office environment, where the social power control between VAs can bring great interference reductions for the APs with hidden terminal interference than the non-cooperative power control scenario.

Super Wi-Fi is expected to enable the Internet access everywhere in a country. Considering the infrastructure deployment issues, energy harvesting technology is a promising solution for power supply. Most of existing works focused on the energy scheduling from the network operator's point of view. In this paper, we study the access point selection strategy from users' perspectives and consider Super WiFi networks powered by solar energy harvesting. Although the network selection problem has been studied, the energy harvesting scenario has not been well investigated and the influence of battery condition has not been taken into account. In our work, we consider the utility of the access users is affected not only by the total number of accessed users, but also the battery condition. In order to formulate the battery states and user states, we incorporate the physical characteristics of solar cell, as well as the dynamic access behaviors of users through Markov Decision Process (MDP)model. By using the value iteration method, the set of optimal network selection strategies is obtained. Simulation results validate that our method has a remarkable performance improvement of utility over the myopic and random access strategies.

Wireless network's traffic capacity is one of the most important measurements of network performance. In a vulnerable network environment, attacks for network components can cause degradation of traffic capacity and lead to increasing network congestion and overall performance degradation. In this paper, we propose an effective protection resources allocation scheme which allocates limited protection resources of nodes based on assessment of vulnerability of network components. This scheme proves to be the best allocation strategy in respect to enhancing the quantity of network traffic capacity. The simulation results show that the proposed allocation scheme outperforms other traditional allocation schemes based on critical node analysis. A thorough analysis of traffic capacity and network vulnerability has also been provided.

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users' decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms.

Collaborative spectrum sensing is an effective method to improve detection rates in cognitive radio networks. However, it is vulnerable to spectrum sensing data falsification (SSDF) attacks when malicious secondary users (SUs) report fraudulent sensing data. In order to improve the robustness, numerous attack prevention schemes have been proposed to identify malicious SUs. Nevertheless, most of them neglect to incentivize SUs to send truthful reports. An incentive method based on peer-prediction is proposed to identify malicious suspects, punish attackers, and incentivize SUs to send truthful reports simultaneously for decision fusion. Moreover, continuous peer-prediction derived from the binary case is introduced, which is capable of preventing attacks in the continuous domain. Theoretical analysis and simulation results demonstrate that honest SUs are rewarded for accurate and truthful sensing results, while malicious SUs incur penalty for making falsified sensing reports. A significant improvement of detection rates is obtained by the proposed scheme when there are no more than half of malicious SUs conducting SSDF attacks.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology for improving network resource utilization, and cooperative communication technology is usually used to mitigate the interference caused by D2D communication. Due to the additional signal processing cost introduced by cooperative communication, the cellular links who have the exclusive usage right of the network spectrum can charge the D2D links a fee for spectrum usage to enhance their profit. In this paper, we propose a contract-based cooperative spectrum sharing mechanism to exploit transmission opportunities for the D2D links and meanwhile achieve the maximum profit of the cellular links. We first design a cooperative relaying scheme that employs superposition coding at both the cellular transmitters and D2D transmitters. The cooperative relaying scheme can maximize the data rate of the D2D links without deteriorating the performance of the cellular links. Then, we employ a contract-theoretic framework to model the spectrum trading process based on the cooperative relaying scheme, and derive the optimal power-payment contracts for the cellular links under both the cases that the private information (i.e., channel quality) of the D2D links is continuous and discrete using tools from continuous-and discrete-time optimal control theories, respectively. Analytic and numerical results confirm the efficiency of the proposed spectrum sharing mechanism.

Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using term frequency vectors to represent microblog texts will cause “sparse data” problem. Finding proper representations of microblog texts is a challenging issue. In this paper, we apply deep networks to map the high-dimensional representations of microblog texts to low-dimensional representations. To improve the result of dimensionality reduction, we take advantage of the semantic similarity derived from two types of microblogspecific information, namely the retweet relationship and hashtags. Two types of approaches, including modifying training data and modifying the training objective of deep networks, are proposed to make use of microblog-specific information. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.

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.

Cellular network is widely used and device-to-device (D2D)-assisted approaches have been proposed for improving performance on spectrum efficiency, overall throughput and energy efficiency. However, few of them have considered the downlink transmission for multiple concurrent devices from an energy efficiency perspective. In this paper, we focus on a D2D-assisted cellular communication in video stream sharing scenario. Two energy saving solutions for downlink transmission are proposed with constraint on D2D cluster's energy consumption. We take peak signal-to-noise ratio (PSNR) as the measurement for video quality and consider both the downlink transmission energy and reception energy. In particular, we propose the D2D cluster formation approach and the D2D caching performance both for the purpose of energy saving, with distributed merge-and-split algorithm adopted from the perspective of coalition game theory and a relaxation factor defined to give constraints on total energy consumption for each cluster. Both D2D cluster and D2D caching approaches are effective for energy saving for the BS combined with all user devices; however, D2D cluster brings an unfairness problem between the cluster head and other cluster nodes. Therefore, we compare the two approaches on energy saving performance as well as fairness measurement. Moreover, a centralized algorithm for D2D cluster is also proposed as a benchmark for the distributed D2D cluster algorithm. Simulation result shows considerable amount of energy saving in the proposed D2D cluster and caching assisted cellular network for video stream sharing problem.

The energy consumption of a base station (BS) has attracted much attention in the study of wireless communication. Device-to-device communication, which can be utilized to offload the traffic from the BS, provides an effective way to increase network energy efficiency. How to optimally coordinate users to redistribute the traffic so as to minimize the energy consumption is an important issue. In this paper, we study two problems that are critical to this issue. First, considering that relaying data to others incurs costs to the users and different users have different costs, we propose a contract theoretical approach to design the mechanism for pricing the contributions of users. The second problem is to make a proper matching between users who demand data and users who are willing to relay data. Matching theory is exploited to deal with this problem. Specifically, we consider both interference-free and interference scenarios and develop matching algorithms, which can achieve stable matching and weak stable matching, respectively. Simulation results demonstrate the effectiveness of the proposed algorithms.

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.

Collaborative spectrum sensing is an effective method to improve the detection rate in cognitive radio. However, it is vulnerable to spectrum sensing data falsification attacks. In order to improve the robustness, numerous attack prevention schemes have been proposed to identify malicious secondary users (SUs). Nevertheless, most of them neglect to incentivize SUs to send truthful reports. Therefore, an incentive method based on Private-Prior Peer-Prediction with approximate subjective priors is proposed to identify malicious suspects and punish attackers when falsifying the sensing data simultaneously. The theoretical analysis and simulation results demonstrate that honest SUs are rewarded by accurate and truthful sensing results while malicious SUs receive heavy loss for making falsified sensing results. Moreover, a significant improvement of detection rates is demonstrated when there are a large number of malicious SUs conducting cooperative attacks compared to the pure majority rule scheme.

In small cell networks, due to the small coverage of small cell access points (SAPs), handoffs may be executed frequently. Therefore, evaluating the utility that a user equipment (UE) can acquire from an SAP is of great significance. In this paper, different from traditional evaluation schemes, we propose a social interactive evaluation scheme. The UEs are allowed to share their local believes and fuse them in a non-Bayesian manner. One advantage of the scheme is that it allows UEs to evaluate an SAP that they do not connect to, based on which UEs can get prepared for handoff in advance. Both the theoretical analysis and simulation illustrate that UEs not connecting to an SAP is able to learn the real utility iteratively and accurately. Additionally, compared to UEs performing individual Bayesian estimation, UEs with the non-Bayesian scheme can learn the real utility faster if the signal cannot be observed in every iteration.

The issue of data pricing is becoming more important than before in order to build an internet ecosystem. In this paper, we consider a data redistribution market where users with extra data quota are able to sell data to users that have used up their data quota. We consider this market problem with multiple users on both sides, and analyze on two possible market environment to achieve market equilibrium: the exogenous pricing scenario and endogenous pricing scenario. The stable matching algorithm and message-passing algorithm are used respectively. Simulation results show that while exogenous pricing market achieves equilibrium at a certain market price, the endogenous pricing market achieves equilibrium for each pair with better overall performance although without stability.

Privacy issues arising in the process of collecting, publishing and mining individuals' personal data have attracted much attention in recent years. In this paper, we consider a scenario where a data collector collects data from data providers and then publish the data to a data user. To protect data providers' privacy, the data collector performs anonymization on the data. Anonymization usually causes a decline of data utility on which the data user's profit depends, meanwhile, data providers' would provide more data if anonymity is strongly guaranteed. How to make a trade-off between privacy protection and data utility is an important question for data collector. In this paper we model the interactions among data providers/collector/user as a game, and propose a general approach to find the Nash equilibriums of the game. To elaborate the analysis, we also present a specific game formulation which takes k-anonymity as the anonymization method. Simulation results show that the game theoretical analysis can help the data collector to deal with the privacy-utility trade-off.

How to enhance the network capacity is one of the most important issues. To achieve this, the existing works have focused on improving either the network structure or routing strategies with a common assumption of uniformly distributing the replicas of information among the nodes in the network. The nodes associated with information replicas are considered as source nodes (or server). However, for many networks such as the Internet, some nodes have much more traffics than the others, exhibiting an asymmetric phenomenon. In this letter, we study the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. Simulation results show that in homogeneous networks, most of the nodes can be the sources. While in heterogeneous networks such as the scale-free networks, only a small number of the nodes can be the sources. Moreover, an interesting phenomenon is observed that the optimal proportion of source nodes in Erdös-Rényi random network and Barabási-Albert scale-free network exhibits a power law relationship with the network size.

Current researches on topology control with cooperative communication (CC) in wireless ad hoc networks have focused on network connectivity, path energy-efficiency and node transmission power reduction. However, fault-tolerance related issues have not been adequately addressed. In this paper, we propose a CC-based scheme to achieve more efficient fault-tolerant topology control. We first definek-connectivity under the CC model and then design a distributed scheme for building at-spanner withk-connectivity of an arbitrary communication network. Simulation results confirm that the proposed scheme can tolerate node failures as well as exploit the advantage of CC to achieve path energy-efficiency and lower power consumption of the network.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of it have been studied, e.g., spectrum sharing, interference mitigation, etc, the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in cognitive femtocell network, and propose a two-tier pricing game theoretic framework with a dynamic pricing model. We first model the cognitive users' network access behavior as a 2-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm can quickly converge to the Nash equilibrium prices.

The limited sensor node energy and the large number of nodes with dynamic network topology information have always been the important design concerns in Wireless Sensor Networks (WSN). Node clustering is an effective way to tackle with the two issues by grouping the nodes into hierarchies in order to reduce communication distance and the amount of message. This paper mainly focuses on the unification of the node energy consumption in WSN. The distributions of the energy consumption for various scenarios in the hierarchical network are analyzed for the first time and two main reasons are found leading to the asymmetry of the energy consumption among nodes. One is the energy consumption from the communications between nodes and base station, and the other is that from the cluster head for receiving data from other nodes. It is concluded that the probability of the node acting as cluster head should depend on the distribution of the head's energy consumption, and a variable sampling space oriented to the potential number of cluster heads is established thereafter. Furthermore, a new clustering algorithm, the Segment Equalization Clustering based on Cluster Head Energy Consumption (SECHEC) algorithm is proposed, which can effectively improve the network lifetime and ensure the availability of the system within its entire lifespan.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.

Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the PU's licensed channels. If the SUs can be synchronous with the PU's time slots, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's exact communication mechanism, additional interference may occur. In this paper, we propose a dynamic spectrum access protocol for the SUs confronting with unknown primary behavior and study the interference caused by their dynamic access. Through analyzing the SUs' dynamic behavior in the primary channel which is modeled as an ON-OFF process, we prove that the SUs' communication behavior is a renewal process. Based on the Renewal Theory, we quantify the interference caused by the SUs and derive the corresponding closed-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS) and the secondary network's stability. Finally, simulation results are shown to verify the effectiveness of our analysis.

One critical issue in dynamic spectrum access of cognitive radio networks is the analysis of interference caused by Secondary Users (SUs). Most of the current works focus on mitigating the aggregated interference effects of SUs at Primary Users (PUs) in the physical layer. However, the interference is also dynamically related to the communication behaviors between PUs and SUs. In this paper, we analyze the interference caused by SUs in the MAC layer by taking into account the dynamic behaviors between PUs and SUs. Based on the ON-OFF primary channel state model, we derive the close-form expressions for the probability of interference caused by SUs and quantify the interference effect in two scenarios: slotted secondary network and non-slotted secondary network. We also discuss how to control SUs' access behavior such that the normal communication of PUs can be guaranteed. Finally, simulation results are shown to verify the effectiveness of our analysis.

One critical challenge for accurate localization with Received Signal Strength Indicator (RSSI) is the anisotropic environment, which causes the RSS-Distance Relationship (RDR) to vary spatially. To alleviate localization error caused by RDR anisotropy, most of existing works adopt multiple RDR algorithms. However, we have found that the arbitrary RDR selection in these algorithms can lead to large localization error. Moreover, localization accuracy can be further enhanced by utilizing information provided by more Access Points (APs). To address these problems, we propose a Probabilistic Neural Network based localization algorithm in this paper. The algorithm features two steps: Global Optimization and Regional Compensation, during which all APs exchange information about the Blind Node (BN) to locate it collaboratively. Simulation result shows that the proposed algorithm can achieve a localization accuracy 35% higher than that of multiple RDR algorithms.

This paper presents a cross-platform solution of smartphone-based mobile sink for wireless sensor networks, named uSink. With a cross-platform SD card, named uSD card, any smartphone with SD interface can be empowered with the capability to communicate with wireless sensor nodes. Furthermore, a middleware on mobile phone, named uSinkWare, is also designed to provide a typical mobile sink's functionalities, including sensor detection, topology monitoring, routing, sensor data collection and sensor control, etc. Thanks to Qt, uSinkWare can also work on many popular mobile platforms, such as Symbian, Maemo, Windows mobile and embedded Linux. uSink has been implemented, and its performance has been verified by experiments. The experiment results show that current high-end smartphones have enough capabilities to work as mobile sinks, and uSink can be regarded as an attempt on this aspect.

In this paper, a platform "uSensing" is proposed to support mobile smartphones accessing into wireless sensor networks(WSNs). Since phones have different platforms, e.g. operating systems and CPU processors, it is a challenge to provide a universal platform for smartphones. To solve it, we design novel hardware and software: 1) "uSD": a SD card with zigbee radio; 2) "uSinkWare": a software running on smartphones. The uSD card and uSinkWare consists of uSensing platform. With uSensing, any phones can communicate with WSNs and parse sensor messages. We demonstrate the proposed uSensing in Nokia 5800XM to connect with our WSNs testbed, and evaluate the performance of uSensing through measuring the phone CPU and RAM. The experiment results show that the uSensing can support the smartphones very well to access into the WSNs as the data sink.

In this paper, we present a 3D indoor navigation service system based on smartphones. To achieve an accurate indoor navigation system in a multi-storey building, it is necessary to determine the storey where the user is. By attaching an atmosphere pressure sensor on smartphones, the altitude information of the user can be obtained, and then, the storey where the user is can be calculated indirectly. With the information of storey, we project localization anchor nodes, e.g. WiFi-AP, of different storeys onto the storey where the user is, and calculate their projection distances based on received signal strength (RSS). To utilize the projection distance in 2D localization algorithms, the systematic error deviation from the storey deviation can be reduced. In addition, we develop a software running in a smartphone to implement our localization method, and provide a smartphone navigation service in our office building. Experimental results show that our method is more accurate than the traditional triangle localization method.

Packet level coding schemes are used to improve the transmission reliability and efficiency in data broadcasting applications, especially for wireless networks. However, existing coding schemes have large redundancy for packets are coded by a random way or some certain probability distributions. Although the Maximum Distance Separable (MDS) codes are designed without redundancy, they are not effective when the packet erasure probability is high. To reduce the coding redundancy, lots of work use the receiver side information to adjust the codes construction on-the-fly, which is more beneficial. However, these work have disadvantages:1) the coding redundancy is also large; 2) the feedback schemes have great affect on performance. In this paper, we design a robust feedback scheme and a novel "Rateless Online MDS Code"(ROME) to eliminate the coding redundancy. Our contributions include that our codes are throughput optimal codes without redundancy, and the analysis on theoretical finite field size bound to achieve throughput optimal codes. We also design the finite field construction to speed up the encoding and decoding process. Finally, we compare the performance between ROME and other existing codes, like RLC, LT, RT oblivious and SLT codes, and their performance when feedbacks are erased.

Advances in wireless access technologies like WiMax and LTE are driving the wireless video applications and wireless broadcasting video service in particular. Though existing erasure codes are designed to achieve high throughput for wireless broadcast/multicast, their potential for wireless video broadcasting can only be achieved if the distortions associated with video packets and their complex decoding dependency are also considered. In this paper, we propose a Joint Video Adaptation and Erasure Code(JVEC) for wireless video broadcast, taking into consideration the video decoding dependency among temporal scalable decoding dependency among I, P and B frames, and formulate a solution to minimize the average video distortion. Assuming that the source knows the erasure state of transmitted packets at the receiver side through the feedback channel, JVEC can generate erasure codes on the fly to firstly guarantee the decoding of important frames and secondly correct erased frames as many as possible. The simulation result shows that JVEC can achieve less video distortion compared with other erasure codes, such as LT code, RT oblivious code and SLT code.

Dynamic spectrum access has become a focal issue recently. Lots of works have been done concerning secondary users (SU) synchronously accessing primary users' (PU) network. However, on one hand, SU have to periodically synchronize with PU's time tables, which will bring lots of unnecessary overhead. On the other hand, it is possible that SU have no idea about PU's communication scheme at all or even communications among PU are not based on synchronous scheme. In order to address such problems, this paper advances an asynchronous algorithm, called A-DSA, for SU to asynchronously access CR-based Ad-Hoc network. We focus on three questions in this paper: 1) how to choose channels to sense; 2) how to sense the chosen channels; 3) how to determine the final access channel after sense. Three strategies are proposed towards each question. Our simulations show that choosing sense channels by A-DSA can attain 20% more success rate than randomly choosing. Moreover, sensing by A-DSA can not only achieve nearly 50% less interference probability than equal allocation of the overall sense time, but also well adapt to time-varying channels.

A novel receiver-initiated soft-state probabilistic multicasting protocol (RISP) for mobile ad hoc network is proposed in this paper. RISP introduces probabilistic forwarding and soft-state for making relay decisions. Multicast members periodically initiate control packets, through which intermediate nodes adjust the forwarding probability. With a probability decay function (soft-state), routes traversed by more control packets are reinforced, while the less utilized paths are gradually relinquished. In this way, RISP can adapt to node mobility; at low mobility, RISP performs similar to a tree-based protocol; at high mobility, it produces a multicast mesh in the network. Simulation results show RISP has lower delivery redundancy than mesh-based protocols, while achieving higher delivery ratio. Further, the control overhead is lower than other compared protocols.

This paper presents a proxy-based framework for Internet access through GPRS/UMTS networks. The proxy, deployed at the network boundary, is able to employ some scheduling strategies to assist the wireless network in charge of incoming traffic according to system capacity and user demand. In particular, we investigate the system performance on flow level and consider the impact of some user behaviors. The objective is to maximize utilization of the network by exploiting the property of delay tolerance, and to improve user-level performance such as response time. In addition, the proxy splits TCP connections into two halves, the wired and wireless sides. The TCP stacks on the wireless-facing side of the proxy can be modified to enhance the performance over the wireless link, while keeping compatibility with the conventional TCP implementations on mobile hosts. The effectiveness of this approach is illustrated by analysis and simulations.

Discrete wavelet multicarrier transceiver (DWMT) system, which can be viewed as a kind of OFDM, has many advantages because it uses wavelet as its base functions. In this paper we present a new sub-carrier frequency synchronize method for DWMT systems with little assistant information. The essential ideal of this arithmetic is: when an orthogonal multicarrier system is of perfect frequency synchronize, the demodulated signals of different sub-carriers are independent of each other. Whereas when frequency offset exists, every demodulated signal is constructed by several modulated signals projects on the demodulating frequency. So the adjacent demodulated signals consist of the element of the same modulated signal, and these demodulated signals are correlated with each other. The degree what they correlated with each other is a function of the sub-carrier relative frequency offset. Since that little assistant information is used in this algorithm, the spectrum efficiency can be largely increased. Simulation result show that if the number of the sub-carrier of the DWMT system is bigger than 1000, the relative frequency offset can be limited in 2%.

A novel predictor for network traffic, the wavelet domain recursive least-squares (WDRLS) predictor is discussed. Empirical studies have shown that network traffic possesses diverse statistical properties and exhibits a complex correlation structure characterized by short-range dependence (SRD) and long-range dependence (LRD). A challenge in predicting network traffic is how to exploit such a complex correlation structure with both high accuracy and computational efficiency. In the proposed WDRLS predictor, we use the wavelet transform to tackle these issues. Specifically, we predict the wavelet coefficients and solve the prediction of network traffic through a reverse wavelet transform. Our approach is based on the discovery that, although the network traffic has both SRD and LRD correlation structures, the corresponding wavelet coefficients are all SRD. We further assume that the SRD wavelet coefficients can be well approximated by a linear correlation structure for prediction. A least-squares method is adopted to make predictions in the wavelet domain. An important feature about the WDRLS predictor is that it can make an on-line prediction of network traffic. This is made possible by implementing the least-squares method in a recursive way. The performance of WDRLS is investigated with real network traffic. Simulation results demonstrate the feasibility of using the linear correlation structure to predict SRD wavelet coefficients and show that WDRLS can achieve high prediction accuracy when working with real LAN or WAN traffic.

Wireless channels are error-prone and susceptible to several kinds of interference from different time scales, causing difficulties in the support of real-time multimedia services. This paper investigates the resource optimization and scheduling of wireless networks through a time-scale decomposition approach. We decompose the dynamics of time-varying wireless channel conditions into random processes in two different time scales and deal with the two time scales by two different algorithms: the resource optimizer optimizes the sum of utilities in the slowly changing time scale, and the slot scheduler exploits the efficiency in the highly variable time scale. Our scheme can obtain high system utilization and provide a minimum service guarantee simultaneously. Simulation results show that our scheme can improve substantially the performance and efficiency.

Wireless channels are error-prone and susceptible to several kinds of interference from different time scales. This paper investigates the resource optimization and scheduling of wireless networks through a time-scale decomposition approach. We decompose the dynamics of time-varying wireless channel conditions into two random processes in different time scales: a slow time-varying process in a larger time scale, called frame-scale, and a stationary random process with high variation in a smaller time scale, called slot-scale. This results in two different algorithms dealing with each time-scale: the resource optimizer optimize the sum of utilities in the slowly changed time scale, and the slot scheduler exploits the efficiency in the highly variable time scale. Our scheme can obtain a high utilization while providing service guarantee. Simulation results show our scheme can improve the performance and efficiency substantially.

ATM networks oriented connections provide pure QoS (quality of service) for diversified services through a series of traffic management mechanisms, the ABR (available bit rate) flow control is especially important among these approaches. In the binary flow control scheme, the cell rate and queue length may oscillate with great magnitude to reduce link utilization, so that the EFCI (explicit forward congestion indication) algorithm is regard as ineffective, however its simplicity is attractive to high performance switch design. In this paper, the EFCI algorithm is analyzed based on classical control theory. It is found that the nonlinear hysteresis determines the congestion, and is the dominant reason that causes the oscillation. Then, a probability congestion detection approach, called p-EFCI is put forward. Numerical results show that the improved algorithm deeply constrains the oscillation magnitude, and the queue length is controlled in limited scope to guarantee cell zero loss.

In this paper, we focus on understanding the binomial congestion control algorithms (Bansal et al., 2001) and can generalize TCP-style additive-increase by increasing inversely proportional to a power k of the current window (for TCP, k=0) and generalize TCP-style multiplicative-decrease by decreasing proportional to a power l of the current window (for TCP, l=1). We discuss their global fairness and stability. We prove that such congestion control algorithms can achieve (p, k+l+1)-proportional fairness globally no matter what the network topology is and how many users there are. We also study their dynamical behavior through a control theoretical approach. The smoothness of the congestion control results in a less stable system and slower convergence to fair bandwidth allocation. The modeling and discussion in this paper are quite general and can be easily applied to equation-based TCP-friendly congestion control schemes, another category of TCP-friendly transport protocols.