Underwater acoustic backscatter communication technology brings a new perspective on addressing the energy dilemmas of Internet of Underwater Things (IoUT). However, time asynchronism in underwater acoustic backscatter devices (UABDs) can significantly degrade the performance of the UABD-based IoUT system. Existing time synchronization algorithms lose practicability leading to high energy consumption in scenarios where charging delays vary. To address these challenges, we propose UAB-Sync, a time synchronization algorithm specifically designed for the system. UAB-Sync introduces a novel three-stage architecture that uses dual constraints of time and energy to dynamically optimize the duration of energy signal, achieving adaptive approximation of optimal results. Besides, a closed-form solution for clock parameter estimation that incorporates Doppler factor estimation and accounts for multi-source measurement errors is developed, ensuring effective synchronous correction. Simulation results demonstrate that UAB-Sync significantly outperforms existing synchronization schemes in terms of both accuracy and energy efficiency for the UABD-based IoUT system.

This paper presents WirelessGPT, a generative foundation model designed for multi-task learning in integrated sensing and communication (ISAC) systems. Built upon large-scale heterogeneous wireless datasets including Traciverse, Sensiverse, and DeepMIMO, WirelessGPT learns universal spatio-temporal-frequency representations through self-supervised pretraining with masked channel token prediction. The proposed architecture introduces a multi-scale patch embedding module to capture both local and global channel features, and a triple-axis attention encoder to jointly model temporal, spatial, and frequency-domain dependencies. After pretraining, the model can be efficiently fine-tuned via lightweight adapters for diverse downstream tasks such as channel estimation, channel prediction, human activity recognition, environment reconstruction, and object tracking. Experimental results show that WirelessGPT achieves superior accuracy and generalization under limited labeled data and dynamic ISAC conditions, outperforming traditional and task-specific models in low-SNR and high-mobility scenarios while maintaining efficient inference suitable for edge deployment. By unifying communication and sensing functionalities within a single generative backbone, WirelessGPT establishes a scalable paradigm for AI-native 6G systems, enabling shared representations that support heterogeneous wireless tasks.

Artificial intelligence is expected to play a central role in enabling future 6 G networks. Developing foundation models that support a wide range of downstream tasks is critical for advancing 6 G standardization. This paper proposes a general framework for compressing wireless channel state information (CSI) using pretrained wireless foundation models. The foundation model is pre-trained using self-supervised learning with a masked reconstruction objective, achieving a normalized mean square error on the order of$10^{-3}$during pretraining. The model is evaluated on a range of wireless communication and sensing tasks, including classification tasks where compressed CSI is directly used for prediction, and regression tasks that require full CSI reconstruction. For regression tasks such as massive MIMO CSI feedback, the pre-compressed output from the foundation model is used as an auxiliary input to the downstream compressor, effectively enhancing the reconstruction quality. Compared to the Type-I codebook with comparable number of feedback bits, our method improves SGCS by 16.22% and reduces NMSE by 93.24%. Additionally, it achieves comparable SGCS performance to the Type-II codebook while using only 29% of the feedback bits. Comparison with existing research further confirms the contribution of the foundation model's compressed output in improving CSI compression performance. For classification tasks such as WiFi-based human activity recognition and human identification, the compressed representations produced by the foundation model can be directly utilized without additional fine-tuning. These representations achieve over 97% accuracy, outperforming conventional AI-based methods even under higher compression ratios. These findings demonstrate that leveraging a pretrained wireless foundation model consistently enhances performance across both classification and regression tasks, underscoring its versatility and potential in wireless CSI processing.

Pinching antennas are a promising technology for enhancing the performance of future indoor communication systems by leveraging spatial degrees of freedom. This paper pioneers the application of pinching antenna arrays in integrated sensing and communication (ISAC) systems and investigates dynamic array partitioning strategies. To maximize the secrecy sum rate (SSR), a partitioned array optimization problem under binary constraints is formulated, while satisfying sensing performance requirements and transmit power limitations. Specifically, the antenna partitioning constraints are modeled as minimum and maximum numbers of transmit antennas, along with binary constraints determining whether each antenna element functions in transmit or receive mode. To solve the non-convex optimization problem, a beamforming algorithm integrating semidefinite relaxation, generalized Rayleigh quotient, and minimum mean square error is proposed. Then, an element-wise iterative optimization method and a deep Q-network (DQN)-based partitioning approach are respectively developed to optimize the array configuration, thereby enhancing security performance under guaranteed sensing constraints. Simulation results demonstrate that the DQN-based approach outperforms the conventional iterative optimization method. In terms of security performance, the pinching antenna array can achieve a 69.70% reduction in the number of antennas and a 30.16% saving in transmit power compared to conventional fixed-position antenna (FPA) systems. Moreover, the pinching antenna system attains a 35.72% improvement in SSR performance, surpassing traditional FPA configurations.

In this paper, we introduce a layer-wise compensated aggregation algorithm designed for federated learning in dynamic environments. To reduce data transmission and maintain model continuity, our method implements a hierarchical update strategy that avoids artificially modular partition of neural networks and thus more flexible in practice. Specifically, weight layers closer to the output, which significantly impact model performance, receive direct updates. Conversely, layers further from the output with minimal influence, are updated using gradient compensation from the previous iteration. To ensure consistency across the model, we compensate these less influential weight layers with residual values from earlier rounds, thus enhancing integration and continuity. Experimental results from two datasets confirm that our algorithm not only secures stable convergence but also matches the generalization performance of traditional federated averaging, even under conditions with dropout rates as high as 90%.

Heterogeneous multiagent systems (HMASs) leverage diverse agent capabilities to address complex tasks in dynamic environments, yet traditional approaches face limitations in autonomy and generalization when adapting to evolving scenarios. To overcome these challenges, we propose OC-HMAS, an IoT-integrated framework that synergizes self-organization and self-correction through multimodal perception. The system processes RGB images, LiDAR point clouds, and instance segmentation maps for real-time environmental awareness, while vision-language models and large language models (LLMs) jointly enable context-aware task decomposition, role allocation, and adaptive planning. Integrated path optimization and obstacle avoidance mechanisms further ensure operational safety and scalability across logistics, inspection, and search-and-rescue operations. Experimental validation demonstrates the framework’s superiority over SMRC-LLM, with 5.15% higher success rates and 14.2% faster task completion in logistics, alongside 4.69% accuracy gains and 12.9% time reduction in inspection scenarios. These results validate its enhanced adaptability in IoT-augmented environments, establishing a new benchmark for autonomous HMAS deployment.

This paper addresses the challenges of privacy protection, device constraints, resource heterogeneity, and trusted inference in the integration of large language models (LLMs) with Internet of Things (IoT) devices, proposing a Hierarchical Federated Collaborative Computing (HFCC) framework. Unlike traditional federated learning, HFCC employs horizontal splitting + chunking to reduce LLMs computation overhead. The framework dynamically splits LLMs into: 1) global shared layers optimized by edge servers, and 2) device-local layers trained on private data, ensuring raw data remains on-device. During inference, chunking of shared layers and dynamic task allocation adjust computational loads based on real-time device states, mitigating high-load security risks. Furthermore, leveraging the hierarchical federated learning architecture, the system employs an anonymized parameter aggregation mechanism during training to achieve multi-level privacy protection. Simultaneously, a cross-device consensus verification mechanism performs trusted validation of distributed intermediate results, effectively identifying malicious node behavior. Experiments show 58% faster inference in resource-constrained environments, significantly reduced data exposure risks, and 94% malicious node detection accuracy versus traditional federated learning. This lays a solid foundation for building an intelligent, efficient, and secure IoT ecosystem.

In this article, we exploit the positioning results for communication, and propose an integrated positioning and communication (IPAC) framework for terahertz (THz) massive multi-input-multioutput (MIMO) networks. Specifically, we derive an explicit expression for the Cramér-Rao bound (CRB), which is used to evaluate the positioning performance. Furthermore, based on the established relationship between positioning and communication, we propose a joint beam tracking and power allocation scheme for mobile users in THz massive MIMO networks, which minimizes the positioning error under both the data transmission outage constraint and total power constraints. Unfortunately, the joint beam tracking and power allocation optimization problem is nonconvex, and intractable due to the outage constraint. To address this challenge, we decompose the nonconvex problem into a beam tracking subproblem and a power allocation subproblem, and propose a proximal policy optimization beam tracking (PPO-BT) algorithm for the beam tracking subproblem and a robust power allocation (RPA) algorithm for the power allocation subproblem. Furthermore, we extend the proposed THz IPAC scheme to more practical 3-D scenarios. Simulation results demonstrate that our proposed THz IPAC framework can satisfy positioning and communication requirements at the same time, and our proposed methods outperform existing methods.

In this article, we propose ABR-FL, an adaptive recovery and selective backup algorithm designed to manage models compromised in federated learning (FL), while minimizing computational costs for clients and memory costs for the server. Our algorithm integrates multiple stages, including selective backup mechanisms, recovery point selection, initial recovery stage fine-tuning, approximation training using historical gradients, and post-approximation fine-tuning. Initially, the server evaluates client updates using a Taylor expansion, backing up only those updates that align well with the global model and excluding potentially malicious updates. To optimize memory usage and enhance computational efficiency, the server evaluates updates over the most recent k iterations, groups clients, and assesses updates from each cluster. Subsequently, the server performs a sensitivity analysis on historical backup models and stores models with sensitivity below a certain threshold in a starting point pool for potential recovery initiation. The recovery stage involves fine-tuning the model to comprehend the model’ s learning trajectory and ensure that the approximation training process on the server aligns with this trajectory. The server employs second-order gradient information (Hessian approximations) and a rank-two correction matrix to calculate the recovered gradient from the historical gradient. Post-approximation fine-tuning addresses errors stemming from omitted higher order terms by making small, precise adjustments to the model parameters. Theoretically, we establish that the global model recovered by ABR-FL closely approximates that of a model trained from scratch under certain assumptions. Finally, we build a FL system and conduct extensive experiments to demonstrate the effectiveness of our algorithm in terms of model accuracy.

In the context of 6G communication networks, large communication models emerge as the core application for edge communication networks. They hold immense application potential in areas like intelligent connected vehicles, collaborative robots, and holographic communications. However, practical implementations of these applications face challenges such as unstable learning environments, resource constraints, information theft, and heterogeneity of edge devices. This paper delves into the distributed deployment of large communication models under resource-constrained nodes and introduces a distributed architecture based on cloud-edge collaboration. Crucially, we introduce a negotiation and debate mechanism between small AI models. This mechanism adopts innovative strategies, enabling it to match or even surpass the capabilities of large language models in terms of inference and accuracy. Furthermore, addressing the data confidentiality issues of large model parameter transmissions, this study proposes a secure federated learning mechanism based on privacy computing. Combining novel model layering technology with privacy protection measures, it aims to enhance the security of model parameter transmissions. Experimental results confirm that while improving learning efficiency and inference accuracy, this strategy successfully ensures the privacy security of edge nodes, offering a practical solution for future privacy protection.

This study investigates the channel state information (CSI) feedback problem in large-scale multiple-input multiple-output (MIMO) systems, which is important for intelligent co-operative traffic management in smart ports. MIMO systems depend on CSI feedback for effective precoding, which enhances the system's transmission gain. While various strategies have been developed to minimize CSI feedback overhead, these have predominantly been assessed in static scenarios. Addressing the complexity and variability inherent in smart ports, this paper introduces a novel approach utilizing a Transformer-based method for CSI feedback. This method integrates the LoRA algorithm, optimizing computational efficiency during training and enabling rapid adaptation to complex, evolving environments. Experimental findings demonstrate that this approach not only requires fewer computational resources and operates more swiftly but also exhibits superior adaptability to environmental fluctuations. In addition, this approach greatly improves the robustness, stability, and resource optimization fairness of the system compared to existing CSI feedback techniques.

This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessively on the rationality of other underwater nodes when guided by fully cooperative approaches, making it difficult to seek a trade-off between individual QoS and global fair-effective communications under imperfect conditions. Therefore, this paper presents aSEmi-COoperativePowerAllocation approach (SECOPA) that achieves fair-effective communication and robustness in IC-UASNs. The approach is distributed multi-agent reinforcement learning (MARL)-based, and the objectives are twofold. On the one hand, each intelligent node individually decides the transmission power to simultaneously optimize individual and global performance. On the other hand, advanced training algorithms are developed to provide imperfect environments for training robust models that can adapt to the time-varying acoustic channels and handle unexpected node failures in the network. Numerical results are presented to validate our proposed approach.

Underwater wireless sensor networks (UWSNs) stand as promising technologies facilitating diverse underwater applications. However, the major design issues of the considered system are the severely limited energy supply and unexpected node malfunctions. This paper aims to provide fair, efficient, and reliable (FER) communication to the imperfect and energy-constrained UWSNs (IC-UWSNs). Therefore, we formulate a FER-communication optimization problem (FERCOP) and propose ICRL-JSA to solve the formulated problem. ICRL-JSA is a deep multi-agent reinforcement learning (MARL)-based optimizer for IC-UWSNs through joint link scheduling and power allocation, which automatically learns scheduling algorithms without human intervention. However, conventional RL methods are unable to address the challenges posed by underwater environments and IC-UWSNs. To construct ICRL-JSA, we integrate deep Q-network into IC-UWSNs and propose an advanced training mechanism to deal with complex acoustic channels, limited energy supplies, and unexpected node malfunctions. Simulation results demonstrate the superiority of the proposed ICRL-JSA scheme with an advanced training mechanism compared to various benchmark algorithms.

Federated learning is an attractive solution for efficient data processing and optimization in distributed networks. However, communication bottlenecks often result in sluggish optimization and increased resource consumption, leading to reduced system efficiency. To address these challenges, this paper proposes a novel approach based on Federated Low-bitwidth Quantization (FLQ) framework, which optimizes resource utilization in distributed communications. FLQ quantizes network parameters and gradients, significantly reducing computational and communication costs related to parameter broadcasting and gradient uploading. By utilizing an 8-bit low-bitwidth training and binary vector compression technique, our algorithm greatly enhances network convergence and is well-suited to the energy consumption characteristics of servers and end devices. Moreover, to enhance overall device coordination, we develop a dynamic resource allocation scheme that adapts to changing requirements of individual nodes. Results from extensive experiments demonstrate that our approach leads to faster convergence, lower computational and communication costs and optimized joint network deployment in Internet of Things scenarios.

In this paper, we propose an adaptive distributed learning algorithm that not only resists three types of Byzantine attacks (i.e., gradient negative direction attacks, gradient partial dimension zeroing attacks, gradient scaling attacks) but also ensures high model accuracy. The proposed algorithm is built on a fully distributed model: clients share their local model updates with a group of dynamic committee clients, who cooperatively and iteratively train a global model. Specifically, to counter gradient negative direction attacks, we design a method based on gradient projection that maps clients' local gradients into small subspaces. The design allows committee clients to efficiently and precisely filter out adversarial clients by comparing angles between these subspaces. Moreover, considering that data heterogeneity among clients may cause misdetections of gradient partial dimension zeroing and scaling attacks, thereby reducing model accuracy, we introduce an adaptive multi-dimensional scoring method, which is applied after the gradient-projection-based filtering. The method assists committee clients in scoring and selecting most suitable clients for model aggregation using three hyperparameters, and thus achieves a balance between model accuracy and security. Finally, we conduct extensive experiments on real-world datasets to show the proposed algorithm's effectiveness: it can achieve Byzantine robustness and simultaneously maintain high model accuracy.

As maritime communication networks become more developed and widely used, an increasing number of end devices are being connected to these networks. Edge computing is an effective way to meet the realtime computing needs of these devices. However, with federated learning becoming more prevalent in edge computing decision-making, resource-constrained end devices are becoming more vulnerable to security threats. In particular, poisoning attacks are a significant security concern in the federated learning training process as local data is invisible to the outside world, enabling malicious participants to easily tamper with it. To address this issue, this paper proposes a Common Layer Mean Detection method for poisoning attacks that reduces their impact on the accuracy and resource consumption of the federated model while ensuring its security. The proposed method identifies poisoning attacks by comparing the mean distribution differences between attackers and honest clients in the common layer. Aggregation weights are then set based on the detection results to eliminate the impact of spurious parameters of malicious participants on the overall model. The effectiveness of this approach is demonstrated by comparing it with related schemes in terms of security, communication overhead, and computational overhead. Overall, the proposed method is shown to be both secure and efficient, making it a valuable addition to the field of federated learning for maritime communication networks.

Distributed learning has been widely adopted to train a global model from local data. However, its performance can be severely affected by stragglers. Recently, some research has been dedicated to resolving the straggler problem by adopting gradient coding, the essence of gradient coding is to solve the straggler problem by adding data redundancy. However, the large amount of data redundancy as well as computation and communication overhead that it brings is still hard to be resolved. Besides, the complexity of the encoding and decoding will increase linearly with the number of the local workers. To this end, in this paper, we design a lightweight coding method in the computing phase and seek to ensure fair transmission in the communication phase. Specifically, to tolerate stragglers in computing phase, we propose a two-stage dynamic coding scheme, part of the workers start computing the partial gradients from the data partitions assigned in the first stage, and the remaining workers for computation in the second stage is decided based on which workers have finished in the first stage. To further tolerate stragglers in the communication phase, a perturbed Lyapunov function is designed to maximize admission data balancing fairness as well as the throughput. The experimental result verifies the derived properties and demonstrates that our proposed solution can achieve a better performance for practical network parameters and benchmark data in terms of accuracy and resource utilization in the distributed learning system.

The rising adoption of IoT devices in marine environments has led to an increased interest in edge computing research. Distributed Learning (DL) has emerged as a key solution for addressing privacy concerns in distributed edge computing. However, existing DL frameworks may not fully account for resource limitations and unique maritime IoT network topologies. In this paper, a novel Federated Learning (FL) framework for marine wireless communications was proposed, focusing on modeling the communication environment, designing a practical wireless Ocean Wireless Federation (OWF) framework, and optimizing resource allocation. We develop a comprehensive wireless signal propagation model for marine environments and design an iterative FL algorithm that addresses marine communication challenges. Additionally, we propose a resource scheduling and allocation scheme for efficient bandwidth, energy, and computation utilization. Extensive experiments validate the properties of our OWF algorithm and demonstrate superior performance in accuracy, resource utilization, and convergence speed for practical network parameters.

Providing qualified and fair communications for underwater wireless sensor networks (UWSNs) has garnered considerable interest in light of scarce resources and dynamic channel conditions. Fairness is critical in various situations, including emergency communications in resource-constrained underwater networks that balance load and energy among nodes to optimize network performance. Existing solutions for fair communications, on the other hand, frequently come at the expense of network capacity. This article focuses on the power-management strategy that jointly optimizes the reuse, fairness, and capacity of UWSNs while also proposing a new metric for fair spatial reuse in networks: the fair reuse index (FRI). We observe that the FRI for UWSNs is strongly reliant on the network density, channel conditions, and application requirements. As a result, UWSNs commonly exhibit load imbalance and struggle to meet required network lifetimes. Toward this end, we propose DMPM, a deep multiagent reinforcement learning-based power-management strategy for increasing the fair reuse of UWSNs. DMPM strives to maximize the network’s fair reuse while allowing for gentle network capacity decrease. In two representative communication scenarios, numerical results demonstrate that DMPM achieves a significantly better tradeoff between network capacity and fair reuse than baseline techniques. We also construct three reward functions for DMPM and discuss how different reward functions affect node behaviors. Delivery delays of different models are discussed. We hope that the work provided in this study will prove to be invaluable in the design and optimization of UWSNs.

Federated learning is a machine learning prgadigm that enables the collaborative learning among clients while keeping the privacy of clients’ data. Federated multitask learning (FMTL) deals with the statistic challenge of non-independent and identically distributed (IID) data by training a personalized model for each client, and yet requires all the clients to be always online in each training round. To eliminate the limitation of full-participation, we explore multitask learning associated with model clustering, and first propose a clustered FMTL to achieve the multual-task learning on non-IID data, while simultaneously improving the communication efficiency and the model accuracy. To enhance its privacy, we adopt a general dual-server architecture and further propose a secure clustered FMTL by designing a series of secure two-party computation protocols. The convergence analysis and security analysis is conducted to prove the correctness and security of our methods. Numeric evaluation on public data sets validates that our methods are superior to state-of-the-art methods in dealing with non-IID data while protecting the privacy.

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

In recent years, with the continuous development of internet of things (IoT) technology, many fields have benefited a lot, including the maritime transportation system (MTS). But there are also corresponding risks, such as security and privacy, interference attacks, ransomware attacks, and so on. How to ensure the reliability and efficiency of information transmission is very important for maritime transportation system. In order to solve this problem, we propose an IoT-enabled maritime transport communication system, which is a distributed system composed of base stations and offshore buoys, and uses the unique structure of the blockchain to solve the problems of security and reliability in the network. There are two main advantages: First, the decentralized network is reliable and can handle node failures. Second, the use of blockchain technology can integrate computing resources into the entire network to support different tasks, while taking into account information security and transaction security. On this basis, with the help of edge computing technology, we have also improved the energy efficiency and performance of IoT devices in the system.

Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm.

Caching popular contents at the base station (BS) in maritime Internet of Things (IoT) networks makes sensor nodes be free from frequently responding to user requests, which can remarkably save the energy consumption of sensor nodes. However, to ensure the freshness of contents, cached contents need to be updated periodically. Frequent content updating can minimize the age of information (AoI) of contents while increase the energy consumption of sensor nodes. To make a better tradeoff between the AoI and energy consumption, in this paper, both the cache placement and content updating interval are jointly optimized to minimize the weighted sum of AoI of contents and energy consumption of sensor nodes. As the formulated problem is a mixed integer nonlinear programming problem, the cache placement and the content updating interval are alternatively optimized. For the cache placement problem, a local optimal solution is achieved via the binary constraint reformulation and successive convex approximation. For the content updating problem, the optimal solution with semi-closed form is derived. Simulation results show that our proposed algorithm outperforms other benchmarks in terms of the weighted sum of AoI and energy consumption.

The emergence of digital twin technology is expected to reduce the significant cost of traditional physical debugging. Moreover, it can also fully combine IoT real-time data, large data analysis, and simulation, to make the best decisions. In the digital twin network, the data distribution and computing power of different device nodes shows great heterogeneity, but the internal node data are independent and identically distributed. Therefore, our learning algorithm should be able to learn not only the network commonality, but also the node specificity. In order to provide secure and personalised services for different device nodes, a polymorphic learning (PL) framework is proposed in this paper. PL divides the classical neural network model into a homomorphic model and a polymorphic model to realise flexible network control. Finally, the advantages of the PL algorithm in the collaborative optimisation of different nodes are proven through simulation experiments, and the algorithm running process is simulated through a classic case, with the final results proving the superiority of the algorithm.

We investigate a cooperative federated learning framework among devices for mobile edge computing,named (CFLMEC), where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC,devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

In this paper, we consider a reinforcement learning (RL) based multi-user downlink communication system. An actor-critic based deep channel prediction (CP) algorithm is proposed at the base station (BS) where the actor network directly outputs the predicted CSI without channel reciprocity. Different from the existing methods which either require the perfect channel state information (CSI), or estimate outdated CSI and set strict constraints on pilot sequences, the proposed algorithm has no such premised knowledge requirements or constraints. Deep-Q learning and policy gradient methods are adopted to update the parameters of the proposed prediction network, with the objective of maximizing the overall transmission sum rate. Numerical simulation results and the complexity analysis verify that the proposed CP algorithm could beat the existing traditional and learning based methods in terms of sum rate over different channel models and different numbers of users and antennas.

Driver misbehavior is one of the major traffic safety hazards as car ownership increases year by year. So, it is important to have driver fatigue detection and behavior recognition. Initially, given the fatigue detection problem that the images captured by the visible light camera cannot capture the eyes of a driver wearing sunglasses or eye glasses. As a solution, this paper introduces a DCT-HSV preprocessing algorithm for infrared images, which is believed to enhance the target characteristics of infrared images. The paper also introduces a more efficient lightweight SSD detection model, which achieves a better balance in terms of model size and detection performance. It also shows a better performance in self-built datasets and basic vehicle operation datasets. Secondly, aiming at the problems of high complexity and poor accuracy of the existing driving behavior detection model, this paper designs a driver abnormal behavior discrimination model based on the comparison twin, which has good performance on the Kaggle public dataset. The relevant experimental detection results show that the method constructed in this paper has high detection accuracy, low warning delay, and good practical use value.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine monitoring, military reconnaissance, hydrology surveys, etc. Their location information is an important apriori knowledge when they are carried out underwater. However, the complex underwater environments impose great challenges on location acquisition, especially for autonomous underwater vehicles (AUVs), because of their mobility and finite power. In UASNs, existing location and navigation methods can offer AUVs position information, but they may either need a doppler velocity log (DVL), which is inefficient due to the complex underwater environments, or they may require additional localization infrastructure to deploy underwater, which suffers from large communication latency among AUVs, and costs enormous power. In this article, an efficient velocity estimation and location prediction method (VELP) in UASNs is proposed to avoid the above restrictions. It only utilizes collaborations based on communication among AUVs to achieve higher precision location with lower cost. Specifically, we apply an AUV-assisted velocity estimation algorithm with Doppler shift estimation in the physical layer of UASNs to improve the velocity estimation accuracy instead of the DVL. Meanwhile, we build a belief propagation-neural network-based location prediction model, which decreases the communication requirements and obviates introducing modeling errors. Extensive experimental results show VELP achieves superior performance on both accuracy and efficiency, demonstrating its great advantage in offering AUVs’ location information.

Internet of Vehicles (IoV) is one of the most promising applications of Internet of Things (IoT) in the automotive industry, which can empower moving vehicles to exchange information with neighboring cars, roadside infrastructure, remote servers, traffic control centers, and so on. IoV expects to support a wide range of vehicular services, such as road safety, path planning, infotainment, and smart parking, which will play a vital role in intelligent transportation systems (ITSs)[1]–[3]. The main enabling platforms for IoV consist of dedicated short-range communications (DSRCs)-based networks and cellular networks (C-V2X). However, these terrestrial networks alone might not be able to support the vehicular applications well in all the cases and scenarios, due to the issues of limited coverage and capacity, as well as costly deployment.

Providing qualified and sustainable communications is one of the key challenges for the Internet of Underwater Things (IoUT) facing constrained energy supplements, nonstationary environments, and severe communication interference. Owing to spatial separation, several nodes can (and are often required to) make transmissions simultaneously to maximize network capacity. However, existing transmission solutions often face the dilemma between maximizing local capacity and global concurrency. We break this dilemma via UDARMF, an underwater distributed and adaptive resource management framework, which maximizes network capacity by supporting an increased number of communications in the network. It is a distributed deep multiagent reinforcement learning framework that uses an observation encoder and a local utility network to coordinate the collaboration among underwater nodes by adaptively tuning its transmit parameters. We designed experiments to compare UDARMF with baselines in network capacity, concurrency, and energy efficiency. Extensive experiments were conducted to find the appropriate hyperparameters to achieve the optimal network performances. We also analyze the performance of UDARMF and baselines over diverse communication and lifetime requirements, communication environment, and energy storage. Simple closed-form approximations of UDARMF are given to reveal that an energy-constrained network’s capacity increases with available energy, following a linear trend on the logarithmic scale. Experimental results demonstrate that compared to other methods, UDARMF achieves a much better tradeoff between network capacity and concurrency, at which the lifetime requirements are satisfied. The proposed framework and the closed-form approximations are likely to become valuable tools in designing and analyzing IoUT.

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.

As cloud storage services have become popular nowadays, the integrity of outsourced data stored at untrusted servers received increased attention. Provable data possession (PDP) provides an effective and efficient solution for cloud data integrity by asking the cloud server to prove that the stored data are not tampered with or maliciously discarded without returning the actual data to users. In this article, we propose an efficient identity-based privacy-preserving provable data possession scheme (ID-P$^3$DP) based on the RSA assumption for secure cloud storage. In ID-P$^3$DP, a cloud user takes the outsourcing file and a global parameter in a time period as inputs to generate identity-based homomorphic authenticators, and any third-party auditor (TPA) can check the integrity of the outsourced file by verifying the validity of homomorphic authenticators. The distinguished feature of ID-P$^3$DP is to support the aggregation of identity-based homomorphic authenticators generated by different users under the RSA assumption, which is an open problem in provable data possession. Specifically, we transfer the identity-based homomorphic authenticators generated in distinct time periods into those with the same period parameter, and the cloud can compress the homomorphic authenticators of different users to generate a data possession proof for integrity verification. Besides, by exploiting zero-knowledge proof, the leakage of outsourced data to TPA can be prevented. The soundness of ID-P$^3$DP is proved based on the RSA assumption, and the privacy against TPA is perfectly preserved. Finally, we demonstrate ID-P$^3$DP is more efficient on integrity verification than the existing BLS-based schemes, and cross-user aggregate verification can significantly reduce computational and communication overhead for TPA.

Heart disease seriously threatens human life due to high morbidity and mortality. Accurate prediction and diagnosis become more critical for early prevention, detection, and treatment. The Internet of Medical Things and artificial intelligence support healthcare services in heart disease monitoring, prediction, and diagnosis. However, most prediction models only predict whether people are sick, and rarely further determine the severity of the disease. In this article, we propose a machine learning based prediction model to achieve binary and multiple classification heart disease prediction simultaneously. We first design a Fuzzy-GBDT algorithm combining fuzzy logic and gradient boosting decision tree (GBDT) to reduce data complexity and increase the generalization of binary classification prediction. Then, we integrate Fuzzy-GBDT with bagging to avoid overfitting. The Bagging-Fuzzy-GBDT for multiclassification prediction further classify the severity of heart disease. Evaluation results demonstrate the Bagging-Fuzzy-GBDT has excellent accuracy and stability in both binary and multiple classification predictions.

Deep neural networks (DNNs) have become a critical component for inference in modern mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile- and server-based approaches compromise either the inference accuracy or latency. Instead, a hybrid approach can reap the benefits of the two by splitting the DNN at an appropriate layer and running the two parts separately on the mobile and the server respectively. Nevertheless, the DNN throughput in the hybrid approach has not been carefully examined, which is particularly important for edge servers where limited compute resources are shared among multiple DNNs. This article presents HiTDL, a runtime framework for managing multiple DNNs provisioned following the hybrid approach at the edge. HiTDL's mission is to improve edge resource efficiency by optimizing the combined throughput of all co-located DNNs, while still guaranteeing their SLAs. To this end, HiTDL first builds comprehensive performance models for DNN inference latency and throughout with respect to multiple factors including resource availability, DNN partition plan, and cross-DNN interference. HiTDL then uses these models to generate a set of candidate partition plans with SLA guarantees for each DNN. Finally, HiTDL makes global throughput-optimal resource allocation decisions by selecting partition plans from the candidate set for each DNN via solving a fairness-aware multiple-choice knapsack problem. Experimental results based on a prototype implementation show that HiTDL improves the overall throughput of the edge by$4.3\times$compared with the state-of-the-art.

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

Mobile-edge computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated Quality-of-Service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this article, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

We consider the problem of the intelligent and efficient resource management framework in mobile-edge computing (MEC), which can reduce delay and energy consumption, and features distributed optimization and efficient congestion avoidance. In this article, we present a cooperative learning framework for resource management in MEC from an alternating direction method of multipliers (ADMMs) perspective, named the CL-ADMM framework. First, computing a task requires both the user personal data and corresponding program that processes it, to efficiently cache program in a group, a novel program popularity estimation scheme is proposed, which is based on a semi-Markov process model. Then, a greedy program cooperative caching mechanism is established, which can effectively reduce delay and energy consumption. Second, to address group congestion, a dynamic task migration scheme based on improved cooperative Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Third, to minimize delay and energy consumption for resource allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM-based scheme to solve this problem, which can reduce the complexity of the problem with a new set of auxiliary variables, these subproblems are all convex problems that can be solved by using a primal-dual approach, which guarantees its convergence. Finally, we prove its convergence by using the Lyapunov theory. The numerical results demonstrate the effectiveness of the CL-ADMM framework in reducing delay and energy consumption in MEC.

This paper considers the blind recognition of the type and the encoding parameters of channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural network (RNN), the attention mechanism, and the residual neural network (ResNet), three universal recognizers are proposed to identify the type, rate, and length of the target channel codes, with a training set generated by a small portion of all the possible code parameters. The proposed architectures need near zero a priori knowledge about the target channel code, and only require the length of the received signal to be dozen times of the codeword length. Numerical experiments show that the proposed deep learning methods own strong generalization to identify channel codes from the testing samples not generated by the encoding parameters utilized for the training set.

The maritime communication network is assembled by emergent network technologies. However, the adverse maritime environment impedes the efficiency of resources allocation in maritime communication network. Here we show a joint sleeping scheduling and opportunistic transmission scheme in delay-tolerant maritime wireless communication networks based on software defined networking (SDN) to find a better tradeoff between the energy consumption and the delay. Specifically, an energy-limited delay tolerant networking (DTN) node deployed in the ocean receives/transmits data from/to vessels within its communication range. To further save the energy, a long-term energy minimization problem is formulated with sleeping scheduling and opportunistic transmission. After that, a multi-objective minimization problem of energy and delay is first modeled by Lyapunov optimization (LO), which then is solved by convex optimization. Both mathematical analyses and simulation results demonstrate how the maritime communication network allocates satisfactorily with the proposed allocation scheme.

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.

The advance of edge computing pushes computing functionalities to the network edge and brings lucrative opportunities for edge operators (EOs) to cater the users with low latency requirement. Unlike in cloud computing, edge servers have limited computing capacity and require a proper resource planning. To avoid loss of potential profit, a promising way is to outsource cloud resources from a public cloud with additional cost when the edge computing capacity is insufficient to meet the real-time demands. Besides, the uncertainty of future demands also affects EOs' profits. It's essential to consider the interaction among market participants with different risk attitudes. To this end, we study multiple risk-averse EOs with one risk-neutral Cloud Provider (CP) in an edge computing market, where each EO competes to serve the users by determining the optimal resource provision strategies given the demand and the outsource price charged by the CP, and the CP sets the price based on the best responses of the EOs. We model the interaction between EOs and CP as a two stage Stackelberg game, and employ a data-driven optimization approach to characterize the uncertainty. We explore the existence and uniqueness of subgame Nash equilibrium, and find the equilibrium based on the Sample Average Approximation (SAA) method. Extensive simulations using real-world cluster data traces verify the effectiveness of the proposed method.

By connecting the maritime users to Internet, e.g., boats, ships, etc., it is possible to operate maritime sensing and informatics across seas and oceans. Such marine Internet of things (MIoT) is urging intelligent maritime applications, e.g., real-time vessel tracking, navigation safety, autonomous shipping, etc. Due to the bandwidth limitation of conventional marine channels, broadband communication is desired for these emerging applications. In this paper, we consider operating the TV white space (TVWS) spectrum in 700MHz to support the near-sea surface communication for MIoT terminals. To better utilize the TV channel capacity, we propose a proactive and efficient link adaptation (LA) scheme based on nonlinear autoregressive neural network (NARNN) time series prediction. Specifically, the historical signal samplings are used to predict the near-sea-surface channel link status for the next transmission slot, which is then used to select a proper modulation and coding scheme (MCS) for the next egress frame. We have conducted extensive simulations, and show that the average channel utility can achieve almost 85% of the optimal capacity. The proposed LA scheme can provide useful inspirations for applying data analytics to efficient and adaptive LA schemes for mobile Internet of things.

Mobile crowdsensing (MCS) has become a new sensing and computing paradigm due to the proliferation of global positioning system (GPS) enabled mobile devices. There are three parties in the MCS, the MCS server, task requesters and workers. The MCS server needs to collect workers' location information to optimize the task allocation problem. However, during the location data collection process, workers' location privacy might be disclosed without their knowledge. It is challenging to preserve workers' location privacy while effectively and efficiently selecting proper workers to fulfill an MCS task. In this work, we propose a novel differentially private geocoding (DPG) mechanism to preserve workers' location privacy. Specifically, instead of reporting the exact latitude and longitude to the server, workers can use obfuscated geocode to describe their locations, since geocodes can provide an intuitive visualization of workers' spatial information to the MCS server. Based on the workers' obfuscated geocodes, we also formulate a travel distance minimization problem in MCS into an integer linear programming problem. We leverage conditional value at risk (CVaR) to characterize the uncertainty brought by the obfuscated geocodes, and develop feasible solutions to the formulated optimization problem. We conduct simulations with a real-world taxi dataset and verify the effectiveness of the proposed mechanism.

In the formation network of unmanned ship (USV), link interval distance and channel interference can lead to increased transport delays and decreased network throughput. Therefore, unmanned aerial vehicle (UAV) is introduced to carry out cooperative communication with unmanned ship, to construct an integrated network mechanism of air and sea assisted by UAV. An integrated communication network for terrestrial, sea and high- altitude platform (TSHP) is proposed. Comprehensive consideration the switching cost caused by node movement and link interruption, and the overhead generated by the transmitted power at the node and the link interference factors are also considered. The problem of node access mechanism can be formalized as an optimization problem under multiple constraints. The reinforcement learning strategy is integrated into the basic whale optimization algorithm, the fast convergence and optimization capability of the algorithm are improved. The optimal transmission rate, packet arrival rate and average D2D delay of the link are calculated. Simulation results show that the proposed node access mechanism can effectively improve data transfer rate, average D2D delay and network throughput.

In infrastructure‐less scenarios such as rural environments, wild emergency response, military applications and disaster relief, unmanned aerial vehicles (UAVs) are capable of providing enhanced mobile edge computing (MEC) services for ground users. Although small latency is the most important advantage of MEC system, how to provide delay aware scheduling in UAV‐enabled MEC system still remains unsolved. In this study, the authors investigate the delay aware scheduling problem in UAV‐enabled orthogonal frequency division multiple access (OFDMA) MEC system and formulate two non‐convex optimisation problems. Moreover, they consider uplink and downlink architecture with characteristics in different UAV‐ground links and traffic load. Furthermore, they propose two novel multi‐stages resource allocation algorithms, i.e. the JSPA‐T and JSPA‐F algorithms with respect to downlink transmit power allocation and sub‐carrier assignment. The mathematical frameworks with duality theory based alternative search optimisation and successive approximation method are proposed. The simulation results validate the performance improvement of the proposed solutions as well as the fast converge behaviour and small computational complexity.

The advancement of the Internet of Things has enabled numerous applications ranging from smart wearable to connected vehicles. However, the limited energy and memory resources of low-end IoT devices significantly impede their further flourish. In this article, we consider a system that consists of an IoT device and an edge server. The edge server stores code blocks for the IoT device and loads required blocks to the IoT device for execution thereby alleviates the latter from the limited memory resource. Furthermore, the IoT device can harvest energy from the ambient energy sources to achieve a sustainable operation. To deal with the dynamic energy harvesting process and block request process, we propose a stochastic block prefetching framework (BPF) to optimize the user experienced delay. The BPF assists the IoT device to intelligently prefetch blocks from the edge server according to the historical user behaviors. The BPF consists of three modules, i.e., estimation module, prefetching module, and dual learning module. The estimation module measures the probability of block being requested in the future. The prefetching module requests blocks from the edge server according to the available energy and memory. The dual learning module helps to accelerate the convergence of the framework. The numerous simulation results are provided to verify the effectiveness of the proposed framework.

Narrowband Internet of Things (NB-IoT) is a powerful technology for massive machine-type communications, which is imperative in the forthcoming 5G wireless communications. Unlike the long-time evolution (LTE) protocol, in the NB-IoT protocol specified by the third generation partnership project (3GPP), the narrowband physical random access channel (NPRACH) is newly introduced and its receiver performance is critical to the success of an NB-IoT system. In this article, an optimal activity detection scheme is first designed by using the Neyman-Pearson criterion. Then, a low-complexity iterative search algorithm is developed for the joint estimation of residual carrier frequency offset (RCFO) and timing advanced (TA), avoiding the effect of phase ambiguity. Finally, simulation results collaborate on the effectiveness and efficiency of the proposed receiver.

The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of service (QoS) with the complex ocean environments, resulting in energy saving and increased transmission latency. To investigate the tradeoff between latency and energy consumption in low-cost large-scale maritime communication, we formulate the offloading optimization problem and propose a two-stage joint optimal offloading algorithm, optimizing computation and communication resource allocation under limited energy and sensitive latency. At the first stage, the maritime users make the decision on whether to offload a computation considering their demands and environments. Then, the channel allocation and power allocation problems were proposed to optimize the offloading policy which coordinates with the center cloud servers at the second stage, considering the dynamic tradeoff of latency and energy consumption. Finally, numerical simulation results show the effectiveness of the proposed algorithm.

In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR) technology can improve spectrum utilization in an opportunistic communication manner for IoT, forming a promising paradigm known as cognitive radio ad hoc networks, CRAHNs. However, dynamic spectrum availability and mobile devices/persons make it difficult to develop an efficient data transmission scheme for CRAHNs under an obstacle environment. Opportunistic routing can leverage the broadcast nature of wireless channels to enhance network performance. Inspired by this, in this paper, we propose an Obstacle aware Opportunistic Data Transmission scheme (OODT) in CRAHNs from a computational geometry perspective, considering energy efficiency and social features. In the proposed scheme, we exploit a new routing metric, which is based on an obstacle avoiding algorithm using a polygon boundary 1-searcher technology, and an auction model for selecting forwarding candidates. In addition, we prove that the candidate selection problem is NP-hard and propose a heuristic algorithm for candidate selection. The simulation results show that the proposed scheme can achieve better performance than existing schemes.

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.

With the development of the maritime networks, the data of vessel users is growing exponentially, and more and more resource intensive tasks, such as multimedia applications, high-definition video playback and games, appear in the daily demands. These changes have greatly increased the energy consumption and bandwidth requirements of vessel terminals and networks. In order to meet the requirements of high bandwidth and low delay for the high-speed development of mobile network, and reduce the network load, the concept of mobile edge computing (MEC) is proposed and has been widely supported by the academia and industry. It is considered to be one of the key technologies of the next generation networks. Inspired by this idea, this paper introduces computing offloading technology to maritime mobile cloud networks. Maritime mobile cloud network is the product of the continuous development of cloud computing technology and mobile Internet technology. In this paper, we studied the issue of computation task offloading for vessel terminals, focusing on minimizing the energy consumption of vessel terminals and the execution delay of computation task. First, it determines that whether if it should be offloaded to the cloud server. Second, the server should be selected to run the computation task. The goal of the optimization is to minimize the energy consumption of vessel terminals and the execution delay of computation task taking into account of different weights. To reduce the execution latency and device energy consumption, we proposed a multivessel computation offloading algorithm based on improved Hungarian algorithm in maritime MEC network. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Maritime intelligent transportation system provides intelligent, safe and efficient maritime transport services, which greatly facilitates the applications related to monitoring, safety, infotainment and cargo online management. The Internet of Things (IoT) is especially suitable for the networked communication environment at sea, and drives the development of maritime intelligent transportation on the trend. However, the existing data processing and forwarding methods pose great challenges to Intelligent Transportation Systems (ITS). In particular, realtime multi-type of data adopts different access technologies in wireless communication systems or use the same wireless access technology but belong to different wireless carriers. Utilizing the existing multi-type wireless communication systems, the architecture of the heterogeneous network through inter-system convergence makes multi-system complement to meet the demand of mobile communication services, so as to comprehensively play their respective advantages. In this paper, we consider a multiattribute decision-making method based on Analytic Hierarchy Process (AHP) and Rough Set based on the architecture of maritime wideband communication system with software defined network (SDN) and fog computing architecture. This paper aims to select a feasible network routing scheme for this heterogeneous network, based on multi-attribute of different networks. Finally, we simulate a communication network selection case based on the future maritime communications architecture, and solve the architecture optimization problem through our proposed algorithm. The issue of such network choice necessarily exists in maritime communications architecture, and our tentative assumptions and solutions will be an important basis for such issues.

Depending on the actual demand of maritime security, this paper analyzes the specific requirements of video encryption algorithm for maritime monitoring system. Based on the technology of Internet of things, the intelligent monitoring system of unmanned surface vessels (USV) is designed and realized, and the security technology and network technology of the Internet of things are adopted. The USV are utilized to monitor and collect information on the sea, which is critical to maritime security. Once the video data were captured by pirates and criminals during the transmission, the security of the sea will be affected awfully. The shortcomings of traditional algorithms are as follows: the encryption degree is not high, computing cost is expensive, and video data is intercepted and captured easily during the transmission process. In order to overcome the disadvantages, a novel encryption algorithm, i.e., the improved Hill encryption algorithm, is proposed to deal with the security problems of the unmanned video monitoring system in this paper. Specifically, the Hill algorithm of classical cryptography is transplanted into image encryption, using an invertible matrix as the key to realize the encryption of image matrix. The improved Hill encryption algorithm combines with the process of video compression and regulates the parameters of the encryption process according to the content of the video image and overcomes the disadvantages that exist in the traditional encryption algorithm and decreases the computation time of the inverse matrix so that the comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm.

Clustering has been extensively used in Vehicular Ad- hoc NETworks (VANETs) for routing optimization and radio resource management, and continues to be considered to facilitate data dissemination in heterogeneous vehicular networks with the ever- increasing data traffic demands. Most of the existing clustering mechanisms in VANETs operate in a distributed mode. However, there is redundant control overhead and transmission decisions, such as cluster maintenance, parameter tuning and forwarding scheduling, which are costly in distributed modes. In this paper, a centralized clustering based hybrid vehicular networking architecture (CC-HVNA) is proposed, in which the collaborative control between IEEE 802.11p and LTE is realized to achieve clustering and to coordinate message delivery. In CC-HVNA, a volatile node state SN is set to reflect ever-changing network topology and to update clusters. Location-based Vehicle to Infrastructure (V2I) communications are utilized to gather regional information so as to perform centralized clusters partition and maintain cluster info table in infrastructures. We leverage a control center to integrate cluster info from the Evolved Node (eNodeB) and Road Side Units (RSUs). Owing to the possession of global cluster info, cluster changes can be detected and targeted data dissemination can be supported according to content-oriented service. The performance evaluation demonstrates that the proposed CC-HVNA clustering scheme can achieve a significant improvement of safety data dissemination.

In this paper, we studied the transmission performance of underwater wireless networks, where underwater network users (UNUs) can transmit their data through wireless optical and acoustic communication in a certain range to improve the overall underwater networks. By jointly considering UNUs' volume of data transferred and overall network transmission performance, we introduced an auction game based optical and acoustic communication mechanism (AGOC). With AGOC mechanism, the base transceiver station (BTS) sells wireless optical communication chances through auctions. The users will decide whether to bid according to their own situation, and then the winner could use wireless optical to transmit finally. The simulation results verified the effectiveness of our proposed algorithm. It also be concluded that AGOC mechanism could improve the overall underwater wireless network performance through reducing the number of UNUs contending for the wireless optical channel.

As an emerging solution to mitigate the problems of the shortage of power resources, electric vehicles (EVs) have advocated to provide safety and convenient driving recently. However, with the ever increasing number of EVs and the new demand of services, how to optimally schedule the charging services becomes a challenge. Therefore, in this paper we present a novel pricing mechanism to optimally schedule the charging demands with user utilities. Firstly, a framework with a nonpreemptive priority charging service is shown for users to queue up. Secondly, based on queuing theory, a novel pricing mechanism is designed to balance the load of charging station by considering the characteristics of different regions and the status of queue. Thirdly, the user utility is studied according to the distance, waiting time as well as the expense, in order to improve the user utility. Finally, simulation results show that the proposed scheme can optimally distribute the charging demand and improve the user utility more efficiently than other conventional methods.

The exponential growth of services demands on the sea drives the development of prospective maritime wideband networks. In this paper, the transmission of surveillance videos on board via a maritime wideband communication network is investigated. The latest Time Division Long Term Evolution (TD-LTE) and delay-tolerant networks (DTNs) technology are combined to construct a shore based network framework in order to provide a wide-range transmission over the sea. Accordingly, a video data store-carry-and-forward routing topology is utilized, tailoring for the intermittent network connectivity to efficiently deliver the video data. This study proposes a Two-step Time and Energy Oriented Optimal-stopping (TTEOO) algorithm leveraging backward induction method, based on the optimal stopping rules to schedule data delivery, under the constraint of end-to-end delay of video data and energy consumption of DTN throw box. Simulation results indicate that the proposed method can achieve low consumption cost and high data delivery ratio for the oversea video transmission applications.

In this paper, we investigate the network throughput and energy sustainability of green-energy-powered maritime wireless communication networks. Specifically, we study how to optimize the schedule of data traffic tasks to maximize the network throughput with Worldwide Interoperability for Microwave Access technology. To this end, we formulate it as an optimization problem to maximize the weight of the total delivered data packets, while ensuring that harvested energy can successfully support transmission tasks. The formulated energy and content-aware vessel throughput maximize problem is proved to be NP-complete. We propose a green energy and content-aware data transmission framework that incorporates the energy limitation of both infostations and delay-tolerant network throw boxes. The green energy buffer is modeled as a G/G/1 queue, and two heuristic algorithms are designed to optimize the transmission throughput and energy sustainability. Extensive simulations demonstrate that our proposed algorithms can provide simple yet efficient solutions in a maritime wireless communication network with sustainable energy.

In this paper, we investigate uploading monitoring videos for vessels via a maritime wideband communication network. The Worldwide Interoperability for Microwave Access (WiMAX) technology is utilized to establish a shore-side network infrastructure, and a packet store-carry-and-forward routing mechanism is implemented to address the intermittent network connectivity in maritime communications. A resource allocation problem is formulated to maximize the weights of uploaded video packets, subject to the intermittent network connections and the release time and deadline of each video packet. Time-capacity mapping is applied to transform the original resource allocation problem to a two-machine non-preemptive scheduling problem. As ship routes are relatively stable, the global information in terms of release time, deadline and other time indices of video packets, as well as the schedules of vessels is known a priori. We propose two offline scheduling algorithms, namely Time-capacity mapping based two phase (TMTP) algorithm, and Interval graph theory based job relay selection (IGTJRS) algorithm. Both algorithms achieve a time complexity of O(n2). The performance of proposed algorithms is evaluated through simulation based on actual ship route traces obtained from dedicated Navigation software BLM-Ship.