With the rapid development of lunar exploration, devices deployed on the lunar surface will encounter emergency events including meteor impacts and lunar dust storms. Additionally, the extreme lunar environment characterized by intense radiation and the high latency of Earth-based cloud computing pose severe challenges to real-time and reliable task processing. Existing collaborative computing and fault-tolerant schemes are not fully efficient in dynamic radiation environments. To address these challenges, a collaborative computing architecture integrating lunar surface devices and lunar orbit satellites is proposed for lunar emergency tasks. Comprehensive network, radiation, communication and computing models are established to support this architecture. The problem is then formulated as a multi-objective optimization problem and solved by the Radiation-aware hiErarchical collAborative Fault-Tolerant Reinforcement Learning (REAFTRL) algorithm, which integrates radiation-aware, hierarchical decision-making, and fault-tolerant execution with feedback mechanisms. Simulations show the proposed collaborative computing scheme outperforms traditional fault-tolerant strategies in task completion time, completion rate, and error rate, providing a reliable solution for future lunar exploration.

IIoT sensors collect large volumes of vibration time-series data at high sampling rates. These data are nonstationary and multi-scale and are essential for condition monitoring across different devices and operating conditions. Deep compression sensing reduces data volume while preserving critical information, enabling efficient and low-cost data processing in IIoT systems. However, existing methods for industrial time-series data processing struggle to preserve features effectively under stringent bandwidth and storage constraints. Moreover, most deep compression sensing models overlook computational limitations and robustness demands in noisy industrial settings. Therefore, we propose an Attention-Based Deep Compressed Sensing with Multi-Loss Optimization for IIoT Vibration Data (DCS-AMTD). The model achieves efficient compression and high-quality reconstruction while improving both resource efficiency and noise robustness. Specifically, we design a dual-path convolutional module that incorporates dilated convolutions to capture multi-scale local and global features. We design a one-dimensional convolutional block attention module (CBAM1D) for industrial vibration signals to dynamically reweight multi-scale features and enhance discriminative representations. Furthermore, we design a joint time-frequency loss with multi-domain constraints to improve reconstruction quality under strict bandwidth and computational constraints. Experiments on the Case Western Reserve University (CWRU) and Paderborn (PB) datasets demonstrate that our method achieves superior reconstruction performance across various compression ratios, outperforming existing approaches.

Compound faults, which arise from the interaction of multiple simultaneous failures, pose significant risks to the maintenance of industrial machinery in the Industrial Internet of Things (IIoT), leading to complex and unpredictable system failures. Traditional models tend to misclassify emerging compound faults as known categories due to a bias toward seen data. Additionally, the delayed emergence of compound faults relative to single faults hampers prompt sample collection. Furthermore, compound fault datasets typically exhibit a long-tailed distribution. Driven by these challenges, we propose an incremental contrastive learning model based on cross-modal contrastive embedding (ICLCFD) to achieve an incremental fault diagnosis from single faults to compound faults.Firstly, we employ Symmetrized Dot Pattern (SDP) image transformation to convert vibration signals into visual representations. We extract high-dimensional visual features from these SDP images using a Multi-Scale Residual Convolutional Neural Network (MS-ResCNN) and generate low-dimensional semantic features based on vibration signals to obtain richer feature information. Subsequently, a novelty detection mechanism is developed using the predicted number of fault sources as a count-based indicator to identify newly emerging faults. Furthermore, we incorporate a difficulty-aware class rebalancing sampling strategy to prioritize hard-to-diagnose samples during incremental updates, mitigating the excessive impact of head-class samples on the diagnosis results. Experiments on three open-source bearing datasets (CWRU, PU, and XJTU-SY) demonstrate that ICLCFD achieves a state-of-the-art accuracy of 96.58 ± 0.28%, outperforming existing methods by approximately 2%. The results confirm its effectiveness in handling unknown and compound faults in dynamic IoT maintenance pipelines.

As low Earth orbit (LEO) satellite constellations expand, the volume of on-orbit data processing increases significantly. However, these systems face challenges in data processing due to heterogeneous data distribution and limited onboard resources. This paper presents an innovative framework for personalized federated learning (PFL) tailored to heterogeneous LEO satellite networks, which mitigates data processing challenges and optimizes distributed computational performance across the satellite constellation. Our approach introduces personalized models built on individual satellite datasets, coupled with dynamic model aggregation and pruning techniques for efficient training. By overcoming data heterogeneity and localizing global models, our method significantly improves performance over traditional approaches. Extensive simulation validates the effectiveness of our PFL framework, which demonstrates its superiority in integrating FL with model pruning in satellite networks. Simulation results show that our proposed PFL method outperforms four comparative algorithms, which highlights its effectiveness in addressing the unique challenges of LEO satellite networks.

Semantic segmentation of high-resolution remote sensing imagery plays a critical role in applications such as disaster assessment. However, deploying large models on Autonomous Aerial Vehicles (AAVs) remains challenging due to inherent conflicts among accuracy, model size, and computational efficiency. To address these challenges, we propose FMC-ULite, a novel lightweight architecture designed to achieve a better balance between accuracy and efficiency for real-time processing. Our model incorporates four key innovations, including a Fast Fourier Transform (FFT)-based fusion module for enhanced edge feature extraction and noise suppression in the frequency domain, a simplified MobileNetV3-Large encoder that substantially reduces parameter count, a cross-layer feature fusion (CLFF) module to effectively integrate multi-scale semantic and detail information, and an attention-gated decoder with multi-scale dilated convolutions to prioritize critical disaster regions. Furthermore, an adaptive combined loss function is introduced to alleviate class imbalance. Experiments conducted on the RescueNet dataset show that our model achieves competitive accuracy compared to advanced lightweight methods under a comparable parameter budget, demonstrating its strong suitability for real-time disaster assessment using AAVs.

In the above article [1], the email address and bio of Muhammad Rizwan are incorrect. The correct information is below:

With the rapid development of vehicle-to-everything (V2X) technology, service migration has become an important approach to provide low-latency computing services and ensure service continuity for high-speed moving vehicles in vehicular edge computing (VEC), which enables VEC to efficiently support advanced transportation services. However, optimizing service satisfaction for service migration in multi-vehicle heterogeneous VEC networks is challenging, since the complex, multifactorial, and nonlinear dependencies between service satisfaction and quality of service (QoS) metrics is intractable, and the rapidly changing computational loads in edge server results in inefficient utilization of edge resources. In this paper, we propose a service Satisfaction-based Adaptive service Migration and resource Allocation joint Optimization scheme (SAMAO) to improve service migration efficiency and edge resource utilization in VEC. Firstly, we develop an adaptive computation resource allocation algorithm that can adjust resource allocation strategy according to load status of edge servers to improve vehicle service satisfaction. Then, to minimize energy consumption and ensure service satisfaction for vehicles, we propose a utility maximization algorithm to formulate migration decisions based on pre-allocated computation resources on servers. Finally, numerous simulations based on Shanghai Telecom real-world dataset show that SAMAO can achieve significant advantages in terms of average service satisfaction and computation cost.

In intelligent transportation systems, the ability to make precise and efficient lane-changing overtaking decisions is essential for improving traffic flow, safety, and overall efficiency. However, the coexistence of both connected and non-connected vehicles, driven by the high cost of full deployment and the incomplete global adoption of standardized communication standards, has led to the emergence of Mixed Connected and Connectionless Vehicles (MCCV) scenarios. These scenarios complicate lane-changing overtaking decisions, as the unpredictability of connectionless vehicles, particularly in dense traffic, poses significant challenges and increases safety risks. Furthermore, incorporating fairness into decision-making is vital to ensure equitable treatment of all vehicles, which is key to improving road safety and traffic efficiency. To address these challenges, we propose a fairness-aware overtaking decision optimization method for MCCV scenarios, which aims to enhance fairness while improving safety and efficiency in vehicle decision-making. First, a Bayesian network-based fairness assessment method is introduced to quantify fairness under limited data conditions by modeling the probabilistic relationships between vehicle behaviors and fairness outcomes. Second, we develop a left-lane availability detection mechanism based on adaptive maneuver tree search and a fast-lane speed recommendation mechanism grounded in traffic flow analysis. These mechanisms enhance compliance with traffic regulations and improve efficiency by dynamically assessing lane conditions and providing a real-time speed recommendation based on traffic density and flow. Finally, we incorporate a K-Nearest Neighbor (KNN)-enhanced deep reinforcement learning approach, which integrates a parameterized dueling deep recurrent Q-network with KNN-enhanced experience replay. This approach effectively copes with rare but critical driving conditions, such as unpredictable vehicle behaviors or sudden traffic dynamics, improving decision-making reliability in various traffic scenarios. Extensive simulations demonstrate that the proposed method significantly enhances the fairness, efficiency, and safety of lane-changing overtaking decisions in MCCV scenarios, effectively addressing the unpredictability of mixed-vehicle interactions.

Network Function Virtualization (NFV) facilitates on-demand and flexible service provisioning to meet the escalating demands of Internet of Things (IoT) applications, enabled by Service Function Chain (SFC) technique. The widespread deployment of 5G has connected a massive number of devices and users to IoT networks, accelerating the expansion of IoT scales. IoT users’ service requirements exhibit heightened diversity and dynamism. Consequently, the SFC placement problem in Next Generation Multi-domain IoT (NGMIoT) networks has garnered significant attention. How to efficiently place SFCs under uncertain resource demands to adapt to evolving service request dynamics poses substantial challenges. Therefore, this paper investigates the Robust SFC Placement (RSFCP) problem in NGMIoT networks under resource demand uncertainty. Specifically, we formulate the RSFCP problem as an integer linear programming model to minimize overall SFC placement cost while ensuring service quality. We further prove the RSFCP problem is NP-hard and propose a greedy strategy based heuristic SFC placement algorithm to solve it. Finally, extensive simulation experiments are conducted to evaluate performance, demonstrating that the proposed algorithm outperforms benchmark mechanisms in terms of service acceptance rate and placement cost.

In cloud-edge-end (CEE) collaboration, the resource optimization based on deep reinforcement learning have achieved significant performance improvements in time-slot systems. However, some studies only focus on computing delay and energy consumption in each time slot, ignoring the impact of task backlog queues on system performance. In addition, the delay-oriented optimization tends to offload a large number of tasks to servers, failing to fully utilize the computing resource of mobile devices. To address these issues, we propose the multi-dimensional preference-driven resource optimization framework (MPROF). This study includes several key points: 1) constructing a three-layer heterogeneous architecture that applying the collaboration among edges for CEE; 2) proposing the task backlog estimation mechanism, which mitigates the impact of previous unfinished tasks on the current time slot; 3) proposing the group relative direct-preference policy optimization (GRDPO) that incorporates the preference information for efficient task offloading, and combines it with mathematical programming for the system resource optimization. The simulation experiments are conducted across multiple typical scenarios. The results show that, the proposed framework outperforms existing mainstream methods in task offloading, system delay, task backlog, and energy consumption control, demonstrating certain practical application prospects.

In Vehicular Edge Computing (VEC), load imbalances among edge servers, driven by varying traffic densities and computational demands across geographic areas, can lead to significant delays, decreased efficiency, and potential service disruptions, adversely affecting both user experience and system reliability. This study proposes an innovative adaptive load balancing method that integrates deep reinforcement learning with predictive analytics to optimize resource allocation in VEC. The framework comprises a predictive model called ST-ChebNet, enhanced with Chebyshev polynomials in graph convolutional networks for accurate workload forecasting, and an adaptive model compression strategy utilizing knowledge distillation to dynamically adjust compression ratios based on anticipated workloads. Additionally, the integration of this predictive model with the Soft Actor-Critic (SAC) algorithm, termed GC-SAC, effectively combines graph-based predictive insights with reinforcement learning techniques to tailor resource distribution, minimizing computational delays and enhancing system responsiveness. The simulation results show that the GC-SAC algorithm can significantly reduce the average delay and average energy consumption of vehicular tasks, as well as the workload rate of edge servers.

In order to meet unprecedented demands for reliable and massive connectivity (MC), sixth-generation (6 G) cellular Radio Access Networks (RANs) require architectural innovations. Conventional cellular RANs' scalability is limited by the tightly coupled control and user planes. Fully-Decoupled RANs (FD-RANs) are a promising architectural innovation that enables flexible plane separation. However, current architectures have limitations due to ineffective multiple access schemes. To this end, we introduce NomaFdRaN, an innovative Non-Orthogonal Multiple Access (NOMA)-optimized FD-RAN architecture in order to optimize reliability and MC. To achieve holistic system optimization, NomaFdRaN applies NOMA on all network planes, control plane and user plane, and transmission paths, uplink and downlink. To improve NOMA efficiency, we develop a user pairing optimization approach that minimizes total transmit power while maintaining linear computing complexity. Based on stochastic geometry, we develop analytical models to analyze NomaFdRaN's performance. Subsequently, we analytically derived closed-form expressions for key performance metrics. Our simulation results demonstrate the effectiveness of the NomaFdRaN architecture and provide insights into the deployment strategies for next-generation FD-RANs.

The advancement of Low Earth Orbit (LEO) satellite technology has enabled rapid progress in on-orbit machine learning. However, limited on-board computational resources hinder large-scale model training on individual satellites. Furthermore, the highly dynamic network topology and resource heterogeneity of LEO satellite constellations make collaborative training prone to single-point failures and privacy risks. To address these issues, this paper proposes ConSFL, a lightweight Contrastive learning-driven Split Federated Learning framework. ConSFL enables local feature extraction from unlabeled remote sensing data under resource-constrained conditions, while preserving both model completeness and data privacy. By performing federated learning across heterogeneous submodels, the training of the global model can focus on learning features within specialized semantic dimensions, thereby enhancing overall performance. Additionally, we introduce a spatial attention pooling (SAP) method into ConSFL to aggregate intermediate features with larger feature map sizes from submodel outputs. Simulation results show that ConSFL achieves higher Top-1 accuracy across submodels compared to the best baseline, while SAP enhances ConSFL's ability to capture feature-space information and improves submodel performance under earlyexit mechanisms.

Load imbalance in distributed key-value (KV) storage systems, especially under skewed access patterns across racks or clusters, can significantly degrade performance. We present CacheMigrate, a switch-assisted caching and migration framework that leverages programmable Top-of-Rack (ToR) switches for real-time hotspot detection, in-network caching, and per-key migration state tracking. By decoupling data movement from request handling, CacheMigrate minimizes service disruption and ensures strong consistency during live migration. A distributed migration state management design coordinates ToR and spine switches without centralized bottlenecks. We implement CacheMigrate on a P4-programmable switch and evaluate it using realistic workloads. Results show that CacheMigrate consistently delivers higher throughput and better load balance than existing approaches across diverse workload and system configurations.

RPL, the standard routing protocol for low-power and lossy networks, offers two modes for handling downward traffic: storing and non-storing. Each has significant limitations - the storing mode struggles with router memory constraints, potentially making destinations unreachable when a router's storage capacity is reached. Conversely, the non-storing mode mitigates this issue by employing source routing, but at the expense of increased network overhead. To address these challenges, this paper introduces the Leaf-Centric Mode (LCM), a novel approach that dramatically reduces storage requirements by enabling nodes to maintain routing information only for leaf nodes within their sub-networks, rather than all nodes. This optimized approach offers key advantages for IoT applications, including reduced storage footprint, improved reliability, lower network overhead, and enhanced overall performance. Through comprehensive experimental evaluation, we demonstrate the practical effectiveness of the LCM and establish its viability for IoT applications.

The purpose of this research is to develop and evaluate an adaptive cooperative spectrum-sharing framework for Hybrid Satellite-Terrestrial Networks (HSTNs), incorporating realistic hardware impairments and flexible Amplify-and-Forward (AAF) and Decode-and-Forward (DAF) relaying to enhance reliability, spectral efficiency, and power allocation. This study employs a mathematical and simulation-based approach to evaluate Overlay Cognitive Hybrid Satellite-Terrestrial Networks (OCHSTNs) under realistic hardware impairments and fading conditions. Adaptive Relay Protocol (ARP) is analyzed for both AAF and DAF schemes, incorporating Shadowed-Rician and Nakagami-m channels with Additive Gaussian Noise. Closed-form Outage Probability (OP) expressions for primary and secondary networks are derived, and Monte-Carlo simulations validate analytical results. The method also examines Relay Cooperation Ceiling (RCC) and Data Link Ceilings (DLC) to assess network reliability and spectrum-sharing efficiency. The research demonstrates that ARP significantly improves outage performance and spectral efficiency in HSTNs under realistic hardware impairments. DAF relaying exhibits superior resilience compared to AAF, maintaining higher data rates and reliability. Critical ceiling effects, including RCC and DLC, are identified, highlighting performance limitations at high thresholds. Flexible relaying protocols effectively mitigate Hardware Deficiencies (HDs), ensuring robust primary and secondary network operation while optimizing power allocation and spectrum sharing. The study provides practical guidance for designing resilient hybrid satellite-terrestrial networks, showing that adaptive DAF relaying and flexible spectrum-sharing strategies enhance reliability, mitigate hardware impairments, and optimize network efficiency.

Recently, fixed-wing unmanned aerial vehicles (UAVs) are able to extend the communications mission time and ease of deployment due to their powerful onboard capabilities and flexibility, and reflective intelligent surfaces (RISs) are capable of reflecting links to avoid obstacles and thus improve channel gain. Therefore, RIS-assisted fixed-wing UAVs are widely used in wireless communications. Nevertheless, fixed-wing UAVs have limited energy, so improving energy efficiency is critical. This article focuses on energy efficiency optimization problems under RIS-assisted fixed-wing UAV communications. In communications coverage systems, the flight trajectory of fixed-wing UAVs and service scheduling to ground nodes (GNs) significantly impact energy efficiency. Existing work often adopts circular trajectory and traditional deep reinforcement learning (DRL) algorithms for optimizing trajectory and service scheduling. However, circular trajectory can not adapted to the GN distribution well. In addition, the traditional DRL algorithm has two drawbacks: 1) the efficiency of exploring the empirical process is low and 2) the accuracy of handling the hybrid action space needs to be higher. Thus, we propose the midpoint iteration convex hull (MICH) algorithm based on the Graham scan to design trajectories that can be adapted to the distribution of the GNs. In addition, we propose the action screening virtual and real experience (AS-VRE) mechanism and the N-steps hybrid deep Q and policy network (NsHQPN) algorithm to address the low-exploration efficiency and the low-fetch accuracy in handling the hybrid action space. Experiments show that our proposed MICH algorithm, AS-VRE mechanism, and NsHQPN algorithm can effectively improve the system energy efficiency and outperform other baseline schemes.

Reconfigurable-intelligent-surface (RIS)-assisted autonomous aerial vehicles (AAVs) communications technology improves energy efficiency by reflecting signals. This article utilizes RIS and deep reinforcement learning (DRL) to optimize the scheduling of ground terminals (GTs), AAV trajectories, resource allocation, and time slot lengths to maximize system energy efficiency. Three flaws of the existing DRL algorithm are also addressed to seek higher energy efficiency further. First, DRL faces exploration challenges due to the complexity of the solution space, resulting in low rewards. We propose the ant colony DRL (ACDRL) algorithm, which optimizes the scheduling order of the GTs using the ant colony optimization (ACO) algorithm and feeds the results back to the DRL to optimize the subsequent decision making, thus reducing the exploration overhead. Second, to reduce the degree of local optimization when dealing with hybrid action space planning, we propose a hybrid discrete-continuous DRL (HDCDRL) algorithm to improve action accuracy. Finally, to better generalize the model to similar tasks, we propose the transfer-DRL (T-DRL) model to reduce the training time when the task changes. Experimental results show that our proposed solution outperforms the benchmark solution.

Satellite-terrestrial integrated networks have recently gained substantial interest due to their exceptional coverage, lower transmission delay, robust storage, and computing power. However, existing task offloading schemes often fail to effectively manage task dependencies, resulting in incorrect execution sequences, increased end-to-end delay, and excessive energy consumption. To address these challenges, we propose a dependency-aware task offloading framework for jointly optimizing delay and energy consumption in satellite-terrestrial collaborative networks with mobile-edge computing (MEC). First, we construct a directed acyclic graph (DAG) based dependency-aware task offloading framework aimed at reducing delay and energy consumption. Second, to reduce the frequency of low Earth orbit (LEO) satellite access, we design a cluster head selection strategy (CHSS), which leverages DAG-based task dependencies to optimize the association between Internet of Things (IoT) devices and LEO satellites. Finally, we formulate system delay and energy consumption as a cost-minimization problem, modeling it as a Markov decision process (MDP). We also propose a novel hybrid deep reinforcement learning (DRL) algorithm to effectively handle DAG structures and optimize task offloading decisions, thereby minimizing the total cost. Extensive simulation results confirm the effectiveness of the proposed method, demonstrating that the proposed algorithm significantly outperforms others by reducing system delay by 18.07% and decreasing energy consumption by 21.15% on average, respectively.

With the development of artificial intelligence technology, Autonomous aerial vehicles (AAV) have the ability to sense the environment. multiple object tracking (MOT) in AAV video is a very important vision task with a wide variety of applications. However, there are still many challenges in MOT in AAV video. First, the movement of the onboard camera in the three-dimensional (3-D) direction during the tracking process, as well as the unpredictable measurement noise characteristics of AAVs flying at high speeds, can lead to significant deviations in the prediction of the object’s position. Second, the applicability of the traditional detection algorithm decreases when the object is small and dense in the AAV viewpoint during detection. Finally, the traditional intersection over union (IoU) matching approach does not take into account the effects of the height and width of the box, and the matching results are inaccurate for the prediction and detection box. In order to address these challenges, we recommend the adaptive DeepSort (ADT) algorithm to reduce the prediction bias due to camera movement and difficulty in predetermining measurement noise characteristics, the hybrid attention transformer-YOLOv8 (HAT-YOLOv8) algorithm to enhance the detection capability of tiny objects, and the IoU of height and width (HWIoU) matching algorithm, which improves the matching accuracy and thus the tracking accuracy. Experimental results show that our proposed solution outperforms the baseline solution. It outperforms the current mainstream StrongSort in MOTA, HOTA and IDF1 by 2.86%, 0.9%, and 9.36%. Code repository link:https://github.com/networkcommunication/.

Efficient monitoring of carbon capture and storage (CCS) systems heavily relies on sensor data. However, sensors are susceptible to potential multiple faults, leading to performance degradation and posing a risk of catastrophic failures. Timely detection of sensor faults in CCS systems is crucial for safe and efficient carbon dioxide (CO2) pipeline operation. This paper addresses the challenge of diagnosing multiple sensor faults in CO2 pipelines by introducing a novel approach based on partial-distributed particle filter (PDPF). The novel distributed-filtering framework aims to reduce the computational complexity while identifying multiple faults in highly nonlinear systems. The proposed PDPF architecture comprises a collection of linear local filters and a nonlinear main filter. More specifically, the algorithm segregates nonlinear computations from local filters and assigns them to the main filter. The main filter handles the time updates involving all nonlinear computations associated with the nonlinear system, while the parallel linear local filters, each equipped with a distinct subset of sensor measurements, perform the measurement updates and combine their estimates via information fusion. As for fault detection and isolation, each local filter utilizes a novel kernel density estimation (KDE)-based approach that analyzes the consistency between model predictions and observed behavior, enabling the identification of sensor faults. Compared to existing methods, this approach reduces the computational requirements and is well-suited for highly nonlinear systems experiencing multiple sensor faults. Additionally, performance assessment via numerical simulations confirms its effectiveness and superiority in comparison to stateof-the-art alternative methods.

The flocking movement is a fundamental and crucial operation in multi-AAVs systems, encompassing navigation and obstacle avoidance. However, traditional flocking algorithms typically rely on rigid rules and exhibit limited adaptability to diverse environments. Reinforcement learning (RL) effectively addresses this issue as a flexible and model-free framework. In this article, RL techniques are utilized to achieve navigation and obstacle avoidance for a swarm of AAVs. To enhance training efficiency, we propose an improved algorithm called heuristic guides TD3 (HGTD3) by integrating heuristic guides with the twin delayed deep deterministic policy gradient (TD3), aiming to address the protracted learning periods commonly observed in traditional RL methods. Considering the network-constrained environment, we propose the negative interference flocking algorithm (NIFA): the network interference flocking algorithm and an AAV flocking algorithm designed based on the sparrow search algorithm. NIFA can guide the losing AAV to follow the swarm and at the same time maintain the overall navigation and avoidance efficiency. Finally, we demonstrate the scalability and adaptability of HGTD3-NIFA in a simulation experiment in terms of multi-AAVs flocking and navigation.

In modern vehicular networks, the absence of infrastructure support, such as roadside units (RSUs), presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates deep reinforcement learning (DRL) and long short-term memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the convoy formation algorithm considering future positions (CFA-FPs) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies.

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs’ perception capacity of the region of interest (RoI) while alleviating the pressure of intensive computation on onboard resources. However, existing CP and computation schemes are based on inefficient broadcast communications and still face challenges, such as highly dynamic communication link channel conditions caused by vehicle mobility, and limited computing resources in VEC environments. Considering the delay sensitivity of CAVs’ perception tasks and the need for enhanced perception, we propose a unicast-based cooperative perception and computation scheme to achieve more efficient resource utilization and perception task execution in VEC scenarios. Our goal is to maximize CP gain and minimize task execution delay by optimizing the decision of each ego CAVs. To solve the sequential decision-making problem of multiobjective optimization, we propose a solution based on improved multiagent proximal policy optimization deep reinforcement learning, where CAVs agents make adaptive decisions distributed based on partial observations. Simulation results show that compared with the baseline algorithm, our proposed scheme effectively reduces the execution delay of ego CAVs perception tasks and ensures a high perception gain.

Satellite Edge Computing (SEC) enhances real-time data processing by deploying computational resources at the edge of satellite networks, reducing Task Completion Delay (TCD). To minimize TCD, the characteristics of satellites with large coverage, strong collaborative capabilities, and limited resources must be fully considered. This paper proposes a novel three-stage task offloading framework to optimize task execution in dynamic and resource-constrained satellite environments. First, to efficiently offload computational tasks among large-scale, heterogeneous users, we introduce a region division-based offloading strategy and develop the Adaptive Division Offloading Region (ADOR) algorithm, which dynamically partitions satellite coverage areas to improve offloading efficiency. Second, to enhance the collaborative computing capabilities of Low Earth Orbit (LEO) satellite constellations, we propose a Particle Swarm Optimization Genetic (PSOG) algorithm to optimize TS under dynamic conditions. Finally, to tackle the limited and interdependent computing resources of satellites, we design an Intelligent Parameter Adjustment (IPA) algorithm based on Q-learning, which dynamically adjusts computational parameters to maximize processing speed while ensuring system stability. Simulation results demonstrate that our proposed framework outperforms existing methods. Compared with the baseline algorithm, it achieves higher task offloading efficiency and better resource allocation. Additionally, it maintains stable satellite operations while reaching the highest processing speed.

The capacity of Dataspace enables the distribution of heterogeneous data from several sources and domains and has attracted attention for resolving data integration challenges. Drone data sharing faces challenges such as protecting privacy and security, building trust and dependability, controlling latency and scalability, facilitating real-time data processing, and preserving the caliber of shared models. Therefore, sixth-generation (6G) networks provide high throughput and low latency to improve drone operations; security issues are exacerbated by the sensitive nature of shared data and the lack of centralized monitoring. To address the challenges, this paper presents a conceptual framework for a Dataspace in the Sky to enable secure and efficient drone data-sharing within 6G networks in the transition from Industry 4.0 to Industry 5.0 . The Dataspace in the Sky integrates Federated Learning (FL), a decentralized Machine Learning (ML) approach that enhances security and privacy by sharing models instead of raw data, facilitating effective drone collaboration. However, the quality of shared local models often suffers due to inconsistent data contributions and unreliable recording mechanisms, which can undermine the performance of FL. To tackle the challenges, the framework employs blockchain (BC) to decentralize and secure the Dataspace, ensuring the integrity of contribution records and improving the reliability of shared models. Dataspace in the Sky empowered decentralized data sharing which addresses latency issues by decentralizing decision-making and enhances trust and reliability by leveraging immutable and transparent BC mechanisms. The robustness of Dataspace in the Sky solution is not only secures drone-sharing operations in 6G environments but enables the development of citizen-friendly mobility services, expanding opportunities across smart environments.

Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments.

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model$l_{c}, l_{s}$-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack ($V_{c0}$) and a Vulnerable sensitive attribute attack ($V_{sa}$) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.

With the recent advancement in network technologies, the vehicle based medical networks extend medical services to mobile vehicles, thereby offering flexible and efficient healthcare services for vehicle users in need. The integration of vehicle based medical network and edge computing enables computation intensive medical service tasks to be offloaded on edge servers, to provide fast service response for vehicle users. An efficient task offloading and resource allocation strategy is critical for Vehicle based Medical Edge Computing System (VMECS) to satisfy real-time and reliability requirements while ensuring service performance. To this end, in this paper, we investigate the problem of task computation allocation in VMECS networks. By introducing deep reinforcement learning, we first present a novel VMECS architecture to automatically achieve the optimal task offloading and resource allocation through the multi-agent collaboration, thereby improving service performance. Then, we formulate the problem of task offloading and resource allocation in VMECS networks as an optimization model with the aim of maximizing task success rate by jointly considering communication interferences, resource allocation and delay requirements. To solve it, we further devise a Distributed distributional deterministic policy gradients based Task offloading and Resource allocation (DTR) algorithm. Final simulation results demonstrate that compared with benchmark algorithms, DTR algorithm can obtain higher task success rate, smaller service time, and less task processing time.

In post-disaster emergency medical rescue operations, rapidly establishing an adaptive and flexible edge computing (EC) network, balancing data offloading with energy consumption, and ensuring the stable operation of the network have become urgent priorities. To address these challenges, we proposed an unmanned aerial vehicle (UAV)-assisted microservice mobile edge computing (MEC) architecture. The architecture can be rapidly deployed to provide temporary network coverage and EC services in disaster-stricken areas. A transformer-based resource management (TBRM) approach is utilized to optimize data offloading efficiency and reduce energy consumption, thereby maximizing the service time of the architecture. To enhance the security and reliability of the architecture, four microservices are designed to manage the full UAV lifecycle, and UAV identity authentication is implemented through dual digital signature certificates. Large-scale simulation experiments have demonstrated the effectiveness of the architecture in complex rescue scenarios, providing strong technical support for postdisaster medical rescue efforts.

Autonomous aerial vehicles (AAVs) have found widespread utility in the field of multi-target tracking (MTT) due to their inherent advantages, such as ease of deployment, flexible maneuverability, and cooperative communication capabilities. AAVs can perform tasks ranging from regional surveillance to tracking and search-and-rescue operations in hazardous environments. Nonetheless, the issue of how to efficiently coordinate multiple AAVs to track diverse mobile targets remains a critical concern. This paper focuses on MTT with multi-AAV, aiming at optimizing system performance in scenarios where network availability is limited. In contrast to treating sensor perception as a monolithic process, we propose a scheme for cooperative sensing and data processing. This scheme is designed to reduce system response latency in environmental information sensing and multidimensional data processing for multiple AAVs. Furthermore, in contrast to assuming linear target movement and single-step target position prediction, we introduce a multi-agent deep reinforcement learning (MADRL) framework combined with multi-step prediction extended Kalman Filter (MP-EKF). This framework is tailored to enhance tracking precision, especially when targets’ trajectories are curved, which can reduce AAV flight displacement if the target’s position can be predicted multiple steps later. In addition, unlike using latency as a real-time application to measure the “freshness” of information, the Age of Information (AoI) is introduced for considering the waiting time of transmission and calculation between multiple AAVs. This assessment method is utilized to comprehensively evaluate and mitigate data latency within the MADRL algorithm. Finally, extensive simulation experiments demonstrate that the proposed scheme significantly outperforms both baseline methods and state-of-the-art approaches in terms of AoI, system latency, and energy consumption.

Radio Access Networks (RAN) are anticipated to gradually transition towards Cloud RAN (C-RAN), leveraging the full advantages of the cloud-native computing model. While this paradigm shift offers a promising architectural evolution to improve scalability, efficiency, and performance, significant challenges remain in managing the massive computing requirements of physical layer (PHY) processing. To address these challenges and meet the stringent Service Level Objectives (SLOs) in 5G networks, hardware acceleration technologies are essential. In this paper, we aim to mitigate this challenge by offloading 5G modulation mapping, a critical yet demanding function to encode bits into IQ symbols, directly onto the switch ASICs. Specifically, we introduce NetMod, a 5G New Radio (NR) standard-compliant in-network modulation mapper accelerator. NetMod leverages the capabilities of new-generation programmable switches within the C-RAN infrastructure to offload and accelerate PHY modulation functions. We implemented a NetMod prototype on a real-world platform using the Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Through extensive experiments, we demonstrate that NetMod achieves modulation mapping at switch line rate using minimal switch resources, thereby preserving ample space for traditional switching tasks. Furthermore, comparisons with a GPU-based 5G modulation mapper show that NetMod is 2.2$\boldsymbol{\times}$to 3.3$\boldsymbol{\times}$faster using only a single switch port. These results highlight the potential of in-network acceleration to enhance 5G network performance and efficiency.

In 5G Radio Access Networks (RAN), Cyclic Redundancy Check (CRC) algorithms play a vital role in detecting accidental changes to digital data during transmission. However, due to the massive bandwidth demands in 5G networks, CRC computation is a resource-intensive process. To address this challenge, we propose performing CRC computation and verification directly in the network path. Specifically, we introduce NetCRC-NR, a 5G New Radio (NR) standard-compliant in-network CRC accelerator. NetCRC-NR implements the 5G NR CRC algorithms specified in 3GPP TS 38.212, including CRC24A, CRC24B, CRC24C, CRC16, CRC11, and CRC6. It leverages programmable switches to perform in-network CRC generation and validation for the Transport Blocks (TBs) and Code Blocks (CBs), aiming at providing high CRC computation throughput and alleviating the computational burden on General-Purpose Processors (GPPs). We design and implement NetCRC-NR on Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Extensive experiments demonstrate that NetCRC-NR performs CRC generation and verification at the switch line rate of up to 4+Tbps CRC throughput, showcasing its efficiency and potential in accelerating the 5G RAN error detection process.

Unmanned aerial vehicle (UAV) has been considered a promising technology for advancing terrestrial mobile computing in the dynamic environment. In this research field, throughput, the number of completed tasks and latency are critical evaluation indicators used to measure the efficiency of UAVs in existing studies. In this paper, we transform these metrics to a single optimization objective, i.e., throughput maximization. To maximize the throughput, we consider realizing this goal in two respects. The first is to adapt the formation of the UAVs to provide cooperative computing service in a dynamic environment, we integrate a policy-based gradient algorithm and the task factorization network as a new reinforcement learning algorithm to improve the cooperation of UAVs. The second is to optimize the association process between UAVs and users, where the heterogeneity of tasks is considered. This algorithm is modified from the Gale-Shapley stability concept to optimize the appropriate association between tasks and UAVs in a dynamic time-varying condition to get the near-optimal association with few iterations. The scheduling of dependent tasks and independent tasks jointly also has to be considered. Finally, simulation results demonstrate the improvement of cooperation performance and the practicability of the association process.

Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-scale computing nodes near the edge side, reducing application latency for MUs. The resource allocation and task offloading in MEC has been widely studied. However, the burgeoning complexity inherent to modern applications, often represented as Directed Acyclic Graphs (DAGs) comprising a multitude of subtasks with interdependencies, poses huge challenges for application offloading and resource allocation. Meanwhile, previous work has neglected the impact of edge caching on the offloading execution of dependent tasks. Therefore, this paper introduces a novel dynamiccaching dependency-aware taskoffloading (CachOf) scheme. First, to effectively enhance the rationality of cache and computing resource allocation, we develop a subtask priority computation scheme based on DAG dependencies. This scheme includes the execution sequence priority of subtasks on a single MU and the offloading sequence priority of subtasks from multiple MUs. Second, a dynamic caching scheme, designed to cater to dependent tasks, is proposed. This caching approach can not only assist offloading decisions, but also contribute to load balancing by harmonizing caching resources among edge servers. Finally, based on the task prioritization results and caching results, this paper presents a Deep Reinforcement Learning (DRL)-based offloading scheme to judiciously allocate resources and improve the execution efficiency of applications. Extensive simulation experiments demonstrate that CachOf outperforms other baseline schemes, achieving improved execution efficiency for applications.

With lithium-ion batteries prevalent in safety-critical industries, accurate and reliable estimation of battery state-of-health, termed trustworthy prognosis, has become crucial. However, neglecting model uncertainty during representative correlation learning may lead to inappropriate aggregation of ambiguous segments. Moreover, shifts in dominant spatial channels and sparsity in useful temporal features further hinder the trustworthy estimation. Furthermore, the misalignment between the predicted and actual distributions leads to a confidence biases issue and degrades performance in distinguishing out-of-distribution samples. To address these challenges, we propose a hierarchical stochastic spatial–temporal Transformer (HSSTT). First, HSSTT implements stochastic self-attention utilizing Gumbel–Softmax reparameterization for uncertainty quantification. Then, a hierarchical spatial–temporal Transformer is designed to leverage uncertainty-aware timestep-wise dilated convolution and clustered stochastic self-attention. Finally, we theoretically analyse the confidence bias issue through bias-variance decomposition and develop a principled calibration strategy. Experimental results on four datasets demonstrate the superiority of HSSTT in trustworthy prognosis against seven State-of-the-Art models.

Vehicle trajectory prediction, as a key enabler of the intelligent transportation system, has attracted considerable attention from academia and industry in recent years. However, the variability and dynamism of traffic conditions pose significant challenges to current vehicle trajectory prediction methods, particularly in the form of domain bias. Domain bias occurs when a model trained on one traffic domain, such as one segment of a highway, underperforms when applied to another segment with different traffic patterns. To address this challenge and advance the field, we propose a new transfer learning-based collaborative vehicle trajectory prediction framework called TRACER, designed to provide reliable and accurate traffic predictions with high adaptability for cross-domain highway traffic scenarios. The core of our framework lies in an adaptive interactive extraction module and a trajectory generation module based on Bidirectional Long Short-Term Memory (BiLSTM), further strengthened by a pre-task of intention recognition for vehicle operation types. To improve model robustness, consistency regularization is applied by injecting disturbances into the target data, and a one-dimensional Convolution (Conv1D)-based intention extraction module is integrated into the BiLSTM-based trajectory generation process, leading to notable improvements in prediction accuracy. Our framework is first trained on source domain data, followed by the transfer of a small amount of labeled data from the target domain, and the overall model is further refined using unlabeled data. By effectively mitigating domain bias, TRACER significantly enhances trajectory prediction accuracy while maintaining high adaptability. The results underscore the importance of addressing domain shift challenges in trajectory prediction tasks and demonstrate the potential of domain adaptation techniques to improve the prediction accuracy of vehicle trajectories across different domains in highway scenarios.

Vehicular Edge Computing (VEC) is an emerging computing paradigm that serves as a specific application of Mobile Edge Computing (MEC) in intelligent transportation systems. As a core technology of VEC, task offloading improves computing efficiency and service quality by offloading computing tasks from vehicular devices to edge nodes. However, the high mobility of vehicles, heterogeneity of resources, and real-time requirements present significant challenges for task offloading. To address the issue of reducing overall system latency and increasing the offloading success rate in multi-task offloading, we propose a task offloading scheme combining Deep Reinforcement Learning (DRL) and Kuhn-Munkres (KM) Matching algorithm, named the PDQKM offloading scheme. Firstly, to mitigate the delay caused by frequent Roadside Unit (RSU) handovers, we propose a method to detect whether a vehicle is within the coverage area of the RSU. This method filters out unreasonable offloading decisions, avoiding the overhead associated with frequent RSU handovers. Secondly, the combination of DRL and the KM matching algorithm leverages the strengths of both approaches. DRL provides initial offloading strategies in highly dynamic and high-dimensional decision environment. Although DRL may get stuck in local optima, it can quickly adapt to environmental changes. The KM matching algorithm, a classic solution for perfect task-resource matching, performs global optimization on the initial strategies provided by DRL. This integration overcomes the limitations that a single algorithm might have. Finally, to effectively coordinate and manage the heterogeneous resources of RSU, we utilize an improved KM matching algorithm to update computational resources in real time, enhancing matching efficiency. Experimental results demonstrate that PDQKM outperforms comparable offloading schemes in terms of overall system latency and offloading success rate optimization.

Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose anenhancinGedge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNgscheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.

Unmanned aerial vehicles(UAVs), or drones, are garnering considerable focus in the realm of wireless communications research because to their notable characteristics, including exceptional mobility, versatile deployment capabilities, and robustness in maintaining line-of-sight (LoS) links. This paper studies a UAV-assisted bidirectional relaying system for multi-pair user devices (UDs), where a rotary-wing UAV is used to serve as a mobile relay for providing information transmission between UDs belonging to a pair on the ground. The UDs in a pair communicate with each other via the UAV relay employing the physical-layer network coding (PNC) technique. To trade off fair communications with system energy consumption, we jointly optimize transmission scheduling and association, UAV relay and UD transmission power, and UAV trajectory to maximize system energy efficiency during UAV relay communications. Due to the formulated problem being mixed-integer and nonconvex programming, it proves to be excessively complex to solve. For ease of solution, this problem is initially decomposed into three sub-problems. Next, by adopting the block coordinate descent (BCD) method, the successive convex approximation (SCA) method, and the Dinkelbach method, an efficient iterative algorithm is proposed that alternately solves variables of each sub-problem while fixing others. The numerical results demonstrate that our designed scheme is capable of substantially improving the system energy efficiency in comparison with other baseline schemes and benchmark schemes.

Self-healing group key distribution (SGKD) protocols guarantee the security of group communications by allowing authorized users to independently recover missed previous session keys from the current broadcast without retransmission. However, existing SGKD protocols have flaws: (1) collusion resistance and revocable nodes are both upper-bounded by the degree of polynomials used, (2) the disclosure of personal secrets enables the recovery of group key, (3) temporary revocation of a group member is not possible, and (4) a revoked node may obtain the session key when initiating mutual healing, moreover, a malicious node may cause the recovery of false group keys. To address these limitations, we propose an SGKD protocol using the Chinese remainder theorem (CRT) and Physical Unclonable Function (PUF). Our proposed SGKD protocol generates a PUF-based dynamic secret by stimulating nodes’ PUF using a polynomial-based encrypted challenge. This secret is then employed to retrieve a CRT-based encrypted group key. By combining PUF and CRT, we can generate dynamic secrets on the fly and reduce computation time significantly. Utilizing such a technique, our protocol achieves superior security goals, including resistance to any coalition of group nodes even if nodes’ personal secrets were disclosed. Furthermore, the proposed protocol provides an unlimited number of revocable nodes. Additionally, a revoked node can rejoin its group in later sessions without affecting backward secrecy. Moreover, the protocol provides a backward secrecy guaranteed mutual-healing feature free from desynchronization. Our performance and security analyses (i.e., theorem-based formal analysis, NS3-based experiment, and formal verification using the AVIPSA tool) show that our proposed protocol achieves stronger security goals and better efficiency in terms of computation, communication, and storage costs compared to existing SGKD schemes.

Vehicle-to-Everything (V2X) technology plays a pivotal role in enabling real-time traffic coordination and safety, warning, and decision support. Within V2X, the Basic Safety Message (BSM) serves as the core to transmit critical vehicle status, location, and intention information to provide a foundation for ensuring reliable traffic safety and coordination mechanisms. Data accuracy stands as a key to the effectiveness and reliability of the V2X system, in which the transmission of error data can potentially result in severe traffic accidents. During vehicular operation, sensors may generate error data owing to looseness or external conditions. However, immediate sensor replacement is often impractical or infeasible. Therefore, this paper introduces a collaborative scheme involving vehicles, Road Side Units (RSUs), and Data Center (DC) to jointly enhance the accuracy of vehicle-transmitted BSMs. Our scheme involves analyzing statistical features of vehicle driving information to detect error BSMs. Subsequently, these detected errors are corrected by leveraging historical data from the vehicle and its relative relationship with surrounding vehicles. In addition, we propose a time optimization method to reduce the average processing time of each data by RSUs. The extensive experimental results demonstrate that the proposed scheme can accurately detect error BSMs and effectively correct error BSMs. The entire scheme also meets the requisite computational latency requirements.

Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely,ANovel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.

As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to obtain global road information in practical scenarios since there will still be many vehicles on the road without onboard units (OBUs) in the near future. Specifically, although C-V2X vehicles have sensors that can perceive their surroundings and broadcast their perceived information to the C-V2X system, their line-of-sight (LoS) is limited and obscured by the environment, such as other vehicles and terrain. Besides, vehicles without OBUs cannot share their perceived information. These two problems cause extensive areas with unperceived information in the C-V2X system, and whether vehicles are in these areas is unknown. Thus, extending the perceivable range of the limited scenario for C-V2X applications that require global road information is necessary. To this end, this paper pioneers investigating the scenario inpainting task problem in C-V2X. To solve this challenging problem, we propose an effiCient trAfficScenario inpainTing (CAST) solution consisting of a generative architecture and knowledge distillation, simultaneously considering the inpainting precision and computation efficiency. Extensive experiments have been conducted to demonstrate the effectiveness of CAST in terms of Precise Inpaint Rate (PIR), Rough Inpaint Rate (RIR), Lane-Level Inpaint Rate (LLIR), and Inpaint Confidence Error (ICE), paving the way for novel solutions for the inpainting problem in more complex road scenarios.

Although some studies in recent years have focused on the coexistence of service and task dependencies in the collaborative optimization of service caching and task offloading in Vehicle Edge Computing, the challenges brought by dual dependencies have not been fully addressed. Therefore, this paper proposes a more comprehensive joint optimization method for service caching and task offloading under dual dependencies. First, this paper proposes a service criticality prediction method based on the Gated Graph Recurrent Network to perceive complex task dependencies and accurately capture the service requirements of critical task types. Based on this, a hierarchical active-passive hybrid caching strategy is designed, which aims to satisfy diverse service demands while reducing the additional overhead caused by remote service requests. Second, a global task priority computation method based on application heterogeneity has been developed to prevent cascading delays in task chains. Finally, this paper formulates a joint optimization problem for service caching and task offloading in a three-layer VEC system, models it as a Markov Decision Process, and applies a Proximal Policy Optimization-driven collaborative optimization algorithm named COHCTO. Simulation results show that COHCTO achieves multi-objective optimization across metrics such as delay, energy consumption, caching hit rate, and application success rate under conditions different from those of other algorithms.

The emergence of unmanned aerial vehicles (UAVs) ushers in a new era for mobile edge computing (MEC), significantly expanding its range of service and potential applications. Due to the limited storage capacity and energy budget of UAVs, it is crucial to determine a reasonable service caching and task offloading strategy. Service caching means that task-related programs and the associated databases are cached on edge servers. In this paper, we consider the time latency and energy consumption caused by frequent changes to the service caching, aiming to jointly optimize the computational offloading, resource allocation, and service caching in multi-UAV assisted MEC systems at different time scales. The objective of this optimization is to reduce the overall system delay while staying within the energy limitations of both the UAVs and ground devices. An improved service caching policy (SCP) is proposed, which is based on task popularity and utilizes the greedy dual size frequency (GDSF) algorithm. The SCP is combined with the twin delayed deep deterministic policy gradient (TD3) algorithm to propose an innovative dual timescale TD3 (DTTD3) algorithm. The numerical outcomes obtained from a substantial number of simulation experiments demonstrate that DTTD3 outperforms existing benchmark methods in terms of convergence and parameter optimization.

Edge-Cloud-End Collaborative (ECEC) computing emerges as a promising paradigm to support computationally intensive applications within Digital Twin (DT) networks, providing flexibility and real-time performance for heterogeneous task scheduling and resource allocation. However, effectively balancing key Service Level Agreement (SLA) parameters, such as response delay, availability, and throughput in dynamic network environments remains challenging, especially for complex, large-scale applications. Existing solutions typically lack SLA-oriented proactive resource reservation schemes. To address these limitations, we propose an SLA-driven hierarchical bidirectional closed-loop DT Network Architecture (SLA-DTNA), comprising four layers: real network, local DT, edge DT, and cloud DT. The proposed architecture systematically decomposes SLA requirements into measurable system parameters, aiming to minimize overall system cost. Specifically, a differentiated task management mechanism is designed at the local DT layer to ensure Quality of Service (QoS) for critical tasks. At the edge DT layer, we propose a heuristic-based lightweight scheduling algorithm leveraging DT capabilities for efficient task resource mapping and reduced scheduling complexity. At the cloud DT layer, we apply the Deep Deterministic Policy Gradient (DDPG) algorithm for adaptive resource reservation, dynamically adapting schemes based on task preferences and historical behavior patterns. Simulation and experimental results validate that the proposed SLA-DTNA enables fine-grained and intelligent resource allocation, enhances overall network performance, and effectively satisfies dynamic SLA requirements.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions.

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) technologies into WRSNs.

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.

Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients' medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112 120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10034017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.

This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between thehigh-tierand low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.

With the advent of 6G technology, satellite edge computing networks have emerged as a crucial component of next-generation communication systems. This paper investigates the problem of dependent task offloading in 6G satellite edge computing networks, taking into account the impact of satellite mobility and task dependencies. We formulate an optimization problem to jointly optimize offloading decisions, task waiting times, and computation resource allocation in a multi-satellite and multi-user device scenario. The objective is to minimize the weighted sum of waiting times and energy consumption. To address this problem, we propose a heuristic algorithm to determine the priority of tasks in the queue and develop an improved hybrid genetic grey wolf optimization (HGGWO) algorithm. The HGGWO algorithm effectively solves the optimization problem by leveraging the strengths of both genetic algorithms and grey wolf optimization. Extensive simulations are conducted to evaluate the performance of the proposed approach. The results demonstrate that our algorithm achieves superior performance in terms of total cost compared to four other benchmark schemes.

Cooking is one of the oldest and the most common human activities in everyone’s daily life. Instructional cooking videos have also become one of the most common data sources for multimodal visual understanding researches. Compared to other domains, multimodal cooking videos: 1. not only have significantly stronger cross-modal dependencies between the speech transcriptions and their semantically-aligned visual frames at static time stamps; 2. but also have significantly stronger cross-context dependencies among the sequential steps along the temporal dimension, resulting as an ideal domain for contextualized semantic understanding. We propose Cooking-CLIP, which introduces the concept of language-image pretraining (CLIP) from a general-purpose multimodal embedding problem into a customized recipe generation application. We also propose a context-aware pretraining approach, to facilitate a better CLIP customization to cooking-related applications. Our approach achieves higher text generation accuracies than a strong zero-shot baseline on two instructional cooking video data sets, CrossTask and YouCook2. We also achieve comparative accuracies against a fully-supervised approach, with only a narrow difference, in spite of our zero-shot setting.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context of mobile edge computing (MEC) has garnered significant interest within the sector. However, UAVs also face constraints in terms of resources like storage and computational power. Therefore, it is vital to develop effective UAV assistance solutions to provide long-term demands of in-network services. The dynamic scheduling and computation offloading of UAVs is the subject of this paper. Specifically, we propose a deep deterministic policy gradient algorithm based on a greedy strategy (DDPGG) to jointly optimize dynamic scheduling, device association, and task allocation of UAVs, with the goal of minimizing the weighted sum of total system energy consumption and time delay. The problem is formulated as a nonlinear programming problem involving mixed integers. The simulation results demonstrate that the DDPGG algorithm we have proposed exhibits a higher level of performance in comparison to its competitors.

Cellular vehicle-to-everything (C-V2X) can provide ubiquitous mobile computing and communication services for vehicles, acting as a key technology to realize future urban intelligent transportation systems (ITS). Due to the lack of long-term insight into complex and dynamic urban road states, the existing historical road information-based strategies for C-V2X applications are inadequate to satisfy their high-performance requirements. Fortunately, it is feasible to provide fine-grained future road states for C-V2X decision-making by predicting traffic states to address this issue. To this end, this article proposes a fine-grained joint traffic prediction method in the urban road network with high-spatial complexity (ROUTE). ROUTE uniquely forecasts both micro-level (individual vehicle states) and macro-level traffic, thus supporting the diverse requirements of C-V2X applications. ROUTE is comprised of three key parts, including a vehicle coordinate transformation model, a spatial interaction-based turning model, and a micro-traffic prediction model. First, the complex spatial topology of the urban regional road network is normalized in ROUTE using the coordinate transformation model. Second, the turning model calculates the next road that the vehicle chooses after leaving the current one. Third, a transformer and generative adversarial network-based model (FORMERGAN) predicts future micro-traffic states. Extensive experimental results demonstrate that ROUTE surpasses its competitors in accurately predicting fine-grained long-term micro-traffic and macro-traffic states.

Supporting mobile edge computing (MEC) in satellite-terrestrial networks (STNs) provides essential offloading services for devices for the Internet of Things (IoT) devices in remote areas. However, when terrestrial demands for computing resources are high, the MEC servers on visible LEO satellites may suffer from insufficient capacity, while those on more distant LEO satellites remain underutilized. To address this issue, this article investigates cooperative task offloading across multiple LEO satellites within an MEC-based STN. We propose a Quality-of-Service (QoS)-aware offloading decision and resource allocation scheme supported by a software-defined network (SDN) for a STN architecture. This architecture integrates the LEO Walker constellation with satellite ground stations (SGSs), with the aim of providing edge computing services to IoT devices in remote areas. To meet the task’s QoS requirements, the tasks can be offloaded to either SGS or LEO satellites within the constellation. To address the challenges of a vast state space and complex action space within the system, we introduce the QOS-aware multihop task offloading in satellite-terrestrial edge networks (OUTSIDE) algorithm, which combines the global search capabilities of genetic algorithms with the local refinement strengths of the Lagrangian multiplier method to minimize the total task computation latency while satisfying QoS demands. Finally, comparative analysis and simulation experiments were conducted. These demonstrate that the OUTSIDE algorithm outperforms other approaches in terms of efficiency and effectiveness.

With the rapid expansion of Internet of Everything (IoE) devices and the increasing demand for high-speed data and reliable communication services, particularly within 6G cellular networks (CNs), the design of efficient and robust CNs has become a critical research area. Consequently, enabling massive connections, optimizing network resource utilization, and achieving cost-effective network operation pose significant challenges. To this end, integrated space-ground cellular networks based on control- and user-plane separation (ISGCN-CUPS) architecture has been proposed as a promising solution. Furthermore, it becomes an integral aspect of the broader paradigm of integrated space-air-ground CNs (ISAGCNs). However, scalability poses an issue when increasing the number of connected cellular users, especially when conventional orthogonal multiple access (OMA) is utilized. To address this challenge, this paper introduces the non-orthogonal multiple access (NOMA)-enabled ISGCN-CUPS architecture. Subsequently, we provide an analytical model to analyze the scenarios of proposed architecture. Utilizing stochastic geometry, we derive closed-forms for coverage probabilities over control and data channels, by considering the propagation channel models for control and data channels, both with and without interference. Furthermore, total area spectral and energy efficiencies are computed. The proposed architecture demonstrates significant enhancements in terms of the key evaluation metrics compared to conventional and OMA-enabled ISGCN-CUPS architectures.

Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task offloading. Subsequently, we propose a real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

With the increasing communications between the In-Vehicle Networks (IVNs) and external networks, security has become a stringent problem. In addition, the controller area network bus in IVN lacks security mechanisms by design, which is vulnerable to various attacks. Thus, it is important to detect IVN anomalies for complete vehicular security. However, current studies are constrained by either requiring labeled data or failing to accurately detect message-level anomalies without labeled data. In addition, the concept drift of existing methods has become a challenge over time. To address these problems, this paper proposes an IVN anomaly detection method based on Self-supervised Learning (IVNSL), which is capable of detecting message-level anomalies without labels. The essential idea of IVNSL is to make the message prediction model learn the distribution of normal messages in sequences using message sequences with noise. Furthermore, to accurately detect anomalies, a Message Prediction Model based on Hierarchical transformers (MPMHit) is proposed, which captures the spatial features of the message and the dependencies between messages. Meanwhile, to solve the concept drift over time, this paper proposes an online update mechanism for MPMHit based on vehicle-cloud collaboration. We conduct an extensive experimental evaluation on the car hacking dataset, resulting to an F1-score average and average false positive rates of IVNSL being 2.282% higher and 1.595% lower than the best baseline method. The average detection speed of each message is as fast as 0.1075 ms.

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters the progress of ITS through enabling direct vehicle communication and interaction with infrastructure. However, the penetration rate of V2X vehicles is advancing gradually. Consequently, there will be mixed scenarios on the road, involving both on-board units (OBUs)-equipped and non-equipped vehicles. This results in disparities in communication capabilities, highlighting the need to ensure the efficient and safe operation of vehicles in such mixed scenarios. This paper addresses this challenge by presenting a feasibility analysis and prediction method for lane-changing overtaking maneuvers in mixed scenarios, specifically for vehicles equipped with OBUs. This method assists vehicles in completing overtaking maneuvers by offering a non-binary lane-changing overtaking feasibility index along with corresponding speed guidance. First, vehicle sensors are used to sense the state of surrounding vehicles, addressing any missing sensor data due to occlusions. Moreover, the future driving behavior of the vehicle is taken into account to more accurately predict the future state of the vehicle. Then, a deep reinforcement learning algorithm is deployed to process the hybrid action space to train a lane-changing overtaking model, which also takes into account the influence of the flow of each lane in front of the vehicle, and finally predicts the feasibility of the vehicle performing lane-changing overtaking. Experimental results demonstrate that our method can accurately predict the vehicle’s future state and effectively assist the vehicle in completing lane-changing overtaking maneuvers. This research provides strong support for the integration of ITS and V2X technologies.

The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication.SipDeep, proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments,SipDeepdemonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.

Vehicular crowd sensing (VCS), emerging as a new paradigm within mobile crowd sensing, leverages vehicles as the participator, which can obtain broader sensing coverage and higher sensing flexibility. Previous works ignored the strong impact of environmental factors on workers' travel costs, as well as improper gains from speculative behavior (i.e. workers detour or delay to get more compensation), resulting in unfair income of workers. Moreover, these works focused solely on sensing tasks within specific domains, lacking generalization ability. Therefore, our work is dedicated to providing a fair and universal VCS platform, which is called Environment-cost-based Heterogeneous Task Allocation (EHTA) framework. Our work differs from previous works in the following aspects: 1) We introduce the Environment Cost (EC) based on the investigation of traffic conditions to accurately quantify workers' efforts, and propose a straightforward yet efficacious detection methods to identify speculative behavior of malicious workers, both of which could guarantee the fairness in workers' income. 2) We design a spatial-temporal fair incentive mechanism based on monetary reward to ensure the fair execution of tasks in both space and time dimensions. 3) We summarize the characteristics of three kinds of sensing tasks and propose a universal task allocation algorithm to assign multiple types of tasks simultaneously. The effectiveness of our framework was validated by simulations, which are conducted on a data set comprising 13,000 taxi trajectories from Shanghai in April 2015. We compared our framework against four baseline algorithms, and the results shows that EHTA framework outperforms in terms of task expenditure, task utility and fairness.

Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving connectivity and data sharing among vehicles and road infrastructure. In a Mixed Connected and Connectionless Vehicles (MCCV) scenario consisting of connected vehicles equipped with On-Board Units (OBUs) and non-connected vehicles lacking OBUs, communication disparities create challenges in critical lane-changing overtaking decisions. These discrepancies hinder the adaptation of fully connected scenarios to dynamic interactions among these different types of vehicles. Considering the diversity in decision-making ways and capabilities of non-connected vehicles in MCCV scenarios, ensuring the coordinated execution of safe and efficient lane-changing overtaking maneuvers by multiple connected vehicles is crucial for enhancing traffic efficiency. Therefore, we propose a collaborative strategy to facilitate safer and more efficient lane-changing overtaking maneuvers for connected vehicles in the MCCV scenario. First, we design a multi-criteria priority detection, and a dynamic event-triggered mechanism based on confidence intervals to foster efficient collaboration among connected vehicles, optimizing decision-making and reducing conflicts. Second, to accommodate diverse driving styles of autonomous and human-driven vehicles, we introduce an Improved Dynamic Precise Fuzzy C-Means (IDP-FCM) algorithm to dynamically identify and adapt to different driving styles, thereby improving safety. Finally, tackling the challenge of multiple connected vehicles performing lane-changing overtaking involving hybrid action space, our proposed Multi-agent Contrastive Parameterized Dueling Deep Q-Network (MCPDDQN) algorithm incorporates contrastive learning to improve strategy stability in complex driving scenarios. Experimental results demonstrate the effectiveness of our strategy in improving road safety and traffic efficiency of the MCCV scenario.

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, AdaptiveSwarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRedictionInVEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveDgenetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because vehicles with time-varying trajectories need to process massive data with high complexity and diversity. In this paper, a VEC-based computation offloading model is developed with consideration of data dependency of tasks. The minimization of the average response time and average energy consumption of the system is defined as a combinatorial optimization problem. To solve this problem, we propose aMobility-aware dependent taskoffloading (MESON) Scheme for urban VEC and develop a DRL-based algorithm to train the offloading strategy. To improve the training efficiency, a vehicle mobility detection algorithm is further designed to detect the communication time between vehicles and RSUs. In this way, MESON can avoid unreasonable decisions by lowering the size of the action space. Moreover, to improve the system stability and the offloading successful rate, we design a task priority determination scheme to prioritize the tasks in the waiting queue. The experimental results show that MESON is superior compared to other task offloading schemes in terms of the average response time, average system energy consumption, and offloading successful rate.

Mobile radio frequency identification (RFID) systems typically experience the continual movement of many tags rapidly going in and out of the interrogating range of readers. Readers that are deployed to maintain a current, real-time list of tags, which are present in the interrogating zone at any moment, must repeatedly execute a series of reading cycles. Each of these reading cycles provides the readers very limited time to identify unknown tags (those newly entering into the reader’s range), and, at the same time, to detect missing tags (those just leaving the reader’s range). In this paper, we study the continuous tag recognition problem, which is critical for mobile RFID systems. First, we obtain a lower bound on communication time for solving this problem. We then design a near-OPTimal protocoL, called OPT-L, and prove that its communication time is approximately equal to the lower bound. Finally, we present extensive simulation and experimental results that demonstrate OPT-L’s superior performance over other existing protocols.

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate theimportanceof nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.

Vehicle trajectory prediction is a promising technology for improving the performance of Cellular Vehicle-to-Everything (C-V2X) applications by providing future road states. Various vehicle trajectory prediction methods have been proposed to increase the accuracy of the predicted trajectory. Although the existing vehicle trajectory prediction methods can accurately predict the future trajectory under the assumption that data comply with the Independent and Identically Distributed (IID), their performance is seriously degraded in practical implementation due to the ubiquitous distribution shifts in vehicle trajectory data. To improve the universality of the vehicle trajectory prediction method, generalizing the method to an environment that never appeared in the training data, namely, the Domain Generalization (DG) task, should be considered. Thus, we propose a plug-and-play inFORmaTive caUsality-based vehicle trajectory predictioN architecturE (FORTUNE) to improve the DG capability of vehicle trajectory prediction methods. First, a novel structural causal model (SCM) of vehicle trajectory prediction is established to simulate the causality of the data-generating process. Second, we utilize the principle of mutual information to learn the invariant representation of the SCM. Third, an invariant knowledge-transferring module is proposed to increase learning ability without destroying the structure of the original model. The results from simulation experiments demonstrate that the proposed scheme can significantly improve the DG capability of vehicle trajectory prediction methods.

Recently, sparse arrays have received considerable attention owing to their capability of achieving increased degrees of freedom (DoFs) by exploiting the virtual sensors resulting from their difference or sum-difference coarrays. Mutual coupling is another factor that attracts interest to these kinds of arrays. In this paper, both the fundamental criteria of high DoFs and reduced mutual coupling are considered in the design of the proposed array configuration for the direction of arrival (DoA) estimation of non-circular signals. Simulation results are provided to verify the robustness of the proposed array against heavy mutual coupling.

The research on backscatter multiple access schemes has been an interesting topic. Existing schemes in the area of backscatter multiple access are not designed for specific applications, and leave out the contemplation of practical application requirements. In this paper, therefore, we present Ortho-CodeA, a backscatter multiple access scheme that targets indoor Internet of Things (IoT) applications (e.g., the smart home) and considers the practical application requirements. To implement Ortho-CodeA, we address several key challenges including imperfect time synchronization, high hardware complexity at receiver, and synchronizing multiple tags information in a low-power manner. We theoretically analyze the feasibility of our scheme and evaluate the performance of Ortho-CodeA through extensive simulations. The results show that Ortho-CodeA supports concurrent transmissions of up to 7 tags and achieves BER of 0 when SNR is greater or equal to 0 dB.

The Metaverse is an emerging concept that aims to integrate the physical and virtual worlds, creating a shared 3D virtual world where users can interact and immerse in new experiences. With the rise of Metaverse-driven Cyber-Physical-Social Systems (CPSSs), integrating drones as a critical technology in the Metaverse has become increasingly important. CPSSs have become proliferating and integral to our daily lives. This paper proposes a conceptual framework for Metaverse-driven drone edge intelligence, which integrates drone-enabled sensing, communication, and computation to enable real-time decision-making in CPSSs. We present a detailed analysis of the challenges and opportunities for integrating drones in the Metaverse and discuss the potential impact of our framework on various application domains. Our work contributes to advancing the Metaverse and CPSSs by providing a novel approach for empowering real-time decision-making and enabling new user experiences through integrating drones and the Metaverse. The proposed framework has the potential to revolutionize the way we approach data-driven decision-making in various industries and applications, including precision agriculture, transportation, emergency response, smart cities, healthcare, manufacturing, and energy.

Recently, innovations in the Internet of Medical Things (IoMT), information and communication technologies, and machine learning (ML) have enabled smart healthcare. Pooling medical data into a centralized storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated learning (FL) overcomes the prior problems with a centralized aggregator server and a shared global model. However, there are two technical challenges: 1) FL members need to be motivated to contribute their time and effort and 2) the centralized FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralized fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasize three main research streams based on a systematic analysis of blockchain-empowered: 1) IoMT; 2) electronic health records (EHRs) and electronic medical records (EMRs) management; and 3) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications.

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This article proposes a hierarchical protocol called territory-bound data delivery (TBDD) that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption (EC) throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last, we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, EC, delay, network lifetime, and success ratio.

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabilities. However, the highly dynamic network topology, strict low-delay constraints, and massive data of tasks of VEC pose significant challenges for implementing efficient offloading. Digital Twin-based VEC is emerging as a promising solution that enables real-time monitoring of the state of the VEC network through mapping and interaction between the physical and virtual worlds, thus assisting in making sound offload decisions in the physical world. Thus, this paper proposes an intelligent partial offloading scheme, namely, Digital Twin-Assisted Intelligent Partial Offloading (IGNITE). First, to find the optimal offloading space in advance, we combine the improved clustering algorithm with the Digital Twin (DT) technique, in which unreasonable decisions can be avoided by reducing the size of the decision space. Second, to reduce the overall cost of the system, Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy, allowing for automatic optimization of computational delay and vehicle service price. To improve the efficiency of cooperation between digital and physical spaces, a feedback mechanism is established. It can adjust the parameters of the clustering algorithm based on the final offloading results in this clustering. To the best of our knowledge, this is the first study on DT-assisted vehicle offloading that proposes a feedback mechanism, forming a complete closed loop as prediction-offloading-feedback. Extensive experiments demonstrate that IGNITE has significant advantages in terms of total system computational cost, total computational delay, and offloading success rate compared with its counterparts.

Abstract The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

Greedy routing efficiently achieves routing solutions for vehicular networks due to its simplicity and reliability. However, the existing greedy routing algorithms have mainly considered simple routing metrics only, e.g., distance based on the local view of an individual vehicle. This consideration is insufficient for analysing dynamic and complicated vehicular communication scenarios which inevitably degrades the overall routing performance. Software-Defined Vehicular Network (SDVN) and Graph Convolutional Network (GCN) can overcome these limitations. Thus, this paper presents a novel GCN-based greedy routing algorithm (NGGRA) in the hybrid SDVN. The SDVN control plane trains the GCN decision model based on the globally collected data. Vehicles with transmission requirements can adopt this model for inferring and making the routing decisions. The proposed node-importance-based graph convolutional network (NiGCN) model analyses multiple correlated metrics to accurately evaluate the dynamic vehicular network is available at:https://github.com/a824899245/NiGCN. Meanwhile, the SDVN architecture offers a global view for model training and routing computation. Extensive simulation results demonstrate that NiGCN outperforms popular GCN models in training efficiency and accuracy. In addition, NGGRA can improve the packet delivery ratio and substantially reduce delay compared with its counterparts.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Many opportunistic routing (OR) schemes treat network nodes equally, neglecting the fact that the nodes close to the sink undertake more duties than the rest of the network nodes. Therefore, the nodes located at different positions should play different roles during the routing process. Moreover, considering various Quality-of-Service (QoS) requirements, the routing decision in OR is affected by multiple network attributes. The majority of these OR schemes fail to contemplate multiple network attributes while making routing decisions. To address the aforesaid issues, this paper presents a novel protocol that runs in three steps. First, each node defines aRouting Zone (RZ)to route packets toward the sink. Second, the nodes within RZ are prioritized based on the competency value obtained through a novel model that employs Modified Analytic Hierarchy Process (MAHP) and Fuzzy Logic techniques. Finally, one of the forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio, and network lifetime.

Software-Defined Vehicular Network (SDVN) is a networking architecture that can provide centralized control for vehicular networks. However, the design for routing policies in SDVNs is generally influenced by several limitations, such as frequent topological changes, complex service requests, and long model training time. Intelligent Digital Twin-based Software-Defined Vehicular Networks (IDT-SDVN) can overcome these weaknesses and maximize the advantages of the conventional SDVN architecture by enabling the controller to construct virtual network spaces and provide virtual instances of corresponding physical objects within the Digital Twin (DT). In this paper, we propose a junction-based hierarchical routing scheme in IDT-SDVN, namely, intelligent digital twin hierarchical (ELITE) routing. The proposed scheme is conducted in four phases: policy training and generation in the virtual network, and deployment and relay selection in physical networks. First, the policy learning phase employs several parallel agents in DT networks and derives multiple single-target policies. Second, the generation phase combines the learned policies and generates new policies based on complex communication requirements. Third, the deployment phase selects the most suitable generated policy according to the real-time network status and message types. A road path is calculated by the controller based on the selected policy and then sent to the requester vehicle. Finally, the relay selection phase is utilized to determine relay vehicles in a hop-by-hop process along the selected path. Simulation results demonstrate that ELITE achieves substantial improvements in terms of packet delivery ratio, end-to-end delay, and communication overhead compared with its counterparts.

Topic modelling (TM) has shown significant progress in boosting the effectiveness of image captioning in the last few years. Although important improvements have been shown in previous topic-guided image captioning models, some challenges remain unsolved, such as the independence of the topic predictors and the sentence generators, resulting in ineffective exploitation of semantic information. Also, all the predicted topics or the top-one topic are used throughout the whole captioning task without considering the current time step's linguistic context, which deviates the captioning network to focus on inaccurate image objects. To tackle these challenges, we propose a novel image captioning method consisting of four modules: enhanced topic predictor (ETP), retrieval-based topics re-weighting module (RTR), subsequent topic predictor (STP), and caption generation module. The prediction and generation modules are trained in an end-to-end manner to promote the efficient use of topics by predicting suitable topics at each time step. ETP predicts the topics using the image features, and is enhanced with topic embedding (TE). The RTR is only applied in the testing stage for re-weighting the topics predicted by ETP. In each time step, the STP automatically predicts concise topics subsets to alleviate the diversity of the image topics. Compared with the existing topic-based models, our model can automatically generate more accurate and diverse captions, boosting the explainability of how the topics influence the generated word in each time step. Extensive experiments on the MS-COCO and Flickr30K benchmark datasets show that our method enhances the overall image captioning's performance and the topic prediction task, and outperforms many recent image captioning approaches in terms of the evaluation metrics.

Image captioning is a promising task that attracted researchers in the last few years. Existing image captioning models are primarily trained to generate one caption per image. However, an image may contain rich contents, and one caption cannot express its full details. A better solution is to describe an image with multiple captions, with each caption focusing on a specific aspect of the image. In this regard, we introduce a new number-based image captioning model that describes an image with multiple sentences. An image is annotated with multiple ground-truth captions; thus, we assign an external number to each caption to distinguish its order. Given an image-number pair as input, we could achieve different captions for the same image under different numbers. First, a number is attached to the image features to form an image-number vector (INV). Then, this vector and the corresponding caption are embedded using the order-embedding approach. Afterward, the INV’s embedding is fed to a language model to generate the caption. To show the efficiency of the numbers incorporation strategy, we conduct extensive experiments using MS-COCO, Flickr30K, and Flickr8K datasets. The proposed model attains 24.1 in METEOR on MS-COCO. The achieved results demonstrate that our method is competitive with a range of state-of-the-art models and validate its ability to produce different descriptions under different given numbers.

Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a promising technology to provide computational services to ground terminal devices (TDs) in remote areas or for emergency incidents with its flexibility and mobility. This paper aims to maximize the UAV’s energy efficiency while considering the fairness of offloading. We formulate this optimization problem by jointly considering the UAV flight time, the UAV 3D trajectory, the TD binary offloading decisions, and the time allocated to TDs, which is a mixed-integer nonlinear programming problem. The problem is transformed into two sub-problems and we propose a PDDQNLP (parametrized dueling deep Q-network and linear programming) algorithm based on the combination of deep reinforcement learning (DRL) and linear programming (LP) to address them. For the first sub-problem, a DRL-based algorithm is used to optimize the TD offloading decisions, the UAV trajectory, and the UAV flight time. The action space is hybrid that contains discrete actions (e.g., binary offloading) and continuous actions (e.g., UAV flight time). Therefore, we parameterize the action space and propose the PDDQN algorithm, which combines the DDPG algorithm for handling the continuous action space and the Dueling DQN algorithm for handling the discrete action space. According to the solution obtained from the first sub-problem, the LP is used to adjust the time allocated to TDs in the second sub-problem. The numerical results show that the PDDQNLP algorithm outperforms its counterparts.

Smart Parking has gained so much popularity in recent years due to the increasing number of vehicles in big cities, resulting in traffic congestion in urban areas. Not only on the streets but also in places such as educational institutions, hospitals, commercial activities, special events, and entertainment uses. Finding a free parking lot in these places has evolved difficulty for the drivers. To solve such a problem, governments and researchers tried to find alternative solutions to overcome or mitigate the traffic congestion. Many solutions have been proposed, such as increasing the parking capacities, which takes much time or makes it hard to find a square area in crowded places. Most existing studies, do not consider the cost of deployment, energy, and time-to-market consideration which makes the available systems need further investigation. In this paper, we propose an intelligent parking system prototype that can be useful for the drivers to have a prior knowledge about the available parking lots in the area of interest. Our proposed system involves deploying mmWave Radar sensor nodes in each parking lot to detect the availability of parking vehicle through transmitting radio pulses periodically. The detected information can be sent to the gateway through multi-hop for further statistics and reports. We also give an intensive analysis and study about the challenges and consideration on the mmWave radar design aiming to improve the detection accuracy and avoid false-detection that occurs from objects near to the sensor. To ensure continuous operation and extend sensor life-time, we propose Bluetooth Low Energy BLE-enabled relay-feature as the communication protocol between the nodes.

Vehicular Edge Computing (VEC) is a computing paradigm that brings Mobile Edge Computing (MEC) to the road and vehicular scenarios by providing low-latency and high-efficiency computation services. One key technology of VEC is task offloading, which allows vehicles to send computation tasks to surrounding Roadside Units (RSUs) for execution, thereby reducing service delay. However, the existing task offloading schemes face the important challenges because the vehicles with time-varying trajectories and limited computing resources need to process massive data with high complexity and diversity. In this paper, we propose a Cooperative Task Qffloading Scheme (CTOS) based on Cybertwin-assisted VEC. Specially, a novel Cybertwin-assisted VEC network architecture is established by applying the combination of the Digital-Twins (DT) and the Generative Adversarial Network (GAN). With the powerful prediction capability of GAN, the data of DT is advanced with the physical entity, which is an effective assistant for task offloading. Then, we leverage the distributed Deep Reinforcement Learning (DRL) to make offloading decisions, which consider the limited resources of RSUs and the cooperation of vehicles. The simulation results demonstrate that the proposed scheme can achieve excellent performance in terms of system stability and efficiency.

In this paper, a new type of coprime-array-based structure, named AtCADiS, is proposed to achieve increased degrees of freedom (DoFs) and reduced mutual coupling. The closed-form expressions for the sensor positions and the number of uniform DoFs (uDoFs) of AtCADiS are provided. Specifically, AtCADiS is constructed via two steps. First, we shift the leftmost sensor of tailored coprime array with displaced subarrays (tCADiS) to the right by N. Second, we increase the number of sensors of tCADiS by $\left\lfloor {\frac{M}{2}} \right\rfloor $ that are appropriately placed to connect the positive and negative lags of difference coarray of tCADiS and further improved its uDoFs remarkably. Finally, simulations show that AtCADiS can achieve higher number of uDoFs than the existing coprime-array-based structures by using the same number of physical sensors, which leads to stronger resolution capability and higher direction of arrival (DoA) estimation accuracy. In the presence of mutual coupling, AtCADiS can achieve comparable mutual coupling leakage compared with the existing array structures.

We have made research achievements worth sharing on the complicated topic of text-to-image synthesis. Our analysis of popular articles shows that they often use stacked structures to construct and generate confrontation network models and usually introduce multiple sets of generators and discriminator pairs. The entanglement between different generators affects the quality of the final synthesized image. Some researchers have proposed a single-stage network model to avoid traps between multiple generators, But it lacks the use of unstructured natural language information with different granularity. To correct this serious defect, we propose a multi-granularity feature network MGF-GAN, which plays the role of text information with different granularity based on the advantages of the single-stage network. Specifically, we input the three granularity features of the text, including sentences, aspect words, and single words of text, into different stages of the model through spatial attention and channel attention mechanisms to gradually refine the synthetic image from global and local perspectives. In addition, we reconstruct the loss function based on the contrast concept to stabilize the training and ensure that the visual meaning between the synthesized image and the natural language is consistent. We conducted validity experiments on CUB bird and COCO. The significant effect is sufficient to prove the effectiveness and advancement of our MGF-GAN.

Video Question Answering (VideoQA) is a challenging multimodal task that requires the ability to recognize visual elements and reason relations in spatial and temporal dimensions according to the given video and question. Most existing GNN-based methods model the visual elements in a video as graph structures and reason relations between them. Despite the remarkable results of their work, they neglected that the question also has graph structure dependencies, which can be used to reason about relations between the video and the question. In this work, we propose a multimodal graph reasoning and fusion network that builds three graph neural networks for appearance, motion, and text sequences, respectively, and hierarchically reasons and fuses nodes from different modalities. Our proposed method achieves superior performance to several state-of-the-art methods on three benchmark datasets.

Software-defined vehicular network (SDVN) is a promising networking paradigm that can provide intelligent information exchanges by separating network management and data transmission. Although the transmission quality of vehicles can be greatly improved by deploying softwarized networking schemes, critical networking issues such as the timeliness of data packets remain due to the dynamic nature of vehicular networks. It is vital to design efficient networking schemes by deeply considering the characteristics of the network, transportation system, and users, to improve overall network performance. To this end, this paper proposes asocial computing inspired predictiverouting scheme (SPIDER) for SDVNs that has a comprehensive consideration to enable low-latency reliable data exchange under dynamic vehicular networks. As for the link lifetime grounded on the vehicular historical data, we introduce the context feature mining and one-shot prediction method to predict vehicle movements with considering the energy saving. We also involve social computing techniques to find the relay nodes with good data spreading abilities. The extensive experiments prove our proposed scheme outperforms four existing schemes.

Data transmission in Vehicular Ad Hoc Networks (VANETs) often suffers from routing interruptions due to the unstable communication links between vehicles. Over the past decades, many traffic-aware routing protocols have been proposed to alleviate routing interruptions by sensing traffic conditions. However, in most traffic-aware routing protocols, vehicles must transmit a large number of control packets to accumulate traffic information, which may degrade network performance due to the resulting intense competition over the wireless medium. Instead of using control packets, we propose to leverage the beacon mechanism that has been widely used in VANETs to realize traffic awareness. Vehicles broadcast beacons to exchange necessary information with their neighbors periodically. We can leverage this information exchange process among vehicles to replace control packets. To realize this idea, first, a mathematical analysis is provided to demonstrate its feasibility. Then, we propose a concrete protocol to address the technical challenges of using beacons. Extensive simulation results show that our protocol performs better than the state-of-the-art counterparts regarding packet delivery ratio, average delivery time, and network overhead.

Temporal information is critical for routing computation in the vehicular network. It plays a vital role in the vehicular network. Till now, most existing routing schemes in vehicular networks consider the networks as a sequence of static graphs. We need to find an appropriate method to process temporal information into routing computation. Thus, in this paper, we propose a routing algorithm based on the Hidden Markov Model (HMM) and temporal graph, namely, Prediction-Based Temporal Graph Routing Algorithm (PT-GROUT). This new algorithm considers the vehicular network as a temporal graph, in which each data transmission as an edge has its specific temporal information. To better capture the temporal information, we select Software-Defined Vehicular Network (SDVN) as our network architecture, which is a preferred architecture for processing the temporal graph regarding the vehicular network since all vehicle statuses can be easily managed. To compute the future routing path accurately and efficiently, the future temporal graph is predicted by applying HMM, in which we model the current vehicular network with dynamic programming and greedy strategies. With the temporal information and reasonable setting of HMM, PT-GROUT can better evaluate the vehicular network and discover the evolution of the internal structure of the network. The optimal routing path can be achieved more efficiently. The simulation results demonstrate that PT-GROUT can substantially improve the computation efficiency and reduce packet loss and delivery delay compared with its counterparts.

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a multi-criteria decision making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the analytic hierarchy process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink.

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

Abstract Wireless sensor networks (WSNs) are critically resource‐constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non‐transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.

This work is devoted to solving the problem of multicriteria multihop routing in vehicular ad hoc networks (VANETs), aiming at three goals: 1) increasing the end-to-end delivery ratio; 2) reducing the end-to-end latency; and 3) minimizing the network overhead. To this end and beyond the state of the art, heuristic routing for vehicular networks (HERO), which is a distributed routing protocol for urban environments, encapsulating two main components, is proposed. The first component, road-segment selection, aims to prioritize the road segments based on a heuristic function that contains two probability distributions, namely, shortest distance distribution (SDD) and connectivity distribution (CD). The mass function of SDD is the product of three quantities: 1) the perpendicular distance; 2) the dot-production angle; and 3) the segment length. On the other hand, the mass function of CD considers two quantities: 1) the density of vehicles and 2) the interdistance of vehicles on the road segment. The second component, vehicle selection, aims to prioritize the vehicles on the road segment based on four quantities: 1) the relative speed; 2) the movement direction; 3) the available buffer size; and 4) signal fading. The simulation results showed that HERO achieved a promising performance in terms of delivery success ratio, delivery delay, and communication overhead.

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by the processing and propagation of the routing request and response. Thus, to predict the high-precision trajectory beyond the state of the art, we propose a generative adversarial network (GAN)-based vehicle trajectory prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components: 1) vehicle coordinate transformation for data set preparation; 2) neural network prediction model trained by GAN; and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver’s psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the average accuracy, mean absolute error, and root-mean-squared error.

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi-Sugeno-Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes-road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

The safety driving-related content demands of vehicle users increase rapidly, especially with the development of autonomous driving. It is significantly necessary to obtain the safety-related transportation information of an area when vehicles are drove there, whether or not they are controlled by human being. However, vehicular content caching can bring issues in distributed-fashion, such as high response delay and low content response ratio because of the poor traffic condition and the obstructions of buildings. As a consequence, we adopt UAVs (Unmanned Aerial Vehicles) to assist the driving safety-related content caching for vehicles. Besides, since the power energy and the caching storage of UAVs are limited, it is needed to design an optimal caching scheme to guarantee the driving safety-related content demands of vehicle users as well as reduce the energy consumption of UAVs. In this article, we propose a novel deep Q-learning based air-assisted vehicular caching scheme to respond to the driving safety-related content requests of vehicle users. First, a three-layered content response architecture is introduced, where an airship is leveraged to take charge of the scheduling of UAVs to improve the content response. Then, a multi-objective mathematical model is built to describe the specific problem of the proposed scheme. Finally, deep Q-learning is applied to solve the multi-objective problem by learning from the history content requests of vehicle users. Extensive experiments have been conducted which show the proposed scheme outperforms its counterparts in terms of content hit ratio, response delay, being scheduling probability and packet buffering time.

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Opportunistic routing (OR) is gaining popularity in low-duty wireless sensor network (WSN), so the need for efficient and reliable data transmission is becoming more essential. Reliable transmission is only feasible if the routing protocols are secure and efficient. Due to high energy consumption, current cryptographic schemes for WSN are not suitable. Trust-based OR will ensure security and reliability with fewer resources and minimum energy consumption. OR selects the set of potential candidates for each sensor node using a prioritized metric by load balancing among the nodes. This paper introduces a trust-based load-balanced OR for duty-cycled wireless sensor networks. The candidates are prioritized on the basis of a trusted OR metric that is divided into two parts. First, the OR metric is based on the average of four probability distributions: the distance from node to sink distribution, the expected number of hops distribution, the node degree distribution, and the residual energy distribution. Second, the trust metric is based on the average of two probability distributions: the direct trust distribution and the recommended trust distribution. Finally, the trusted OR metric is calculated by multiplying the average of two metrics distributions in order to direct more traffic through the higher priority nodes. The simulation results show that our proposed protocol provides a significant improvement in the performance of the network compared to the benchmarks in terms of energy consumption, end to end delay, throughput, and packet delivery ratio.

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the routing flexibility. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without interrupting the network services. Through extensive performance evaluation studies and simulations, the results demonstrate that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade.

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting-time and the duplicate packets. To this end and beyond the state-of-the-art, this work proposes an SDN-based architecture, namely MINI-SDN, that separates the control and data planes. Moreover, based on MINI-SDN, we propose MINI-FLOW, a communication protocol that orchestrates the computation of flows and data routing between the two planes. MINI-FLOW supports uplink, downlink and intra-link flows. Uplink flows are computed based on a heuristic function that combines four values, the hops to the sink, the Received Signal Strength (RSS), the direction towards the sink, and the remaining energy. As for the downlink flows, two heuristic algorithms are proposed, Optimized Reverse Downlink (ORD) and Location-based Downlink(LD). ORD employs the reverse direction of the uplink while LD instantiates the flows based on a heuristic function that combines three values, the distance to the end node, the remaining energy and RSS value. Intra-link flows employ a combination of uplink/downlink flows. The experimental results show that the proposed architecture and communication protocol perform and scale well with both network size and density, considering the joint problem of routing and load balancing.

This article modeled the data routing problem in Wireless Sensor Networks as an in-zone random process. The data packets are randomly routed from the source to the sink within the defined RoutingZone via any-path. The proposed “Zone Probabilistic Routing (ZPR)” is a distributed probabilistic and randomized anycast routing protocol. In ZPR, the forwarding probability distribution is defined by multiplying the Four Probability Distributions (4PD) namely: direction, transmission distance, perpendicular distance, and residual energy. In order to meet different performance requirements for different applications, these probability distributions are completely controllable via a set of exponential control-parameters (direction control, transmission distance control, perpendicular distance control, and residual energy control). This set of parameters is user-oriented and can be modified prior to nodes deployment to achieve different performances. Through extensive simulations and experimental results, the optimal values for these exponential control-parameters have been obtained to meet different performance requirements in terms of energy consumption, energy balancing, network lifetime, and delay. Furthermore, through an extensive performance evaluation study and simulation of large-scale scenarios, the results showed that our proposed ZPR protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, and data routing efficiency.

Opportunistic Routing (OR) is adapted to improve the performance of low Duty-cycled Wireless Sensor Networks by exploiting its broadcast nature. In contrast to traditional routing, where packets are transmitted along pre-determined paths, OR uses a prioritization metric to select a set of candidates as potential forwarders. This solves the sender's waiting time problem. However, too many candidates may simultaneously wake-up, generating more duplicate packets, occupying the restricted resources and hinder the packet delivery performance. Consciously, to restrict the number of candidates and to counterbalance between the waiting time problem and the duplicate packets problem, this paper proposed a new protocol that combines two main parts. First, each node defines a Candidates Zone (CZ) by a regular geometric shape of four corners. The packets generated by the node will be routed via any path within the CZ. Expressly, the nodes within the CZ are allowed to be selected as candidates. The size of CZ is controlled by the network density. Second, the candidates within the CZ are prioritized based on the OR metric, which is defined as the multiplication of four-distributions: direction distribution, transmission-distance distribution, perpendicular-distance distribution, and residual energy distribution. Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that our protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.

Budget allocation for multi-channel in advertising deals with distributing different sub-budgets to different channels under a fixed budget periodically. However, the issue of sequential decision making, with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits, has rarely been addressed. Besides, there is a lack of explicit linking between the advertising actions taken in one channel and the responses obtained in another. What's more, the budget constraint restricts the feasible space of various optimal strategies. In this paper, we resolved these challenges by invoking a novel integrated algorithm based on both the Reinforcement Learning (RL) and Multi-Choice Knapsack Problem (MCKP), termed as Q-MCKP. Besides, we proposed some improvements such as a discretization method of discretizing the costs so as to decrease the complexity of the model. Moreover, the reward function of Q-learning was rebuilt by concerning an additional impact factor among channels. We conducted experiments using approximately two years of daily practical advertising data. Comparing to the state-of-arts, our experimental results demonstrated more effective in two angles.

The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor, and track a target, object, or event. In this paper, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). We present a novel tracking model, named Grouping based Location Tracking (GLT), scaling well with the number of nodes and the number of mobile objects. GLT is based on the Grouping Hierarchy Structure, GHS. In GHS, nodes are partitioned into groups (not clusters) according to their maximum covered region (MCR) such that each group contains a number of nodes and a number of leaders. GLT consists of two tiers. The first tier, which is called the Notification Tree (NT), enhances the activation mechanism, the data cleaning mechanism, and the energy balancing mechanism. On the other hand, the second tier, which is called the Hierarchical Spanning Tree (HST), supports the data reporting mechanism and the lifetime prolonging mechanism. Simulations results show that GLT reduces the communication node selections overhead without diminishing object tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches.