Learning-based automatic modulation recognition on edge devices is envisioned as a critical enabler of real-time spectrum management in future 6G Internet of Things networks. In addition to the requirement of accurate modulation detection, edge devices pose the challenge of reducing the size of the automatic modulation recognition model. This paper proposes a lightweight hybrid model of multi-channel convolutional neural network and the mobile vision transformer. The proposed model addresses both accuracy improvement and model size reduction by employing weight-based pruning and post-training dynamic range quantization. Performance results in edge computing environments such as the Raspberry Pi and Jetson platforms using the RadioML 2016.10a dataset show that the proposed model achieved up to a 91% reduction in memory usage compared to its original version prior to pruning and quantization and demonstrated up to an 8% improvement in average accuracy compared to the baseline convolutional neural network model.

Cross-domain data sharing is essential for industrial 4.0, envisioning for intelligent manufacturing, equipment collaboration and industrial automation. Inherently, gearing by the interconnection of devices and systems across heterogeneous domains, how to ensure efficient and secure multi-party data sharing remains a critical challenge in the Industrial Internet of Things (IIoT). Many broadcast encryption and signature encryption schemes have been proposed to ensure the privacy and rapid sharing of cross-domain data in recent years . However, existing schemes still have shortcomings in supporting fine-grained access control and adapting to resource-constrained edge node deployments. To address this issue, this paper proposes a ciphertext policy attribute-based broadcast matching encryption scheme (CP-ABBME). This scheme integrates a ciphertext policy attribute-based encryption (CP-ABE) mechanism to achieve one-to-many secure ciphertext transmission in a fine-grained manner. Furthermore, considering the limited computation and storage resources of terminal devices, the proposed scheme builds an encapsulation–decapsulation chain architecture. It introduces bidirectional access control for mutual authentication between the sender and receiver, while offloading deployment and partial decryption to the receiving gateway. This design effectively reduces terminal-side computation and storage overhead during decryption and authentication. Formal security analysis shows that the proposed scheme meets stringent security requirements. Experimental results show that CP-ABBME improves the decryption performance by 39.3% to 99.6%, and reduces the terminal storage resource consumption by up to 9.35 KB.

With the rapid development of Internet of Vehicles (IoVs), the imperative for data security within IoVs has increased, especially regarding data tampering and spreading false data. To address this issue, a secure blockchain-based trust management is employed to detect malicious behaviors. For instance, legitimate vehicles may be exploited to disseminate incorrect data, while blockchain technology maintains the transparency and reliability of trust value. However, existing schemes are challenged by the slow generation of trust opinions (lack of trust opinions due to insufficient interactions) and high latency. To solve such problems, we propose a Blockchain-based Trust Management secure Social Routing in IoVs (BTSR). Specifically, the trust management for vehicles we proposed then considers four types of trust: direct trust, indirect trust, global trust, and RoadSide Unit (RSU) trust. The direct trust is evaluated by the data-sending vehicle, indirect trust is provided by other vehicles, global trust originates from RSUs, and RSU trust is used to evaluate RSUs by Trusted Authority (TA). Furthermore, to speed up the generation of trust opinions, a verifiable active detection mechanism is employed via the RSU to increase the frequency of interactions. Here, the sender transmits detection data to the receiver, which then forwards it to the RSU for validation, thereby enhancing the interaction between the vehicle and the RSU. Meanwhile, to prevent active detection from being exploited by malicious RSUs and vehicles, the Trust Outlier Factor (TOF) algorithm is introduced to identify outlier RSUs whose trust opinions significantly deviate from those of other RSUs. Finally, we employ a dual-layer Hyperledger Fabric (HF) to reduce latency by lowering the consensus requirement at the edge layer, and to ensure data security by combining trust management at the core layer. A multitude of experiments confirms that our scheme surpasses baseline schemes, showcasing high precision, recall, and F-measure.

As efficiency and security requirements emerge in computing offloading fields, trusted computing offloading has grabbed tremendous sights, especially in Air-Ground Integrated Networks (AGINs). Although traditional computing offloading studies have attempted to employ trust to identify malicious devices without mobility, the integration of trust management and computing offloading has not been explored in a high-hazardous environment. Then, with the increasing requirement of adaptation and security in new-generation network architectures (i.e., AGINs), trust management plays a pivotal role in expanding the implementation of trusted computing offloading. Therefore, we propose a trusted time-aware computing offloading mechanism in AGINs based on communication, computing offloading, and trust modeling. Specifically, a maximum reward optimization formulation is designed to generate an optimal offloading strategy, considering service utility trust, opinion service trust, computing efficiency trust, time constraints, rewards, and task volumes. Based on problem analysis, a solution paradigm is condensed to improve the probability of seeking an efficient solution. Moreover, a greedy search algorithm is designed to find the possible efficient solution, including the default solution setting stage, the boundary search stage, and the greedy reallocation stage. Due to the trust threshold affecting identification results, a dichotomy-based dynamic trust threshold method is employed to empower trusted computing offloading mechanism with the adaptive capability in AGINs. Extensive experiments show that our mechanism outperforms other baselines in terms of accuracy, precision, recall, F-measure, task success rate, and average response time.

Data collection and distribution through crowdsensing has become an emerging trend in smart city scenarios. By leveraging existing vehicle resources without deploying dedicated infrastructure, Vehicular CrowdSensing (VCS) provides low-cost and high-mobility data collection on road networks. Typically, the Crowdsensing Platform (CP) issues data collection tasks, recruits Sensing Vehicles (SVs) to complete tasks, and sells the collected data to Data Demanders (DDs). Here, the goal of CP is to maximize profits through data collection and sales, and the goal of DDs is to improve satisfaction by purchasing high-quality sensing data. It can be seen that both CP and DD hope that SVs can complete more sensing tasks at a limited cost (high efficiency) while ensuring the accuracy of data collection (high quality). However, due to individual rationality and selfishness, not all SVs are willing to complete the sensing task. Therefore, how to motivate SVs to complete sensing tasks with high quality and efficiency, while handling the relationship among CP, DDs, and SVs, is a problem that needs to be considered. To solve the above problems, this paper proposes a Reputation-based Hybrid Incentive Approach (RHIA), with the goal of maximizing the utility of CP, SVs, and DDs. Specifically, in order to improve the task completion quality of SVs, we introduce vehicle reputation to measure SVs. Then, we propose a one-to-one bargaining game between CP and each SV, and use the reputation value as the sequential basis of the game. Meanwhile, in order to improve the task completion efficiency of SVs, we also design a unique SV Trajectory Planning Algorithm (STPA). Further, in order to meet the needs of DDs, a one-to- multi Stackelberg game between CP and DDs is proposed. Here, the existence and uniqueness of Nash equilibrium is proved through backward induction. Finally, based on real-world datasets, the effectiveness of our proposed RHIA and STPA is verified. Our proposed method can ensure the long-term stability of the VCS system, which also improves the utility of participating individuals.

Enhancing the quality of experience (QoE) in interactive video streaming (IVS) remains a persistent challenge due to the need for ultra-low latency and rising bandwidth demands. Conventional algorithms, whether rule-based or learning-based, are obsessed with achieving tight coupling between encoding and sending bitrate adaptations for low-latency guarantee. However, our measurement studies reveal alarming harms of tight coupling in suppressing throughput, encoding bitrates and smoothness, as application- and transport-layer bitrate adaptations inherently have different mechanisms and goals. To tackle this problem, we propose Octopus, the first loosely coupled cross-layer bitrate adaptation algorithm for IVS to maximize QoE. Instead of blind synchronization, Octopus promotes mutual cooperation and independence between encoding and sending bitrate adaptations by integrating a multi-head network with shortcut connections and auto-regressive action modules. Additionally, based on meta-imitation reinforcement learning, we design a network condition-aware online adaptation scheme that enables the loosely coupled policy to swiftly adapt to diverse and dynamic wireless networks. We implement Octopus on a testbed, a microcosm of real-world deployment, with transceiver pairs running WebRTC on the WeChat for Business dataset. Results show that Octopus outperforms state-of-the-art algorithms, either improving bitrates by 37.1%, or optimizing stalling rate and smoothness by 54.1% and 9.2%, or achieving all-around improvements.

Belief propagation (BP) is a promising technique capable of providing reliable marginal probability distributions for the factor graph (FG) based wireless distributed cooperative positioning (DCP), which is of paramount importance in scenarios lacking global navigation satellite systems. However, BP may fail to converge on FG with short loops and thus only attains a poorly approximate distribution in practical applications. To solve the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network (GNN) enhanced parametric belief propagation (GNN-PBP) approach. We first approximate the nonlinear terms in the FG-based spatio-temporal messages by using Taylor polynomials, thus obtaining high-precision closed-form representations for each message flowing on the FG. The parametric representations of spatial messages are then refined by GNN-based learning. Finally, high-accuracy closed-form expressions for the a posteriori distributions of node positions are derived by inference on the FG. Numerical results demonstrate that our method is able to improve the positioning accuracy for wireless networks that have high density loops.

In developing Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traffic sign detection is a critical task. However, existing methods face limitations in real-time performance and small-object detection accuracy, partly due to deployment platform constraints. Our proposed Neck-Heavy YOLOv5 (NH-YOLOv5) introduces significant improvements. It features a hybrid data augmentation approach that enhances the detection of small objects. Moreover, a unique neck-heavy network architecture is designed to optimize detection performance without increasing computational complexity. Notably, NH-YOLOv5 is integrated into the digital twin framework of traffic systems. By leveraging the digital twin’s ability to simulate and monitor traffic scenarios in real time, the model can continuously adapt and improve its detection capabilities. Experimental results based on the Tsinghua - Tencent 100K (TT100K) dataset demonstrate that our method achieves a 53% reduction in computation while enhancing accuracy and small object detection ability, making it highly suitable for real-time traffic sign detection in the context of digital twins.

The Internet of Vehicles (IoV) presents a rapidly evolving ecosystem that enables seamless communication among vehicles, infrastructure, and pedestrians. However, the open nature of the IoV exposes it to various security threats, including the spread of malicious data and trust violations. To address these challenges, we propose a Filter-Based Trust Evaluation (FTE) system, which combines direct and indirect trust evaluation metrics to assess the trustworthiness of vehicles. The proposed system utilizes a Kalman filter-based approach to fuse predicted and measured trust values, allowing for dynamic and accurate trust assessments. Our extensive simulations show that FTE outperforms existing trust management schemes, demonstrating superior accuracy, efficiency, and robustness, even in environments with high malicious vehicle ratios. The results highlight the potential of FTE for secure and reliable operation in the IoV systems.

As Electric Vehicle (EV) adoption accelerates, the limitations of fixed charging infrastructure, such as spatial inflexibility and peak-time congestion, become more evident. Vehicle-to-Vehicle (V2V) charging allows EV with surplus energy to directly supply others, offering a decentralized complement to conventional infrastructure. Depending on real-time context, energy exchange can occur in either a static mode (while parked) or a dynamic mode (in motion along overlapping routes). Selecting the appropriate mode and coordinating trustworthy peers under uncertain traffic, energy, and spatial conditions poses a significant challenge. This paper presents a hierarchical framework for trust-aware V2V charging coordination under mode uncertainty. At the strategic layer, a fuzzy logic-based trust estimator selects the most reliable charging mode based on factors such as energy gap, location density, and traffic state. At the operational layer, a unified Large Neighborhood Search (LNS) algorithm performs robust EV pairing, using greedy heuristics for static mode and DTW-based trajectory alignment for dynamic mode. Trust-weighted objectives and soft constraint penalties are incorporated to enhance decision robustness and mitigate unreliable matches. Experiments on real-world Helsinki mobility data demonstrate that the framework improves match success rates, reduces waiting times and trajectory deviations, and enhances the overall resilience of V2V energy coordination.

The Society of Automotive Engineers (SAE) J2954 Differential Inductive Positioning System (DIPS) is a ground breaking technology introduced to enable positioning technique for electric vehicle (EV) wireless power transfer (WPT). This technology and its related standard have great potential to bring EV wireless charging to mass production and opens the doors for commercializing autonomous vehicles. Although the DIPS standard has defined the hardware requirements [1], the positioning algorithm design has not been addressed in the literature. In this paper, we introduce the industry's first algorithm for DIPS. We mathematically derive the signal model and parameters estimation algorithm, then evaluate the estimation accuracy of the proposed algorithm using Monte Carlo simulations. The evaluation results have shown the algorithm can achieve centimeter-level accuracy and approach the Cramér-Rao bound.

In this paper, a low-complexity beam hopping pattern design with frequency reuse (FR) is proposed to efficiently adapt to the non-uniform traffic demands characteristics of the terrestrial cells. Concretely, an FR-based beam hopping (BH) downlink transmission model is conceived for multi-beam satellite systems. Next, a heuristic BH pattern optimization algorithm with FR and load balancing (BH-FR-LB) is proposed to improve the communication capacity, which is divided into two sub-problems with low complexity. Specifically, the cells are allocated into different frequency sub-bands to optimize the traffic load. The selection of the BH pattern is updated based on the remaining traffic demands of the terrestrial cells at each time slot, thereby achieving a trade-off between spectrum efficiency and inter-beam interference mitigation. Simulation results show that the proposed scheme obtains higher traffic satisfaction rate, while only requiring fewer time slots to achieve the data transmission under low traffic demands.

Modern rescue operations rely on wireless communications for safety reporting, area monitoring, and rescue coordination. However, natural disasters severely damage ground infrastructure, creating significant challenges for emergency rescue and recovery efforts. This paper establishes a disaster response network using Movable and Deployable Resource Units (MDRUs) in disaster-affected areas, to provide timely and reliable message transmission services. Firstly, to ensure a timely and efficient disaster response, we design a post-disaster emergency vehicle network architecture. Secondly, we propose a three-phase emergency relief model to dynamically deploy MDRUs, aiming to maximize their service coverage. Finally, we propose a Trajectory-Based Anycast Routing (TBAR) protocol, which enhances message transmission efficiency by optimizing route selection. Specifically, by facilitating the flexibility of any cast in delivering messages to anyone of the reachable MDRUs, TBAR utilizes multiple copies of messages to reduce end-to-end latency and increase the delivery ratio. Moreover, TBAR adaptively evaluates the message delivery capability of candidate vehicles using a multi-attribute decision-making algorithm, considering link quality, trajectory similarity, and distance cost. Extensive simulation results show that TBAR significantly outperforms other baseline algorithms in multiple aspects.

With the development of Internet of Vehicles (IoVs), data security emerges as a significant challenge, especially regarding data tampering and the spread of false information. While cryptography technologies tackle external security threats, they fall short in addressing internal security threats, such as authorized malicious vehicles tampering with and spreading false information. Consequently, trust management becomes a crucial technology, focusing on the analysis and identification of internal inappropriate behaviors to ensure safe interactions among vehicles. This article explores the effective integration of trust opinions provided by roadside units (RSUs) into trust evaluations in IoVs, ensuring the comprehensiveness and accuracy of trust evaluations. We propose a dual-model consensus-based anti-risk confidence allocation trust management scheme (DCACA) in IoVs. Specifically, DCACA utilizes direct trust, indirect trust, and global trust, to evaluation the trustworthiness of vehicles. Furthermore, to address the potential untrustworthiness of network entities (RSUs and vehicles), DCACA employs a dual-model consensus mechanism operates two processes of reaching consensus, including the real-time collection consensus mechanism (RCCM) and the matrix-based consensus mechanism (MCM). RCCM is based on real-time collected trust opinions, reaching consensus to identify potential malicious trust opinions. MCM utilizes trust opinion matrices to collect trust opinions and achieves consensus through the elements in these matrices, identifying the sources of malicious trust opinions. Additionally, DCACA utilizes an anti-risk confidence allocation mechanism assigns confidence levels based on risk assessments, to mitigate the impact of malicious entities. Extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting high levels of precision, recall, and F-measure.

The rapid growth of electric vehicle and autonomous vehicle populations has led to explosive expansion of IoV data being transmitted in the wireless communication infrastructure. Advances in IoV technologies also resulted in more complex and dynamic communication protocols/patterns, which are hard for the underlying wireless network to satisfy. Besides, security considerations of IoV communications require that key management must be stringently prohibit global failure mode of key management, meaning that, if a single IoV node compromises its private key, it will not lead to total security failure of the entire IoV network. To address these issues, in this paper, we propose a heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV (HPKI-MRSC). Firstly, the proposed scheme can realize one-to-many heterogeneous transmission, in which RSUs are deployed on certificateless cryptography (CLC) system, while vehicles are allocated in identity-based cryptography (IBC) system. In this manner, we observe that message transmission efficiency is improved greatly. Secondly, the parallel key-insulated mechanism can employ two helper keys to update private key periodically, and then solve key disclosure problem. Finally, when the number of receiver n is greater than or equal to 3, the proposed scheme has a lower signcryption overhead than other comparative schemes, and thus it is more suitable for IoV.

Under the development of intelligent transportation systems, In-Vehicle Networks (IVNs) serve as a critical channel for both internal and external communications. However, the inherent complexity and diversity of data traffic present significant challenges for the detection of IVN anomalous flows. Meanwhile, the introduction of various novel technologies has introduced new security vulnerabilities to IVNs. These vulnerabilities significantly impact the security of IVNs and the accuracy of in-vehicle Intrusion Detection Systems (IDS). To address these issues, this paper proposes a lightweight and efficient anomaly detection method based on knowledge distillation technology, termed Knowledge Distillation from BERT to CNN-BiLSTM (KDBC). Specifically, the KDBC distills the deep semantic knowledge from the BERT model into a more lightweight CNN-BiLSTM architecture, significantly reducing computational overhead and storage requirements without substantially compromising detection performance. Experimental results demonstrate that the KDBC model enhances both security and versatility, achieving superior detection accuracy in identifying abnormal attacks across diverse IVN data, including automotive Ethernet and CAN networks. Moreover, the KDBC model has been validated for its effectiveness and robustness in actual in-vehicle gateway environments, achieving an accuracy of over 0.98 and an F1 score greater than 0.98.

Reputation updating plays a vital role in cloud-supported vehicular networks, ensuring the continuous freshness of trustworthiness. However, the existing solutions suffer from insufficient privacy and security, as well as impose significant computation and communication overheads on resource-constrained vehicles. In addition, they require vehicles to pre-load numerous keys and reputation certificates, complicating certificate management along with key escrow and revocation issues. Thus, in this paper, we introduce an innovative Privacy-preserving and Blockchain-assisted Reputation Updating (PBRU) scheme with malicious detection, for cloud-supported vehicular networks. Specifically, based on the improved exponential ElGamal variant, the reputation feedback generation and verification process avoids time-consuming homomorphic exponential and bilinear pairing operations, such that computation and communication overheads of vehicles are significantly reduced by 87.42% and 43.32%, respectively. Besides, the PBRU scheme reconstructs the key derivation algorithm and records reputation certificates on the blockchain, eliminating the need for pre-loading keys and certificates on vehicles while enabling traceability. Moreover, the PBRU scheme is capable of detecting duplicate malicious feedbacks by utilizing Bloom filter. Furthermore, theoretical proof and analysis present that the PBRU scheme satisfies more security requirements than the state-of-the-art schemes. Finally, the comprehensive simulation evaluation demonstrates the effectivity and practicality of our PBRU scheme.

Unmanned aerial vehicles (UAVs) equipped with high line-of-sight communications have been explored as a complement to emergency communication vehicles (ECVs), particularly when ground Internet of Vehicles (IoVs) is dysfunctional resulting from natural disasters. To protect the communication security and integrity of this air-ground integrated networks created in an untrusted and open wireless environment, authentication and key agreement (AKA) is an essential mechanism to establish a secure communication channel by negotiating a session key. Nonetheless, general AKA protocols are typically not ideal for time-sensitive and computing-intensive UAVs-assisted IoVs emergency rescue, in that they are unable to simultaneously satisfy efficiency and key security requirements including accountability, resilience, thresholds changeability, shares verifiability, group adaptability, and key update. To address this challenge, this paper proposes an accountable group authentication and key agreement (GAKA) protocol with changeable thresholds and verifiable shares supporting adaptive group memberships and updatable keys (i.e. SecER). To safeguard accurate rescue decisions for ground ECVs enabled by reliable collaboration among multiple UAVs, we propose a pseudonym mechanism that aims to provide accountability for UAVs. To achieve resilience and thresholds changeability, our SecER utilizes secret sharing coupled with random parameters to seamlessly transform our GAKA protocol into one running with a new threshold in one round (i.e. round-optimal), rather than two rounds. Finally, verifiable parameters and updatable keys are respectively applied to counter deception attacks caused by maliciously distributed shares and to support adaptive network topology (i.e. UAVs joining and leaving). Extensive simulations show that compared to the state-of-the-art approaches, our SecER is superior in balancing security and efficiency.

To assess the vulnerability of deep neural networks in the physical world, many studies have introduced adversarial examples and applied them to computer vision tasks such as object detection in recent years. Compared to patch-based adversarial attacks, camouflage-based attacks have received more and more attention due to their ability to attack detectors from multiple viewpoints. However, existing adversarial examples often rely on glass-box models and exhibit limited transferability to closed-box models, which remains a significant challenge. To address this issue, we propose the highly transferable camouflage attack, a novel physical adversarial attack framework designed to generate robust and efficient adversarial camouflage that can mislead object detectors in diverse scenarios. Specifically, we introduce a distraction method to distribute the features of the attention map between models, and propose enhanced transfer strategies to improve adversarial transferability through augmenting the input data and the attacked models. Extensive experiments demonstrate that our highly transferable camouflage attack can effectively mislead object detectors in both digital and physical worlds, enhancing the transferability of adversarial camouflage on multiple mainstream detectors.

Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-drivenDouble-layerReverseAuctionMethod (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.

Visible Light Positioning (VLP) has emerged as a promising technique in the Internet of Things landscape and gained increasing attention worldwide due to its widely existing infrastructure, high precision, and cost-effectiveness. Recently, ratio and difference-based VLP systems have been used to reduce errors from environmental noise, ambient light, and device differences. However, there may be intricate interference patterns that simple ratios and differences struggle to address. Moreover, a single LED often has limited capability to achieve self-diagnosis and self-correction. In fact, the information from other LEDs can be used to refine the signal and suppress interference. Thus, we propose to organize the VLP system in a graph and use the Graph Neural Network to model the interrelationships among LED lamps. This allows us to optimize the signals and further efficiently suppress interferences by simultaneously considering multiple LED lamps. In addition, the precisions of LEDs’ measurements is different due to various factors (e.g., distances and powers), and low-precision measurements may reduce the performance of the VLP system. To address this issue, we incorporate an attention layer to allow our model to give higher weights to high-precision measurements. Finally, the long short-term memory network is used to model the temporal dependencies between adjacent positions in a trajectory. Taking these modules together, we develop a robust VLP system called DeepVLP. The comprehensive experiments demonstrate that DeepVLP achieves better performance than state-of-the-art methods.

In disaster scenarios, secure and reliable data collection in Vehicular Ad Hoc Network (VANET) is crucial, yet the network often suffers from issues such as infrastructure damage, network partitioning, and vulnerabilities to attacks (e.g., False Data Injection and Black Hole Attack). Trust management is a promising solution to prevent these attacks. However, in infrastructure-less and partitioned disaster areas, existing trust schemes face problems of trust evidence sparsity and evaluation inconsistency, leading to inaccurate detection. To address these limitations, we propose an Unmanned Aerial Vehicle (UAV)-Assisted Traceable and Hierarchical Trust Management scheme (UATHTM). The UATHTM includes a Vehicle-to-Vehicle (V2V) local trust model and a UAV-to-Vehicle (U2V) global trust model. The former facilitates rapid detection of false data, while the latter is designed for accurately tracing malicious vehicles. Specifically, the V2V model constructs a mutual adjustment between entity-centric and data-centric assessments, continuously refining trust for accurate local detection. The U2V model incorporates a new trust metric based on disaster trajectory similarity to enhance the accuracy of global tracing, through leveraging the comprehensive view of UAVs. Extensive simulations demonstrate that the UATHTM scheme outperforms existing trust management schemes, showing higher precision, recall, and F1-score in detecting false data and malicious vehicles in VANET under challenging conditions.

Future mobile wireless networks will catalyze substantial demand for precise distributed cooperative positioning (DCP), especially when the global navigation satellite systems are unavailable. However, conventional message passing based DCP methods may suffer considerable performance degradation due to message approximation and sparsity/mobility of nodes. In this paper, we first present a high-accuracy parametric message approximation method, which achieves closed-form representations of all types of messages involved and reduces the computational complexity of message passing procedures. Using these representations, we propose a model- and data-driven hybrid inference approach, dubbed graph neural network enhanced spatio-temporal message passing (GNN-STMP), which fine-tunes parametric messages passed on factor graph and obtains more accuratea posterioridistribution of nodes’ positions by exploiting GNN-generated messages. Furthermore, we develop a universal framework for the parametric message passing based DCP problem, by integrating GNN-STMP with the extend Kalman filter based node’s state prediction and refinement. This framework significantly reduces the positioning ambiguity caused by insufficient spatial ranging measurements from neighbor nodes. Simulation results and analyses demonstrate that, compared with state-of-the-art methods, our proposed approaches achieve the best and near-best positioning accuracy when insufficient and sufficient spatial ranging measurements are available, respectively, while incurring modest computational complexity.

Parametric message passing (MP) is a promising technique that provides reliable marginal probability distributions for distributed cooperative positioning (DCP) based on factor graphs (FG), while maintaining minimal computational complexity. However, conventional parametric MP-based DCP methods may fail to converge in dense wireless networks due to numerous short loops on FG. Additionally, the use of inappropriate message approximation techniques can lead to increased sensitivity to initial values and significantly slower convergence rates. To address the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network enhanced fast convergent parametric MP (GNN-FCPMP) method. We first employ Chebyshev polynomials to approximate the nonlinear terms present in the FG-based spatio-temporal messages. This technique facilitates the derivation of globally precise, closed-form representations for each message transmitted across the FG, and reduces MP's sensitivity to initial positional values. Then, the parametric representations of spatial messages are meticulously refined through data-driven GNNs. Conclusively, by performing inference on the FG, we derive more accurate closed-form expressions for the a posteriori distributions of node positions. Numerical results substantiate the capability of GNN-FCPMP to significantly enhance positioning accuracy within wireless networks characterized by high-density loops and ensure rapid convergence.

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications.

Over the past few decades, as vehicles have become increasingly intelligent, the applications of in-vehicle electronic systems have expanded significantly. However, with the growing complexity of vehicle networks, there is an ever-increasing concern for their network security. In particular, the Controller Area Network (CAN) bus has become a critical medium for communication between various Electronic Control Units (ECUs) within a vehicle. Since the design of the CAN bus lacks sufficient security measures, it is vulnerable to various network intrusions. To address this security challenge, researchers have been searching for ways to enhance the network security of the CAN bus to ensure that vehicle systems are not compromised by unauthorized access or network attacks. This paper introduces a robust intrusion detection system (IDS) for the CAN bus in vehicles, employing a novel Multi-Scale Feature Fusion technique. Leveraging the distinct capabilities of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer neural network architectures, the proposed methodology adeptly captures and prioritizes both shallow and deep features of CAN bus data. Besides, it enhances detection accuracy and robustness against various cyber-attacks. Evaluation on the Carhacking dataset demonstrates superior performance, achieving a precision, recall, and F1-score of 100%. Verified by an ablation study, this approach promises a substantial advancement in safeguarding in-vehicle networks.

With the development of driverless technology, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in the realm of commodity delivery. In the scenario with a high requirement for timeliness commodity delivery, the mobile unmanned retail mode has grabbed tremendous sights. However, the traditional retail mode solely focuses on presetting routes for delivery, but fails to deal with the real-time change of customers orders with the assistance of UAVs and UGVs. In this paper, we propose an air-ground cooperative real-time delivery scheme based on joint scheduling. Specifically, UGVs deliver commodities to customers based on spatial-temporal costs (e.g., delivery distance and order urgency). The UAV serves to replenish UGVs with commodity resources, achieving a balance between regional resource consumption and UAV resource replenishment based on joint scheduling. Finally, experimental results show our scheme outperforms other baseline schemes in terms of customers average waiting time, UGVs average delivery delay time, and UAV total flight distance.

Electric Vehicle (EV) is regarded as the optimal alternative to traditional fuel-powered vehicles. However, the exponential surge in EV charging demand poses challenges in charging infrastructure planning and charging behavior management. The efficacy of traditional Grid-to-Vehicle (G2V) charging mode, which obtains power from the grid, is curtailed by the limited number and uneven distribution of charging facilities, inevitably leading to charging congestion. Instead, the concept of Vehicle-to-Vehicle (V2V) charging has emerged as a spatio-temporally flexible charging mode, which expands EV's role from consumer to provider, forming V2V pairs and utilizing urban Parking Lots (PLs) as charging locations. In this paper, we propose a Hybrid Heterogeneous Modes (HHM)-based EV charging optimization scheme in urban settings. Building upon the G2V mode, we introduce synchronous and asynchronous V2V charging modes as optimization strategies, integrating and exploiting the unique advantages of each mode. We also utilize global EV charging scheduling and comprehensively take four dimensions into consideration (energy trading cost, travel cost, waiting time cost and loss cost), thus achieving flexible mode selection, V2V pairing, and designated charging locations. Simulations confirm the effectiveness of the proposed scheme in reducing total charging costs, optimizing user service experiences, and improving charging facility utilization rates.

Nowadays, Unmanned Aerial Vehicles (UAVs) play an indispensable role in many industries, exhibiting distinctive value and potential. However, there are lots of common threats in UAV networks, such as black hole attacks and message tampering. Additionally, as the size of UAV Flying Ad hoc Networks (FANETs) gradually increases, the network overhead and latency also increase. To address these issues, we propose a Trust Model with Fitness-Based Clustering Scheme (TMFCS) in FANETs. TMFCS integrates the trust model and clustering scheme, aiming to improve network security and reduce network overhead. Specifically, TMFCS focuses on unintentional abnormal behavior (e.g. packet loss due to low energy) and message characteristics (e.g., timeliness, accuracy, and integrity) in the trust evaluation. In addition, TMFCS integrates the fitness-based clustering scheme. The scheme selects cluster heads based on density, trust, and energy, which can effectively ensure clustering security and reduce the network overhead. Meanwhile, TMFCS introduces a cluster maintenance phase to improve network topology stability, by reducing the clustering times. Extensive experiments have shown that TMFCS has higher detection performance than other baseline models, while ensuring clustering security and topological stability.

Trust management is a representative research domain to ensure secure communication and cooperation among entities in Vehicular Ad-hoc Networks (VANETs). However, vehicles as entities are not always trustworthy because of the openness characteristics of VANETs, such as various malicious participators, and selfish behaviors. In this paper, we propose a Path-Backtracking-based Trust Management Scheme (PBTMS) for VANETs to evaluate the trustworthiness of entities based on the interaction histories (according to previous interaction counts), message path-backtracking (tracking the origin and route of messages), and multi-path analysis (analyzing the like-lihood of malicious vehicle presence). Specifically, within the PBTMS framework, the trustworthiness of vehicles is meticulously evaluated. By incorporating both entity trust and path trust metrics, PBTMS computes an aggregate trust value for each vehicle. Extensive simulations demonstrate that PBTMS surpasses other baseline algorithms in accuracy, achieving higher precision in evaluating entity trustworthiness.

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

The Internet of Vehicles (IoV) has witnessed a significant growth in the number of participants. This rapid expansion has increased demands for computing resources and quality of service (QoS), posing challenges for mobile edge computing (MEC) in the IoV domain. Efficiently allocating computing power to meet these service demands has become a crucial concern. Therefore, joint optimization of offloading decisions and power allocation is required to achieve the tradeoff between task latency and energy consumption. To address the above challenge, we propose a multi-agent reinforcement learning (MARL) method called multi-agent twin delayed deep deterministic policy gradient (MA-TD3) in this paper. Compared to its predecessor, multi-agent deep deterministic policy gradient (MADDPG), this algorithm improves performance and execution speed. It solves the slow convergence problem caused by Q-value overestimation and reduces the computational cost. The experimental results illustrate that the proposed algorithm reaches an observable performance improvement.

With the development of Vehicular Ad-hoc Networks (VANETs), several data security challenges are revealed, such as data hijacking and interception. Although vehicles are authorized, malicious behaviors still be carried out. Security lapses may lead to potential accidents, which emphasizes the importance of laying a solid security foundation for VANETs. Thanks to the base security layer provided by cryptography technologies, security problems can be solved in VANETs to avoid accidents. However, trust management focuses on the analysis and identification of misbehavior, to ensure secure interactions among vehicles, and preserve data integrity against security issues. This paper explores trust assessments that consider the transmission path of message as a novel indicator, to provide a comprehensive and accurate trust assessment. We propose a Multidimensional trust Evidence Fusion and Path-Backtracking mechanism for trust management scheme (MEFPB) in VANETs. MEFPB integrates the multidimensional trust evidence fusion and path-backtracking mechanism. Specifically, MEFPB utilizes the Dempster-Shafer theory to fuse multi-dimensional indicators (direct trust, indirect trust, and transmission path of message) for evaluating the trustworthiness of vehicles. The direct and indirect trust are supplied by the message-sending vehicle and its neighbors (i.e., other vehicles). The transmission path of message is provided by roadside units. Furthermore, the path-backtracking mechanism identifies and traces malicious behaviors based on the transmission path of message. Moreover, extensive experiments demonstrate that our scheme significantly outperforms other baseline schemes, exhibiting a high malicious behavior detection rate within VANETs.

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

With the growing popularization of the Internet of Vehicles (IoVs), the combination of satellite navigation system and IoVs is also in a state of continuous improvement. In this article, we present a heterogeneous signcryption scheme with group equality test for IoVs (HSC-GET), which avoids the adversaries existing in the insecure channels to intercept, alter or delete messages from satellite to vehicles. The satellite is arranged in an identity-based cryptographic (IBC) system to ensure safe and fast transmission of instruction, while the vehicles are arranged in certificateless cryptosystem (CLC) to concern the security of the equipment. In addition, the group granularity authorization is integrated to ensure the cloud server can only execute the equality test on ciphertext generated by the same group of vehicles. Through rigorous performance and security analyses, we observe that our proposed construction reduces the equality test overhead by about 63.96%, 81.23%, 80.84%, and 54.98% in comparison to other competitive protocols. Furthermore, the confidentiality, integrity and authenticity of messages are guaranteed.

Edge caching is a promising technology to reduce backhaul strain and content access delay in Internet of Vehicles (IoV). It precaches frequently used contents close to vehicles through intermediate roadside units. Previous edge caching works often assume that content popularity is known in advance or obeys simplified models. However, such assumptions are unrealistic, as content popularity varies with uncertain spatial-temporal traffic demands in IoVs. Federated learning (FL) enables vehicles to predict popular content with distributed training. It preserves the training data remain local, thereby addressing privacy concerns and communication resource shortages. This article investigates a mobility-aware edge caching strategy by exploiting asynchronous FL and deep reinforcement learning (DRL). We first implement a novel asynchronous FL framework for local updates and global aggregation of stacked autoencoder (SAE) models. Then, utilizing the latent features extracted by the trained SAE model, we adopt a hybrid filtering model for predicting and recommending popular content. Furthermore, we explore intelligent caching decisions after content prediction. Based on the formulated Markov decision process (MDP) problem, we propose a DRL-based solution, and adopt neural network-based parameter approximations for the curse of dimensionality in RL. Extensive simulations are conducted based on real-world data trajectory. Especially, our proposed method outperforms federated averaging, least recently used, and NoDRL, and the edge hit rate is improved by roughly 6%, 21%, and 15%, respectively, when the cache capacity reaches 350 MB.

For unmanned aerial vehicles (UAVs)-assisted post-disaster emergency communication networks, UAVs serves as relay nodes of air-based backup network to support transmission of rescue messages to emergency communication vehicles (ECVs), while ECVs provide on-site ground communication and connectivity to the command center (CC) of the rescue operation. Existing works seldom emphasize communication security such as authenticity of communicating parties and integrity of message content. In this connection, authentication and key agreement (AKA) protocols are promising solutions for achieving communication security. However, the traditional approaches to endpoint security and entity authentication of principals may not be practical in emergency situations, in which network equipment and security modules are exposed to an open and untrusted physical environment. Besides, there is a lack of attention to the study of privacy impacts resulted from the physical loss of UAVs. More importantly, cyber attacks and excessive overhead may deteriorate AKA availability. Motivated by above challenges, we propose an AKA protocol, namely AKAEC, which is based on three-factor (i.e. smart card, biometrics, and password) and physically unclonable function (PUF) for protecting UAVs-assisted emergency communication. Specifically, AKAEC includes ECV-to-UAV (E2U) and UAV-to-UAV (U2U), where the former achieves secure emergency communication between ECV and UAV, while the latter realizes secure emergency communication between UAV and UAV. We then provide a formal security proof under the Real-Or-Random (ROR) model and formal security verification by AVISPA. This is followed by a security analysis to show that AKAEC meets the security goals defined for emergency situations. Finally, the performance of AKAEC is evaluated from communication overhead and computational overhead.

Securing Internet of Vehicles (IoVs) systems against common threats such as false message injection remains challenging, and one typical approach is to deploy trust management solutions. In this work, we propose a Consensus-Adaptive Trust management (CAT) based on the Group Decision Making (GDM) in IoVs. Specifically, in our approach the consensus levels are calculated to measure the difference of opinions (trust values) among vehicles. To estimate the reliability of consensus levels, the divergence between consensus levels is calculated, namely: consensus level similarity. GDM allows us to dynamically adjust the opinion of vehicles (namely: Consensus Reaching Process, CRP). Then, a credit guarantee mechanism is designed to improve the efficiency of CRP and seek out malicious vehicles quickly. To empower the adaptability of trust management for changing environments in IoVs, CAT dynamically manages opinions of vehicles, self-confidence, and their consensus thresholds according to the feedback of delivered messages. Extensive simulation results show the potential of CAT operating in high-risk scenarios, and outperforming other competing baseline methods in terms of accuracy, precision, recall, and F-score.

By utilizing on-board sensors and computing resources in intelligent vehicles, vehicular crowdsensing can collect a series of sensing data. Typically, sensing vehicles can be divided into opportunistic vehicles with fixed trajectories and participatory vehicles with changeable trajectories. Therefore, to complete sensing tasks more effectively, how to combine the advantages of the mobility characteristics of the two vehicles is a challenging problem. To solve this problem, this paper innovatively proposes a joint scheduling and incentive-driven two-stage hybrid sensing method. Specifically, the method is divided into two stages: opportunistic vehicle selection and participatory vehicle scheduling. In particular, both types of vehicles are managed through the Crowd Sensing Platform (CSP). For the first stage, this paper proposes a reverse auction-based incentive mechanism to select the lowest-cost set of vehicles to complete sensing tasks. This mechanism mainly consists of two steps: winning vehicle selection and reward payment. It is also verified that the proposed mechanism can ensure the individual rationality and truthfulness of opportunistic vehicles. For the second stage, based on the first-stage sensing results, this paper proposes a Soft Actor-Critic (SAC) based approach to scheduling participatory vehicle trajectories to complete sensing tasks. In addition, this paper also considers sensing fairness to ensure the balance of sensing task completion in different sub-regions. Through the two-stage hybrid sensing method, this paper aims to minimize the CSP overhead while ensuring sensing fairness. Finally, extensive evaluation results based on Roma taxi data sets demonstrate that the proposed method works effectively and outperforms other benchmark schemes in different working scenarios.

With the rapid advancement of vehicle connectivity and intelligent technologies, an increasing number of vehicles are now connected to the Internet. However, these connected vehicles are vulnerable to malicious attacks, posing serious security events. In particular, the in-vehicle controller area network (CAN) bus has witnessed a rise in incidents involving various network attacks, such as denial of service (DoS), fuzzy attacks, and gear attacks. In response, this paper proposes an enhanced cuckoo filter-based intrusion detection system (ECF-IDS) for in-vehicle network. The ECF-IDS builds on an enhanced version of the cuckoo filter. It first utilizes the cuckoo filter to establish two lists (a normal list and an intrusion list) based on the labeled dataset using Car Hacking Dataset (CHD) and can-train-and-test dataset. Then, the input CAN traffic is sequentially compared with these two lists, where the conflicting traffic is further identified using a BERT-based model. The ECF-IDS is experimentally validated using the CHD and can-train-and-test dataset, demonstrating higher detection efficiency, lower resource consumption, and detection success exceeding 99% compared to other algorithms presented in previous studies. Furthermore, we conducted real in-vehicle environment testing on the ECF-IDS model, and its detection performance proved to be excellent.

Since the requirements of cross-domain/layer communications, the Space-ground Integrated Information Network (SIIN) becomes a strategic research area. To improve the service sustainability and reduce the latency of data transmission, literature works focus on evaluating the status of channels between ground stations and satellites, but underestimate the power of dynamic data allocation for handover management. This paper explores the relationship of data allocation and seamless handover in SIIN to provide high-reliability and service sustainability. We propose a Channel Perceiving-based Handover Management (CPHM) strategy to optimize the utilization of channels and dynamically adjust the data allocation strategy. Specifically, CPHM perceives the motion status of satellites to accurately evaluate their service time and reconstruct connectivities, e.g., altitude, velocity, motion direction, and location. Furthermore, CPHM evaluates the service capability of satellites to generate the strategy of data allocation and dynamically adjust this strategy. Then, to improve utilization of channels, CPHM manages transmission queues according the strategy of data allocation and length of queues. Extensive simulation results show that CPHM outperforms other baseline algorithms in terms of delivery ratio, average delivery latency, and interruption ratio.

As a convenient means of transportation, unmanned aerial vehicles (UAVs) can provide consumers and merchants with safe, efficient, and contactless instant delivery services. However, online orders are often concurrent, dynamic, and with time constraints in an instant delivery system. Therefore, it's important to optimally schedule the delivery sequence upon the UAVsystem. In this paper, we design a real-time UAV delivery scheduling model considering dynamic online orders. Through our instant delivery management system, the urban delivery network can be updated in real time according to order information. Considering the practical situation, customers with spatial diversity may produce orders with common order requirements, so we consider the overlap of UAV routes in the scheduling process. An order merging algorithm (OMA) is then proposed to improve the delivery efficiency (the ratio of the number of completed orders to the times UAVs visit stores) of UAVs. With the help of proposed system-level decision-making method, our system can meet real-time concurrent order demands across urban delivery networks. To evaluate the performance of system, we further carry out simulation experiments to verify the effectiveness of our scheme. Results show that the proposed UAVscheduling scheme improves the efficiency of UAV instant delivery.

Pattern matching in Intrusion Detection Systems (IDS) is one of the most critical and time-consuming elements, allowing the system to make decisions based on the real-time threats across the network. A pattern-matching method can be software-based or hardware-based. In this paper, a hardware implementation of bit-split algorithm has been discussed for pattern matching in order to detect unwanted traffic for a maximum number of rulesets. A hardware-based string matching scheme has been preferred here due to its fast speed and quick data parallelism for the high-performance Intrusion Detection System (IDS). The prototype of the detection method is implemented on Spartan SP605 with a small number of rules. For a large number of rules, a multi-scale Field Programmable Gate Array (FPGA)-based hardware architecture has been implemented in which we have used NetFPGA-SUME. This FPGA board has examined incoming packets at a bit rate of 1.25 Gb/sec with an operational frequency of 156.25MHZ. Furthermore, high-level data parallelism has been implemented by instantiating more than one match engine for handling multiple packets to achieve high throughput (Tp).

Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regional languages. Thus the Urdu FN purveyors cannot be discerned using fact-checking portals. State-of-the-art (SOTA) approaches for Fake News Detection (FND) count upon appropriately labelled and large datasets. FND in regional and resource-constrained languages lags due to the lack of limited-sized datasets and legitimate lexical resources. The previous datasets for Urdu FND are limited-sized, domain-restricted, publicly unavailable and not manually verified where the news is translated from English into Urdu. In this paper, we curate and contribute the first largest publicly available dataset for Urdu FND, "Ax-to-Grind Urdu", to bridge the identified gaps and limitations of existing Urdu datasets in the literature. It constitutes 10,083 fake and real news on fifteen domains collected from leading and authentic Urdu newspapers and news channel websites in Pakistan and India. FN for the Ax-to-Grind dataset is collected from websites and crowdsourcing. The dataset contains news items in Urdu from the year 2017 to the year 2023. Expert journalists annotated the dataset. We benchmark the dataset with an ensemble model of mBERT, XLNet, and XLM-RoBERTa. The selected models are originally trained on multilingual large corpora. The results of the proposed model are based on performance metrics, F1-score, accuracy, precision, recall and MCC value. F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902 demonstrate the effectiveness of the proposed approach for Urdu FND. Comparison analysis with SOTA ML and DL models and existing Urdu benchmark datasets exhibit that the ensemble model outperforms them for Urdu FND. The dataset used for our experiments is publicly available at https://github.com/HjH-Whu-CRC/Ax-to-Grind-Urdu for further analysis and validation.

With the emergence of various applications in Intelligent Transportation Systems (ITS), the cooperative communication among vehicles has become increasingly important to provide transportation data services. However, vehicles with selfish behaviors rarely provide high-performance communication services to others, resulting in a low packet delivery ratio and a high end-to-end delay. It is extremely challenging to substantially alleviate the influence of selfish vehicles in vehicular networks, because of the network topology and unstable communication qualities. To address the above problems, the Social similarity and Reputation Based Routing (SRBR) is proposed, considering social relationships, vehicle behaviors, and transmission capacity. The social similarity can improve network performance by leveraging the social relationships of vehicles, while reputation mechanisms can effectively defend against selfish vehicles’ attacks. Additionally, to integrate the above two-dimensional metrics, fuzzy logic is employed. Furthermore, extensive results show SRBR outperforms other baseline schemes in terms of delivery ratio, average latency, and overhead ratio.

Vehicular Ad-hoc Networks (VANETs) play an essential role in traffic safety and travel efficiency. However, due to the variable network topology of VANETs, malicious vehicles can easily invade the network to disrupt the network integrity. Moreover, compromised Roadside Units (RSUs) may pose a tremendous threat to network. Thus, we propose a distributed trust model to resist malicious vehicles and compromised RSUs by a mutual supervision mechanism between vehicles and RSUs. Three stages of this model ensure the trustworthiness of participants, including trust evaluation, adjudication, and vehicle appeal mechanism. In the trust evaluation stage, message receivers calculate three types of trust values (i.e., direct, indirect, and combined trust values) and upload them to RSUs. Then, RSUs dynamically update the trust threshold by aggregating vehicular trust values. In the adjudication stage, RSUs punish/reward vehicles by comparing the trust threshold to aggregated trust values. In the vehicle appeal stage, vehicles appeal to other RSUs if they have received the undesired punishment by an RSU. Then, multiple RSUs jointly judge whether a vehicle is successfully appealed, and the misjudging RSU will be punished. Extensive simulations show that the proposed model effectively identifies malicious vehicles with the presence of compromised RSUs.

The emerging applications of the Internet of Things (IoT), such as driverless cars, have an increasing need for precise vehicle positioning. Inertial navigation systems (INSs) became a possible component of autonomous driving systems due to low computational load, fast response, and high autonomy. However, error accumulation presents a significant challenge. Although nonholonomic constraints (NHCs) and odometry (ODO) have been demonstrated to improve INS, NHC is not always reliable, and ODO is often inaccessible in many applications. To address these issues, we propose a novel untethered pseudo-odometry, SdoNet, a convolutional neural network that estimates vehicle velocity from raw inertial measurement unit (IMU) observations to extend NHC as a 3-D velocity constraint without needing a hardware-wheeled ODO. To eliminate the influence of interference features on the accuracy of SdoNet, we improve the SdoNet by incorporating a residual module, attention mechanism, and soft threshold to guide the network to eliminate interference features. Moreover, a lightweight noise adapter network is proposed to adjust the constraint measurement noise covariance dynamically to apply the velocity constraint properly. The proposed approach is validated on the KITTI data set, demonstrating that SdoNet enhances the network’s learning ability and achieves robust and accurate velocity regression, especially in noisy IMU observations. The mean absolute speed regression error of SdoNet is lower than the two types of long short-term memory networks by 52.52% and 71.86%, respectively. Compared to the process using only NHC, the absolute translation error is reduced by approximately 44.00% after employing the pseudo-ODO velocity constraint and further reduced by around 11.31% after employing the noise adapter.

With the development of GPS technology, location-based information services are becoming more and more diverse. Using the trajectory data generated by GPS can analyze and study the various needs of taxi drivers. Big data technology such as interpreting, manipulating data and extracting nugget of information from data is crucial in Intelligent Transportation System (ITS). In order to enhance cruising efficiency of drivers, this paper proposes a Taxi-cruising Recommendation strategy based on Real-time information and Historical Trajectory data (TR-RHT). Primarily, we construct a Passenger-Demand predict model based on Historical Hotspot (PDHH) to predict the passengers’ demand in hotspot area. Then, an Improved Decision Tree (IDT) predicting algorithm is proposed to construct spatio-temporal index to select suitable historical data. Furthermore, we introduce Hotspot Recommendation based on Historical Trajectory (HRHT), wherein it defines cruising event as the process of taxi searching for passengers. The HRHT model performs statistics and analysis on the different states of taxi operation, which can obtain the probability of catching passengers at each hotspot and calculate the travel time between hotspots. The model selects the statistics result of cruising efficiency and driving time between hotspots based on spatio-temporal index, then analyzes the selected result to provide optimal pick-up hotspots for drivers. Experiment results show that TR-RHT can precisely suggest a cruising path to reduce cruising time for drivers.

Due to vehicle mobility, the topology of Vehicle Ad-hoc Networks (VANETs) may change dynamically. High mobility, limited bandwidth, and dynamic network topology pose challenges for communication in the Internet of Vehicles (IoVs). Literature works have attempted to promote efficient (e.g., lower end-to-end latency) message forwarding. However, due to the uncertain direction of message forwarding and vehicle mobility, they suffer from unreachable destinations and unstable connections. This paper explores the efficient method of message forwarding to alleviate network congestion in IoVs. We propose a Spatio-Temporal domain Autonomous Load Balancing (STALB) routing protocol. Specifically, STALB is a trajectory-based method for controlling the direction of message forwarding. STALB can significantly reduce the end-to-end latency and overload ratio, since it considers the local status of network relay devices (i.e., buffer score, congestion status) from the spatio-temporal domain. Then, we present a path reconstruction mechanism, which ensures that messages are forwarded to destinations within limited Time-To-live (TTL). Extensive simulation results show that STALB significantly outperforms other baseline methods (BSaW, TDOR, and TBHGR) regarding overhead ratio, average delivery latency, and average buffer time. Especially, the delivery rate of STALB can reach 99.9% under the sparse network scenario (4,500 messages), at least 0.7% higher than other baseline methods. Similarly, the average delivery delay of STALB is at least 84.31% lower than that of other baseline methods under the dense network scenario (18,000 messages).

We propose a geographic and spatio-temporal in-formation based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and non- line-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.

Due to inadequate management of Vehicular Ad hoc Networks (VANETs), malicious nodes could participate in communications along with misbehavior, e.g., dropping packets and spreading fake information. Therefore, it is essential to detect misbehavior of internal attackers that will cause network performance degradation (e.g., taking longer time to receive messages or reaching destinations with detours). Apart from the capture of dynamic network topology of VANETs, the social relationship among nodes can also be applied as a relatively stable metric to qualify nodes. This paper proposes a misbehavior detection mechanism based on social relationships, from which nodes determine trust for the receiver or transmitter. Based on the proposed mechanism, road traffic control applications can avoid the interference from malicious nodes. The construction of social relationships depends on the geographic information reflected by the movement of nodes, including contact frequency and trajectory similarity, since the geographic information can accurately indicate the relevance among nodes. In addition to the social relationship, the proposed mechanism also evaluates the data trust from time and spatial factors to reduce the interference of fake data. Finally, the proposed mechanism integrates data trust and social relationships to enable misbehavior detection decisions. Extensive results of simulations show that the proposed mechanism has outstanding malicious nodes detection rates under various proportions of malicious nodes and movement patterns.

In recent years, great attention has been paid on Electric Vehicles (EVs) in terms of environmental pollution. Here, EVs can greatly reduce the environmental pollution, compared with traditional Internal Combustion Vehicles (ICVs). However, since EVs cannot be replenished fast like ICVs, the rigid deployment of charging infrastructure and its limited charging capability, leads to service congestion particularly due to a large number of EVs being parked with charging demand. Compared to CSs with rigid extension in location and charging facilitates, the Vehicle-to-Vehicle (V2V) charging service provides a spatial and temporal advance in flexibility, with potential to supplement with G2V charging mode, which can supplement or even replace the G2V Charging Mode. In this paper, we propose a hybrid V2V charging scheme, consisting of direct and asynchronous V2V charging modes, to achieve a great charging flexibility and alleviate the burden for grid load. Here, we estimate the Minimum Waiting Time (MWT) under each mode, as guidance to switch between modes and optimize charging service under each mode. Results show that our proposed hybrid V2V charging scheme outperforms literature works, in terms of average waiting time and number of full charged EVs.

Vehicular Ad Hoc Networks (VANETs) rely heavily on trustworthy message exchanges between vehicles to enhance traffic efficiency and transport safety. Although cryptography-based methods are capable of alleviating threats from unauthenticated attackers, they can not prevent attacks from those legitimate network participants. This paper proposes a trust model to deal with attackers from the latter case, who can tamper with their received messages and deliberately decrease the trust value of benign vehicles. The trust evaluation process is formed by two stages: (i) the local trust evaluation at vehicles and (ii) trust aggregation on Road Side Units (RSUs). In the local trust evaluation stage, vehicles detect attacks and calculate the trust value for others in a distributed manner. Also, the social metrics of vehicles are calculated based on interaction records and trajectories. In the trust aggregation stage, each RSU collects local data from nearby vehicles and derives aggregation weights from the eigenvector centrality of the local trust network and social metrics. Then the RSU broadcasts the aggregated trust value towards vehicles in proximity. These vehicles can thus obtain a more accurate and comprehensive view. Vehicles with trust value below a preset threshold will be considered malicious. Extensive simulations based on the ONE simulator show that the proposed model (TECS) outperforms another benchmark model (IWOT-V) regarding the malicious vehicle detection and the delivery rate of authentic messages.

In recent years, indoor positioning has drawn intensive attention for both pedestrian and mobile robot applications. Among various indoor positioning technologies, visible light positioning has many advantages due to its high localization accuracy, high bandwidth, energy efficiency, long lifetime, and cost efficiency. For postprocessing or semi-real-time applications, researchers often use smoothers to improve location accuracy. However, smoothers are always local estimators and lack integrity when calculating locations. To globally optimize the positioning results and further improve the accuracy, we propose a nonlinear optimization model based on the idea of graph optimization. Innovatively, the model adds the acceleration as a constraint to become one part of the residuals and regularize the trajectory. We design a signal-to-noise ratio-based weighting strategy to suppress the outliers and better assess the errors. Moreover, we design a loop constraint to further improve the positioning accuracy. The experimental results show that our proposed model significantly improves the accuracy by 71%, which is suitable for indoor positioning.

Object detection is becoming increasingly significant for autonomous-driving system. However, poor accuracy or low inference performance limits current object detectors in applying to autonomous driving. In this work, a fast and accurate object detector termed as SA-YOLOv3, is proposed by introducing dilated convolution and self-attention module (SAM) into the architecture of YOLOv3. Furthermore, loss function based on GIoU and focal loss is reconstructed to further optimize detection performance. With an input size of$512\times 512$, our proposed SA-YOLOv3 improves YOLOv3 by 2.58 mAP and 2.63 mAP on KITTI and BDD100K benchmarks, with real-time inference (more than 40 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed, indicating the suitability for autonomous-driving application. To our best knowledge, it is the first method that incorporates YOLOv3 with attention mechanism, and we expect this work would guide for autonomous-driving research in the future.

To realize high quality smart IoT services, such as intelligent video surveillance in Auto Driving and Smart City, tremendous amount of distributed machine learning jobs train unbiased models in wireless edge networks, adopting the parameter server (PS) architecture. Due to the large datasets collected geo-distributedly, the training of unbiased distributed learning (UDL) brings high response latency and bandwidth consumption. In this paper, we propose an online scheduling algorithm, Okita, to minimize both the latency cost and bandwidth cost in UDL. Okita schedules UDL jobs at each time slot to jointly decide the execution time window, the amount of training data, the number and the location of concurrent workers and PSs in each site. To evaluate the practical performance of Okita, we implement a testbed based on Kubernetes. Extensive experiments and simulations show that Okita can reduce up to 60% of total cost, compared with the state-of-the-art schedulers in cloud systems.

In this paper, the implementation of a distributed primal-dual learning algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual learning algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is provided in a closed form while considering the impact of wireless factors such as data transmission errors. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution compared to the distributed primal-dual learning algorithm without considering imperfect wireless transmission.

As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs’ reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs’ reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).

Electric vehicles (EVs) are being introduced by different manufacturers, thanks to their environment-friendly perspective to alleviate CO2pollution. In this paper, the proposed EV charging management scheme enables pre-empted charging service for heterogeneous EVs (depends on different charging capabilities, brands, etc.). Particularly, the anticipated EVs' charging reservations information, including their arrival time and expected charging time at charging stations (CSs), are brought for planning CS-selection (where to charge). Along with applying ubiquitous cellular network communication to deliver (delay tolerant) EVs' charging reservations, we further study the feasibility of applying opportunistic vehicle-to-vehicle (V2V) communication with delay/disruption tolerant networking (DTN) nature, due primarily to its flexibility and cost-efficiency in vehicular ad hoc networks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V-based charging reservation is promisingly cost-efficient in terms of communication overhead, while achieving a comparable charging performance to apply cellular network communication.

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki's traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration).

Multiaccess edge computing (MEC) is a new paradigm to meet the requirements for low latency and high reliability of applications in vehicular networking. More computation-intensive and delay-sensitive applications can be realized through computation offloading of vehicles in vehicular MEC networks. However, the resources of a MEC server are not unlimited. Vehicles need to determine their task offloading strategies in real time under a dynamic-network environment to achieve optimal performance. In this article, we propose a multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources. Moreover, we construct a distributed best response algorithm based on the computation offloading game model to maximize the utility of each vehicle and demonstrate that the strategy in this algorithm can converge to a unique and stable equilibrium under certain conditions. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithms. The fast convergence and the improved performance of this algorithm are verified by numerical results.

This article aims to design an intelligent mobile charging control mechanism for electric vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled toward EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions and charging demand. Furthermore, with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargers selection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger selection, especially for mobile chargers equipped with suffice power capacity. Note to Practitioners-The convenience of charging service is one major concern for EVs, especially when an urgent charging is required while none charging points are reachable. Recently, a Chinese EV company (NIO, Inc., Shanghai, China) is promoting its mobile charger (ES8 model) to Tesla. Driven by such market trend, this article proposes an efficient approach toward intelligent scheduling of mobile chargers toward parked EVs. Different from fixed charging stations focusing on the problem of long waiting times, the proposed solution is applicable to charging-on-demand with precharging appointment at mobile chargers. Preliminary experiments show great charging efficiency achieved by concerning the issue of where to reserve, i.e., the consideration of optimal selection on mobile chargers. Such mobile charging services can coexist with the governmental or pilots' initiated charging station deployment. However, future research will need to evaluate the holistic service platform.

In practice, visible light signals undergo non-line-of-sight (NLOS) propagation, and in visible light-based positioning (VLP) methods, the NLOS links are usually treated as disturbance sources to simplify the associated signal processing. However, the impact of NLOS propagation on VLP performance is not fully understood. In this paper, we aim to reveal the performance limits of VLP systems in an NLOS propagation environment via Fisher information analysis. Firstly, the closed-form Cramer-Rao lower bound (CRLB) on the estimation error of user detector (UD) location and orientation is established to shed light on the NLOS-based VLP performance limits. Secondly, the information contribution from the NLOS channel is quantified to gain insights into the effect of the NLOS propagation on the VLP performance. It is shown that VLP can gain additional UD location information from the NLOS channel via leveraging the NLOS propagation knowledge. In other words, the NLOS channel can be exploited to improve VLP performance in addition to the line-of-sight (LOS) channel. The obtained closed-form VLP performance limits can not only provide theoretical foundations for the VLP algorithm design under NLOS propagation, but also provide a performance benchmark for various VLP algorithms.

The introduction of Electric Vehicles (EVs) leads to new concern on the E-Mobility. Making charging reservation, by considering the EV's arrival time and its expected charging time at Charging Stations (CSs) has been studied to predict the dynamic status of CSs. In this paper, we propose a Mobile Edge computer Geared v2x for E-mobility Ecosystem (MEGEE), as a decentralized alternative to the conventional centralized cloud based architecture. MEGEE enables the Vehicular Delay/Disruption Tolerant Networking (VDTN)-driven anycasting for information delivery, and Mobile Edge Computing (MEC) functioned CSs for information mining and aggregation. MEGEE efficiently and timely processes essential charging reservations and charging control information, through the Internet of EVs and MEC servers. Our studies show that MEGEE can achieve the close charging performance as performed by the centralized system, while offers a significant saving in communications cost.

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Things (IoT) devices will be supported by the massive machine type communication (mMTC). Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have the advantages of improving network capacity, reducing MTC devices' (MTCDs) latency and enhancing quality of service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs' on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading.

Further accelerated by the Internet of Things (IoT) concept, various devices are being continuously introduced into diverse application scenarios. To achieve unattended updates of IoT smart object(s), there remains a challenging problem concerning how to efficiently deliver messages to specific groups of target nodes, especially considering node mobility. In this paper, the relay selection problem is investigated on the basis of directional movement with randomness (e.g., typically associated with the searching or migrating behavior of animals). Unlike numerous works tackling one-to-one communication, we focus on efficient group delivery (one-to-many). A two-level delay budget model is considered to reflect the flexibility of delay tolerance, which brings potential efficiency gains for group delivery compared with using a single budget boundary. Following the description of the system model, a combinatorial bi-objective optimization problem is formulated and solutions are proposed. Simulation results show that the greedy algorithm can achieve comparable performance to an evolutionary algorithm when the delivery satisfaction outweighs efficiency. Furthermore, we show that our proposed greedy scheme can outperform the state-of-the-art when the delivery efficiency becomes increasingly important.

Mobility, node density, and the demand for large volumes of data exchange have aggravated competition for limited resources in the wireless communications environment. This paper proposes a novel MAC scheme called segmentation MAC (SMAC), which can be used in large-scale vehicle-to-everything (V2X) communication networks. SMAC functions to support the dynamical allocation of radio channels. It is compatible with the asynchronous multi-channel MAC sub-layer extension of the IEEE 802.11p standard. A key innovate feature of SMAC is that the segmentation of the network and channel allocations are dynamically adjusted according to the density of vehicles. We also propose a novel efficient forwarding mechanism to ensure inter-segment connectivity. To evaluate the performance of inter-segment connectivity, a rigorous analytical model is proposed to measure the multi-hop dissemination latency. The proposal is evaluated in network simulator NS2 as well as the standard IEEE 1609.4 and two asynchronous multi-channel MAC benchmarks. Both analytical and simulation results demonstrate better effectiveness of the proposed scheme compared with the existing similar schemes in the literature.

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11- based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.

With the growing popularization of Electric Vehicles (EVs), charging management has become an increasingly important research problem in smart cities. Different from plug-in charging technology, we alternatively enable the battery switch technology to provide fast EV charging (reduce the service waiting time from tens of minutes to a few minutes), by facilitating the switchable (fully-recharged) batteries maintained at CSs and also the batteries cycling procure to refresh their availability. Nevertheless, potential hot spot may still happen at CSs, due to running out of switchable batteries as well as long batteries charging queue. With this concern, we next propose a reservation based EV charging management scheme to alleviate such situation, considering EVs' anticipated charging reservations (including arrival time, expected charging time) to coordinate EVs' charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our enabling technology, in terms of minimized waiting time for the battery switch as the benefit of EV drivers, and higher number of batteries switched as the benefit of CSs.

In this paper, we investigate the profit maximization problem for the mobile virtual network operator in cognitive radio networks considering the uncertain property of users' spectrum demand. In order to achieve more revenues while simultaneously satisfying the needs of users, the cognitive mobile virtual network operator chooses to dynamically sense the idle spectrum in the licensed band which is more economic, and at the same time leases the spectrum from the spectrum owner which guarantees more stable spectrum resources. However, the fluctuant spectrum demand of users imposes unprecedented challenges on the decision making process. To deal with the uncertain features of the users' demand, a flexible distribution uncertainty model is developed. Particularly, a reference distribution is introduced based on historical data and then a uncertainty set is defined to confine the spectrum demand. The uncertainty model developed allows the actual users' spectrum requirement to fluctuate around the reference distribution. Chance constraint approximations and robust optimization approaches are developed to transform and then solve the optimization problem. Simulation results based on the real-world traces evaluate the performance of the proposed scheme and investigate the parameter impacts on the system utilities. Our research may also help shed some insights on the investment policy making for the mobile virtual network operator.

Continuous and effective developments in Autonomous Vehicles (AVs) are happening on daily basis. Industries nowadays, are interested in introducing less costly and highly controllable AVs to public. Current so-called AVP solutions are still limited to a very short range (e.g., even only work at the entrance of car parks). This paper proposes a parking scheduling scheme for long-range AVP (LAVP) case, by considering mobility of Autonomous Vehicles (AVs), fuel consumption and journey time. In LAVP, Car Parks (CPs) are used to accommodate increasing numbers of AVs, and placed outside city center, in order to avoid traffic congestions and ensure road safety in public places. Furthermore, with positioning of reference points to guide user-centric long-term driving and drop-off/pick-up passengers, simulation results under the Helsinki city scenario shows the benefits of LAVP. The advantage of LAVP system is also reflected through both analysis and simulation.

As a cooperative information system, vehicles in Vehicular Sensor Networks deliver messages based on collaboration. Due to the high speed of vehicles, the topology of the network is highly dynamic, and the network may be disconnected frequently. So how to transfer large files in such network is worth considering. The encountering nodes which never meet before flood messages blindly cause tremendous network overhead. We address this challenge by introducing the Encounter Utility Rank Router (EURR) based on social metrics. EURR includes three cases: Utility Replication Strategy, Lifetime Replication Strategy and SocialRank Replication Strategy. The Lifetime Replication is promising and complements Utility Replication. It enhances the delivery ratio by relaying the copy via the remaining lifetime. Considering the network overhead, the SocialRank Replication replicates a copy according to the SocialRank when two communicating nodes have not met before. The routing mechanism explores the utility of history encounter information and social opportunistic forwarding. The results under the scenario show an advantage of the proposed EURR over the compared algorithms in terms of delivery ratio, average delivery latency and overhead ratio.

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

As a promising use-case of the Internet of Things (IoT), wildlife tracking and monitoring applications greatly benefit the ecology-related research both commercially and scientifically. In literature, a forward-wait-deliver strategy has been researched to facilitate energy-efficient dissemination of delay-tolerant information, which penitentially contributes to long-term tracking and monitoring. However, this strategy is not directly applicable for wildlife tracking and monitoring applications, as the movement trajectory of animals cannot be precisely predicted for relay selection. To this end, further studies are required to utilize partially predictable mobility based on more generalized navigational information such as the movement direction. In this paper, the feasible exploitation of directional movement in path-unconstrained mobility is investigated for strategic forwarding. Our proposal is an advance to the state-of-the-art because the directional correlation of destination movement is considered to dynamically exploit the node mobility for the optimal selection of a stationary relay. Simulation results show that higher delivery utility can be achieved by the proposed fuzzy path model compared with a forwarding scheme without contact prediction or one based on linear trajectory model.

In the past decade, technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power consuming receivers and sensors for smart devices (e.g., GPS, WiFi, MEMS sensors, RFID, UWB, BLE, etc.). This led to the fast development of positioning, navigation, and location-based services (PNLBS), and much broader new applications than just providing a location or navigation.

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).

With continually increased attention on electric vehicles (EVs) due to environment impact, public charging stations (CSs) for EVs will become common. However, due to the limited electricity of battery, EV drivers may experience discomfort for long charging waiting time during their journeys. This often happens when a large number of (on-the-move) EVs are planning to charge at the same CS, but it has been heavily overloaded. With this concern, in an EV charging management system, we focus on CS-selection decision making and propose a scheme to manage EVs' charging plans, to minimize drivers' trip duration through intermediate charging at CSs. The proposed scheme jointly considers EVs' anticipated charging reservations (including arrival time and expected charging time) and parking duration at CSs. Furthermore, by tackling mobility uncertainty that EVs may not reach their planned CSs on time (due to traffic jams on the road), a periodical reservation updating mechanism is designed to adjust their charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our proposal, in terms of minimized drivers' trip duration, as well as charging performance at the EV and CS sides.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages. In such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies' moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define uncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal-correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids.

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver's experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs' charging plans, aiming to minimize drivers' trip duration through intermediate charging at CSs. The anticipated EVs' charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs' charging reservations, we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration.

In order to resolve the tension between continuously growing mobile users' demands on content access and the scarcity of the bandwidth capacity over backhaul links, we propose in this paper a fully distributed ICN-based caching scheme for content objects in Radio Access Network (RAN) at eNodeBs. Such caching scheme operates in a cooperative way within neighbourhoods, aiming to reduce cache redundancy so as to improve the diversity of content distribution. The caching decision logic at individual eNodeBs allows for adaptive caching, by taking into account dynamic context information, such as content popularity and availability. The efficiency of the proposed distributed caching scheme is evaluated via extensive simulations, which show great performance gains, in terms of a substantial reduction of backhaul content traffic as well as great improvement on the diversity of content distribution, etc.

As modern vehicle and communication technologies advanced apace, people begin to believe that the Intelligent Transportation System (ITS) would be achievable in one decade. ITS introduces information technology to the transportation infrastructures and aims to improve road safety and traffic efficiency. However, security is still a main concern in vehicular communication systems (VCSs). This can be addressed through secured group broadcast. Therefore, secure key management schemes are considered as a critical technique for network security. In this paper, we propose a framework for providing secure key management within the heterogeneous network. The security managers (SMs) play a key role in the framework by capturing the vehicle departure information, encapsulating block to transport keys and then executing rekeying to vehicles within the same security domain. The first part of this framework is a novel network topology based on a decentralized blockchain structure. The blockchain concept is proposed to simplify the distributed key management in heterogeneous VCS domains. The second part of the framework uses the dynamic transaction collection period to further reduce the key transfer time during vehicles handover. Extensive simulations and analysis show the effectiveness and efficiency of the proposed framework, in which the blockchain structure performs better in term of key transfer time than the structure with a central manager, while the dynamic scheme allows SMs to flexibly fit various traffic levels.

Due to sparse network density, geographic routing in Intermittently Connected Networks (ICNs) suffers from challenges for making the feasible routing decision and handling the local maximum problem, which is in contrast with the conventional geographic approaches in Mobile Ad hoc NETworks (MANETs) relying on high network density. In this paper, we firstly explore the Delegation Replication (DR) approach to overcome the limitation of the geometric metric adopted in our proposed Come Phase to promote message replication coming towards destination, requiring pairwise encountered nodes moving towards destination. Regarding the proposed Leave Phase, using DR also enhances to prevent message replication leaving away from destination. In addition, we handle the local maximum problem addressing the mobility of mobile nodes and message lifetime. Considering the temporarily stationary movement via Stop Phase, evaluation results show the advantage of the proposed Come-Stop-Leave (CSL) in terms of delivery ratio, average delivery latency as well as overhead ratio.

Receiving great interest from the research community, Delay Tolerant Networks (DTNs) are a type of Next Generation Networks (NGNs) proposed to bridge communication in challenged environments. In this paper, the message replication probability is proportionally sprayed for efficient routing mainly under sparse scenario. This methodology is different from the spray based algorithms using message copy tickets to control replication. Our heuristic algorithm aims to overcome the scalability of the spray based algorithms, since to determine the initial value of the copy tickets requires the assumption that either the number of nodes is known in advance, or the underlying mobility model follows the Random WayPoint (RWP) characteristic. Specifically, in combining with the assistance of geographic information to estimate the movement range of destination, the routing decision is based on the encounter angle between pairwise nodes, and is dynamically switched between the designed two routing phases, named as geographic replication and replication probability spray. Furthermore, messages are under prioritized transmission with the consideration of redundancy pruning. Simulation results show our heuristic algorithm outperforms other well known algorithms in terms of delivery ratio, transmission overhead, average latency as well as buffer occupancy time.

The concept of Delay Tolerant Networks (DTNs) are proposed to facilitate communication in challenged mobile wireless networks using the Store-Carry-Forward (SCF) routing behavior. In this paper, our motivation is to take advantage of geographic routing since it routes message without the knowledge about network topology by using realtime location information, overcoming the challenge of large network topology variation in DTNs. Different from traditional geographic algorithms, our approach only adopts history geographic information due to the difficulty to obtain the realtime location of destination, suffering from sparse network density and high mobility. The key insight of our algorithm is to separate message replication depending on the proximity to the movement range estimated for destination, followed by the proposed scheduling methodology for prioritized transmission between each phase as well as anti-diffusion function for redundancy reduction. Simulation results under the Helsinki city scenario show an improvement comparing with two well known geographic approaches in DTNs, considering delivery ratio, average latency as well as overhead ratio.

Without the assumption of contemporaneous end to end connectivity in challenged wireless networks, Delay Tolerant Networking (DTN) routing is an important research area. The contribution in this paper is to take advantage of the proposed DTN geographic replication to overcome the limitation of topology based utility replication, since message replication is prevented due to the local maximum problem that the utility metric of encountered node is worse than message carrier. In brief, the proposed DTN geographic replication is activated only if the utility replication is unable to route message, this hybrid approach promotes a seamless message replication given limited message lifetime. Borrowing from the concept of gravity, messages are under prioritized transmission for load balancing and achieving less delivery latency. Extensive simulation results show promising improvement of the proposed algorithm in terms of delivery ratio, transmission cost, average latency as well as number of aborted messages.

The concept of Delay Tolerant Networks (DTNs) has been utilized for wireless sensor networks, mobile ad hoc networks, interplanetary networks, pocket switched networks and suburb networks for developing region. Because of these application prospects, DTNs have received attention from academic community. Whereas only a few state of the art routing algorithms in DTNs address the problem of aborted messages due to the insufficient encounter duration. In order to reduce these aborted messages, we propose a routing framework which consists of two optional routing functions. Specifically, only one of them is activated according to the encounter angle between pairwise nodes. Besides, the copies of the undelivered message carried by most of the nodes in the network are more likely to be cleared out after successful transfer, which reduces the number of unnecessary transmissions for message delivery. By means of the priority for message transmission and deletion in case of the limited network resource, the proposed algorithm achieves the high delivery ratio with low overhead as well as less number of aborted messages due to the insufficient encounter duration, thus is more energy efficient.

The framework of Delay Tolerant Networks (DTNs) has recently received an extensive attention and widely implemented, ranging from Wireless Sensor Networks (WSNs) to Interplanetary Networks. It has been applied in military communication, scientific research and exploration. Due to the characteristic of long delay, intermittent disruption, limitation of buffer space and energy, the traditional routing algorithms in the Internet do not perform well in DTNs. Since most of the existing DTN routing algorithms are based on the replication mechanism to achieve the high delivery ratio. In this paper, we propose an asymmetric spray algorithm based on the limited number of replication with the consideration of utility metric. In addition, we also design an adaptive replication function to optimize the dropped messages due to the insufficient encounter duration. Simulation results show the effectiveness of our asymmetric spray approach. In combination with the other designed functions, our proposed algorithm achieve a better performance than the state of the art algorithms.

Opportunistic Networks (ONs) utilize the communication opportunity with a hop-by-hop behavior, and implement communication between encountered nodes based on the Store-and-Forward routing pattern. This approach, which is totally different from the traditional communication model, has received extensive interests from academic community. We consider the ONs are a type of Delay Tolerant Networks (DTNs) since their routing behavior are quite same regardless of the bundle layer protocol. Until currently, a set of congestion control mechanisms have been proposed in Deterministic DTNs, which is mainly implemented in the network with limited mobility or the static network with scheduled disruption interval. However, regarding the networks with large topology variation, known as Opportunistic DTNs, to design a congestion control mechanism is difficult. In this paper, we propose an active congestion control based routing algorithm that pushes the selected message before the congestion happens. In order to predict the future congestion situation, a corresponding estimation function is designed and our proposed algorithm works based on two asynchronous routing functions, which are scheduled according to the decision of estimation function. Simulation results show our proposed algorithm efficiently utilizes the distributed storage to achieve a quite low overhead ratio and also performs well in the realistic scenario.