Abstract In recent years, cloud-edge-end collaborative federated learning frameworks have been widely used in many scenarios and achieved good results. However, with the complexity of application requirements, the problems of device heterogeneity and data heterogeneity become more prominent. Traditional frameworks often face challenges such as uneven allocation of computational resources and inefficient training when dealing with these problems. To address this problem, this paper proposes a novel federated learning framework for multi-domain collaborative analysis of networked encrypted flows. First, we split the model training tasks, intelligently assign part of the model training tasks to terminal devices based on their performance, while the remaining model training tasks that require more computational resources are handed over to edge servers. Second, we introduce a resource scheduling scheme among edge servers to reasonably allocate model training tasks and fully utilize resources. Finally, high quality global models are obtained through a weight-enabled global model aggregation scheme. Experiments show that our proposed scheme can effectively address the impact of device heterogeneity and data heterogeneity in encrypted traffic identification in cross-domain networks, and improve the training efficiency and model performance of the overall system while ensuring data privacy and security.

The deployment of Unmanned Aerial Vehicles (UAVs) has increasingly demonstrated potential in addressing the overload of Base Stations (BSs) by enabling efficient data dissemination during peak traffic hours. However, the operational endurance of UAVs in such scenarios is constrained by their limited onboard energy, making efficient data dissemination strategies essential to extend UAV service duration. To address this challenge, this study proposes a UAV-assisted vehicular edge computing data dissemination framework in the software-defined Internet of Vehicles, leveraging centralized control and UAV-enabled cooperative services to improve dissemination efficiency. Specifically, we formulate a UAV-Assisted Cooperative Scheduling (UACS) problem that synergizes network coding, vehicular caching, and UAV caching to minimize service delay and extend UAV endurance. Furthermore, a graph-based model is introduced to capture the interactions among vehicles, UAVs, and the BS. The NP-hardness of the UACS problem is established through a polynomial-time reduction from the minimum clique cover problem. In addition, a Clique Search-based Cooperative Scheduling (CSCS) algorithm is developed to determine optimal coding-based broadcasting and cooperative operation strategies, and its computational complexity is analyzed to confirm its practical applicability. Extensive simulations using real-world traffic datasets further demonstrate the superiority and effectiveness of the proposed solution.

Vehicular Edge Computing (VEC) is widely regarded as a promising paradigm for enhancing the Quality of Service in the Internet of Vehicles (IoV). However, due to limited computing resources and increasing computational demands, especially during peak traffic hours in urban environments, VEC faces significant challenges. Meanwhile, the idle computing resources of nearby Parked Vehicles (PVs) remain largely underutilized. To address this issue, we propose a PV-enhanced IoV framework that models hierarchical interactions among multiple Task Vehicles (TVs), Service Providers (SPs), and PVs in task offloading and resource allocation, efficiently leveraging the computing resources of PVs to complement VEC servers. Specifically, we first design a Game-based Pre-offloading Assignment (GPA) algorithm to determine the optimal SP selection for each TV. Once TVs have selected their respective SPs, we model the interaction between each TV and SP as a Stackelberg game, where the SP acts as the leader by setting the service price, and each TV responds by determining its offloading workload. Furthermore, we prove the existence of a Nash equilibrium through backward induction and develop a Gradient-based Stackelberg Pricing (GSP) algorithm to compute this equilibrium. Finally, to emphasize the role of PVs, we propose an Optimized Recruitment Allocation (ORA) algorithm, which enables SPs to efficiently recruit PVs and allocate their computing resources, while ensuring that each PV selects the SP that maximizes its utility. Extensive simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of offloading performance and overall utility improvement.

The integration of Large Language Models (LLMs) into the Internet of Vehicles (IoV), particularly within vehicular edge computing (VEC), has enabled advanced context-aware inference and decision-making capabilities of intelligent transportation systems. Nevertheless, the substantial computational demands of LLMs pose significant challenges for delay-sensitive, resource-constrained vehicle-embedded systems. In contrast, Small language models (SLMs) enable lightweight inference with low delay but suffer from limited inference accuracy. Effectively orchestrating heterogeneous intelligence across vehicle-embedded systems and edge servers requires a careful trade-off between inference delay and accuracy. This paper proposes a collaborative LLM–SLM inference framework within the VEC paradigm, which supports dynamic task offloading to achieve an optimal trade-off between delay and accuracy. We formulate a multi-objective optimization problem that jointly minimizes the weighted inference delay and maximizes the expected inference accuracy by dynamically determining task offloading and resource allocation. To address this problem, we propose the Chaos and Simulated Annealing-embedded Particle Swarm Optimization (CSAPSO) algorithm within a Multi-Objective Particle Swarm Optimization (MOPSO) framework. The proposed algorithm incorporates chaotic mapping for population initialization to enhance diversity, a fitness evaluation mechanism, particle update rules, a particle repair strategy, and a simulated annealing-based local search operator to improve solution quality. Complexity analysis verifies the algorithm’s practical feasibility. Extensive simulation results demonstrate that the proposed scheme consistently outperforms benchmark methods in achieving a favorable trade-off between delay and accuracy.

Federated Learning (FL), as a distributed training framework, has demonstrated significant potential in preserving data privacy. However, client heterogeneity in data volume, computational resources, and communication conditions causes two critical issues: inefficient global synchronization due to stragglers, and excessive communication overhead from repeated model transmissions. To address these challenges, this paper proposes an Adaptive Federated Distillation (AFD) framework, which enhances training efficiency by dynamic local training optimization and balanced communication load. AFD dynamically adjusts the number of local training epochs based on client-specific data volume, communication conditions, and historical training time. Simultaneously, it employs federated distillation to transmit lightweight logits instead of full model parameters, thereby reducing communication costs and enabling model heterogeneity. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that, compared to FedAvg, AFD reduces communication costs by 3-4 orders of magnitude when CNN or ResNet models are used. Additionally, AFD reduces client energy variance by 96.5% and training time disparity by 88.7% while maintaining model accuracy. Crucially, its model-agnostic design supports heterogeneous architectures, ensuring fairness across diverse edge devices.

Vehicular Edge Computing (VEC) has emerged as a promising paradigm to enhance the Quality of Service (QoS) in the Internet of Vehicles (IoV). However, the limited computing capabilities of Task Vehicles (TVs), coupled with increasing computational demands during peak periods, pose significant challenges. Meanwhile, the unused computing resources of nearby Parked Vehicles (PVs) are often underutilized. To address these challenges, we propose a parked vehicle-assisted VEC architecture that integrates multiple TVs, Service Providers (SPs), and PVs. Specifically, we first design a Game-based Preoffloading Assignment (GPA) algorithm to determine the optimal SP for each TV. After SP selection, the interaction between each TV and its selected SP is modeled as a Stackelberg game, where the SP acts as the leader by setting the pricing strategy, and the TV acts as the follower by determining its offloading strategy. Moreover, we demonstrate the existence of a unique Nash equilibrium and propose a Gradient-based Stackelberg Pricing (GSP) algorithm to derive the equilibrium strategies. Additionally, to enhance the utilization of PV resources, we introduce an Optimized Recruitment Allocation (ORA) algorithm, enabling each PV to select the SP that maximizes its utility. Finally, extensive simulations demonstrate that the proposed scheme significantly improves offloading efficiency.

Abstract With the development of electric vehicles, charging stations have garnered significant attention and progress. As a result, various charging platforms have emerged. However, due to the lack of shared charging information, issues such as low utilization of charging stations during peak hours and insufficient station availability have negatively impacted user experience. Inspired by the concept of an aggregation platform, we propose an aggregated charging platform for electric vehicles (EVs). However, challenges related to trust, such as single-point failures and the difficulty of achieving unified identity management, hinder the implementation of the aggregation model. To address these issues, we propose a blockchain-based EV aggregation charging platform model and a cross-platform identity authentication scheme. Leveraging the decentralized and highly secure characteristics of blockchain, we construct a decentralized aggregation platform and enhance the credibility and reliability of cross-platform authentication through smart contracts. Furthermore, we design a batch authentication scheme to efficiently handle a large number of authentication requests. Simulation results demonstrate that, compared to other schemes, our proposed approach improves authentication efficiency by 50–66.6%.

In the Industrial Internet of Things (IIoT), the identification resolution system enhances communication and overall efficiency between isolated work islands, ensuring the trustworthiness and effectiveness of secure resource sharing across domains through cross-domain authentication. However, traditional identity authentication methods fail to empower users with control over their identity information and face challenges such as difficulty in tracking anonymous users, high computational overhead, and insufficient cross-domain trust. To address these issues, this paper proposes a cross-domain identity authentication scheme based on self-sovereign identity. The scheme leverages aggregate signature technology to enhance authentication efficiency, integrates blockchain technology and smart contracts to achieve cross-domain trust, and designs a mechanism for threshold identity tracking and revocation, as well as an attribute credential update mechanism to enable secure and efficient cross-domain authentication in the identification resolution system. The paper provides formal security definitions and proofs and evaluates the computational and storage efficiency of the scheme through theoretical analysis and experimental simulations. The results demonstrate that the proposed scheme offers significant advantages in resource-constrained scenarios within the IIoT.

Integrating Unmanned Aerial Vehicles (UAVs) into Vehicular Edge Computing (VEC) establishes UAV-enabled VEC, effectively addressing potential degradation of offloading per-formance caused by overloaded edge servers in urban aggregation areas. However, the limited dimensions of UAV s impose constraints on their onboard energy and computational capa-bilities, thereby presenting significant challenges in achieving efficient task offloading. Motivated by this, this work studies task offloading and resource allocation in UAV-enabled VEC for enhancing the offloading performance. Specifically, we propose a task offloading framework in UAV-enabled VEC that per-forms task offloading for vehicles. Based on this framework, we formulate a novel problem called Task Offloading and Resource Allocation (TORA), aiming to minimize the weighted sum of completion time and energy consumption for processing computational tasks. Furthermore, due to the non-convex nature of the TORA problem, we propose a genetic algorithm-based joint task offloading and resource allocation scheme to effectively solve the TORA problem. This scheme comprises a chromosome representation for encoding solutions and a set of reproduction operators (i.e., selection, crossover, and mutation) for solution evolution. Finally, the proposed scheme is evaluated through a constructed simulation model, and the experimental results substantiate its effectiveness.

To solve the problem of climate warming, countries around the world have paid special attention to the construction of carbon governance. Carbon emission accounting is an important policy tool to control the vented CO2. But at present, there are third-party agencies in carbon emission accounting that cannot ensure the fairness and impartiality of accounting, and there may be risks such as illegal use and leakage of sensitive information in the process of carbon emission data transmission. Therefore, We design the blockchain-based carbon emission security accounting scheme (BCESAS) and propose cross-chain verification contract to ensure the efficiency of cross-chain information accounting. In addition, bilinear pairing is used to ensure data integrity, and we encrypt private data using an improved and more secure homomorphic encryption algorithm to ensure that privacy is not leaked during the transfer of carbon emission data, which is more efficent than other homomorphic encryption algorithms. We also use reputation mechanism to regulate the behavior of carbon emission auditors. The theoretical and experimental analysis demonstrates that BCESAS can verify the integrity, correctness and privacy of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization, decentralization, democratization, and digitalization. In this evolving context, energy blockchain, aiming to enhance efficiency, transparency, and security, emerges as an integrated technological solution designed to address the diverse challenges in this field. Data security is essential for the reliable and efficient functioning of energy blockchain. The pressing need to address challenges related to secure data storage, effective data management, and efficient data utilization is increasingly vital. This paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years, adopting an all-encompassing perspective that spans data storage, management, and utilization. Our work systematically evaluates and contrasts the strengths and weaknesses of various research methodologies. Additionally, this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture, specifically designed to meet the complex security requirements of multi-blockchain business environments. Concludingly, this paper identifies key directions for future research, particularly in advancing the integration of storage, management, and utilization of energy blockchain data security.

To solve the current situation of low vehicle-to-pile ratio, charging pile(CP) operators incorporate private CPs into the shared charging system. However, the introduction of private CP has brought about the problem of poor service quality. Reputation is a common service evaluation scheme, in which the third-party reputation scheme has the issue of single point of failure; although the blockchain-based reputation scheme solves the single point of failure issue, it also brings the challenges of storage and query efficiency. It is a feasible solution to classify and store information on multiple chains, and at this time, reputation needs to be calculated in a cross-chain mode. Crosschain reputation calculation faces the problems of correctness verification, integrity verification and efficiency. Therefore, this paper proposes a verifiable and efficient cross-chain calculation model for CP reputation. Specially, in this model, we propose a verifiable cross-chain contract calculation scheme that adopts polynomial commitment to solve the problems of polynomial damage and tampering that may be encountered in the crosschain process of outsourced polynomials, so as to ensure the integrity and correctness of polynomial calculations. In addition, the miner selection and incentive mechanism algorithm in this scheme ensures the correctness of extracted information when the outsourced polynomial is calculated on the blockchain. The security analysis and experimental results demonstrate that this scheme is feasible in practice.

With emerging technology, the blockchain-based Vehicle Ad-hoc Network (VANET) can alleviate traffic congestion and optimize resource management to improve the transportation system's efficiency significantly. However, since the information transmitted in VANET is distributed in an open environment, security, and privacy are now critical issues. Recently, many authentication protocols based on Tamper-Proof Devices (TPD) have been proposed to solve the above-mentioned hindrances, and most of them rely on the ideal TPD with extreme security assumptions. Moreover, when the aggregate signature verification fails, these schemes can only completely discard the aggregate signature, which is impractical for VANET. In order to solve the above problems, we propose a more realistic TPD-based identity verification scheme with a privacy protection function for the blockchain-based VANET. Specifically, it uses an offline self-update method to periodically update the data in the TPD to resist side-channel attacks. Our performance analysis shows that the proposed scheme is superior to the existing schemes regarding security and average delay, so it is more suitable for the actual blockchain-based VANET environment.

With the digital transformation of the energy industry, energy blockchain is playing an important role in application areas, such as energy data sharing and distributed power trading. In this process, the use of energy data is a top priority. Federated learning (FL) can enable the analysis and computation of energy data while protecting their privacy. However, traditional FL relies on a central server and parties involved are not fully trusted. In energy blockchain environment, FL also faces data poisoning attacks launched by energy departments, besides, the supervisory committee carrying out checking models can launch deception attacks. Therefore, we propose a game theory-based incentive mechanism for collaborative security of FL in energy blockchain environment, which can discourage nodes from taking malicious behaviors in iterative training of FL. First, we propose an FL model in energy blockchain environment, which can protect privacy and achieve collaborative security. Considering that game theory can be used to analyze the strategies of participants, we build a game model with energy departments and supervisory committee as players and design our incentive mechanism based on game theory, which is implemented by smart contracts. Even if the accuracy of model checking algorithm is low, malicious behaviors in FL can be reduced by using our incentive mechanism. In particular, we prove that our mechanism can lead game model to a Nash equilibrium (NE) that achieve collaborative security. Security analysis and experimental evaluation show that our incentive mechanism is feasible in energy blockchain with robustness, reliability, and low complexity.

As a key technology for the development of 5G networks, network slicing is developing rapidly. Although network slicing can realize the flexible division of 5G network resources and quickly customize virtual networks that meet the differentiated needs of customers, it is still difficult to determine the optimal service quality parameters in application scenarios. To solve the problem, this article designs a multichain 5G network slicing service quality computing model to calculate the service quality parameters of the network slicing. The calculated service quality parameters can be used as an adjustment basis in the negotiation of the SLA between the customer and the network operator. However, the traditional method of calculating information across chains will cause frequent information interactions and affect efficiency. Therefore, in this scheme, we deploy a smart contract on each blockchain to calculate the information, which can reduce the frequency of information transmission and improve efficiency. In addition, in order to make the calculation between smart contracts more fluent and the requirements more relevant, this article proposes to coordinate the development of smart contracts through multiple blockchains. Besides, to ensure the cross-chain security calculation, the signature by Cosi protocol and multisigncryption algorithms are used in the transmission of nonprivate information and private information in the cross-chain process, respectively. Security analysis and experimental results prove that the multichain 5G network slicing service quality computing model is feasible and efficient in practice.

With the volume of data increasing in the Internet of Things, a new business mode, where data owners share their own data to others for rewards, has emerged. Therefore, how to motivate data owners to participate in the data trading process is the main challenge. So far, lots of works focus on the motivation mechanism designing and ensure a fair distribution of profits among data owners. However, some security and privacy issues are still not well solved and the data owners are still unwilling to participate in the process. Especially, when a data provider claims rewards with its real identity for the shared data, the linkage between its real identity and the shared data will expose the participator's private information included in the shared data, such as location information. To protect user's privacy in the scenario, a privacy and efficiency-oriented data sharing mechanism for IoTs is proposed in this paper. We first propose a blockchain-based data sharing framework in which the behavior of all participants will be supervised. Then, in order to hide the real identities of data providers during the data sharing process, an anonymous certificate-based data sharing policy is proposed. At last, two novel non-interactive zero-knowledge proofs are designed to hide the identities of qualified data providers while claiming rewards to the system. Through security analysis and performance evaluation, the feasibility and effectiveness of the data sharing scheme are illustrated.

With the development of the Industrial Internet of Things, industrial control systems have become more open and intelligent. However, large numbers of unknown protocols exist in IIoT, threatening the security of IIoT devices and systems. Protocol reverse engineering extracts the grammar and semantics of the protocol by monitoring and analyzing the traffic trace or the execution process of instructions, without the need for protocol description. As the executable programs are mainly integrated into the IIoT devices and the communication traffic is relatively sparse, the traditional protocol analyzing method is not suitable for the IIoT environment. This paper proposes an improved sparse protocol parsing method of IIoT protocol based on the BPSO-vote-HMM hybrid model. The binary particle swarm optimization algorithm is introduced to expand the captured IIoT protocol message sequence, solving the problems of sparse samples in IIoT and the low efficiency of the GA-based data expansion model. Besides, we improve on the parameter training part to improve the efficiency and get better model parameters by dividing the training set into several sub-sets, conducting the parameter update parallel, and inputting the results into a voter to generate the final parameter of HMM, which is used in protocol field prediction. Finally, by combining the BPSO-based data expansion model and the protocol field parsing model based on vote-HMM, a hybrid analytical model is constructed to improve the analytical accuracy in a gradual evolutionary manner. Through a series of comparative experiments, the improved protocol field parsing model has better performance on IIoT protocol.

With the development of electric vehicles, the shortage of charging piles (CPs) has gradually been exposed. In response to this situation, CP operators have taken private CPs into the shared charging system. Due to the lack of maintenance personnel for private CPs that join shared charging, users often face the problems of damaged CPs and poor service attitudes of CP owners. Reputation solutions based on third-party platforms face a problem of single-point failures and reputation solutions based on blockchain face problems of storage and query efficiency. To improve storage and query efficiency, this article proposes a multichain charging model that stores different types of information on different blockchains. However, it faces the problem of unreliable information called across chains, when calculating reputation across chains. Therefore, this article proposes a cross-chain trusted smart contract ($C_{2}T$smart contract) to ensure the authenticity, real-time, and interchain write mutual exclusion of cross-chain information, making reputation calculation in the multichain charging model more convenient and more accurate. Especially, we propose a data mutual trust mechanism based on Merkle proof to ensure the authenticity of cross-chain information and prevent forged information from participating in calculating reputation. Furthermore, we present a data structure composed of multiple counting Bloom filters (MCBFs) to verify the real time of information and filter out non-real-time information, thereby ensuring the real time of the calculated reputation. In addition, we put forward an algorithm to guarantee the interchain write mutual exclusion by hash mutexes, making the reputation calculation process more accurate and complete. The security analysis and experimental results demonstrate that$C_{2}T$smart contract is feasible in practice.

Nowadays, the quality of cloud services offered by different service providers varies greatly. Reputation mechanism, as a better service evaluation method, can help standardize and regulate the cloud service market. However, existing reputation systems either rely on a trusted third party with security and privacy issues, or lack reliable evaluation. To address the above issues, we propose a blockchain based privacy-preserving dynamic reputation mechanism for cloud service and a reputation management smart contract (RM) is designed to implement trusted reputation computation. The reputation integrates conformance trust in the subjective view and recommendation trust in the objective view together to provide a comprehensive evaluation. Besides, a miner selection algorithm is designed to prevent miners from launching a collusion attack. Moreover, the Paillier homomorphic encryption algorithm (PHE) is introduced to encrypt the sensitive data of customers, ensuring the security of data stored on the blockchain. Experiment results reveal that the proposed model is feasible and the performance of encryption is acceptable.

With the development of the Energy Internet and the support of the subsidy policies of various countries, Electric Vehicles(EVs) have ushered in a golden development period. However, the development of EVs needs to solve the problems of insufficient charging piles(CPs) and difficulty in finding CPs. In order to solve the problem of difficult charging of EVs, the concept of shared charging came into being, in which idle CPs or private CPs are shared to meet the charging needs of more people and improve the utilization rate of CPs. However, the shared charging scheme implemented by third-party platforms faces the issue of trust lacking. This paper proposes a blockchain architecture for shared charging, which can use the blockchain to build a trust environment involving private pile owners, charging pile(CP) operators, Electric Vehicle(EV) users, etc.. The blockchain architecture also contains the block structure where pointer was added for quick search, contract content that can automatically execute multi-party contracts to achieve secure computing and reputation-based incentive mechanism to provide high-quality charging services in detail. This architecture establishes the multi-party trust environment for shared charging from three aspects: secure storage, secure computing, and secure incentives.

This paper makes the first effort on proposing a fog computing empowered architecture together with a dedicated scheduling algorithm for data dissemination in software defined heterogeneous vehicular ad-hoc networks (VANETs). Specifically, the architecture supports both the logically centralized control via the cloud node in the core network and the distributed data dissemination via the fog nodes at the network edge. A problem calledfog assisted cooperative service(FACS) is formulated, which takes network coding and vehicular caching into consideration, and aims at minimizing the overall service delay via the cooperation of vehicle-to-cloud (V2C), vehicle-to-fog (V2F) and vehicle-to-vehicle (V2V) communications. Further, we derive an equivalence problem of FACS and prove that FACS is NP-hard. On this basis, we propose a Clique Searching based Scheduling (CSS) algorithm at the SDN controller, which considers the heterogeneous communication interfaces and vehicle mobility in scheduling, and enables the collaborative data encoding and transmission among the cloud, fog nodes and vehicles. The complexity analysis demonstrates the feasibility of the proposed algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation based on real vehicular trajectories extracted from different time and space. The simulation results conclusively demonstrate the superiority of the proposed solution.

As the intelligentization of Industrial Internet of Things (IIoT) broke the relatively closed and credible industrial environment, IIoT faces increasingly serious security problems. The commonly used vulnerability discovery method is protocol reverse engineering. However, it is difficult to analyze IIoT protocols with existing protocol reverse engineering approaches, as they influence the normal operation or have spare sample data. In this paper, a sparse protocol parsing method for IIoT protocols is proposed. The parsing method expands the samples of the captured IIoT protocol message sequences using a genetic algorithm (GA), which designs its fitness function based on the protocol response data to select high-quality samples. By combining the GA with the hidden Markov model (HMM) with lower algorithm complexity, a hybrid parsing model is constructed to improve accuracy in a gradual evolution way. Through comparison experiments on various IIoT protocols, our HMM hybrid model has better performance than RNN hybrid models under sparse samples.

Renewable energy powered road side units(RSUs) in Internet of Vehicles(IoVs) are a desirable green alternative choice compared to the traditional electric grid, because it not only extends the service range of IoVs but also saves the cost on energy. However, the energy on RSUs is limited and usually influenced by communication policies. Therefore, the communication scheduling policy on RSUs plays an important role on the persistence of network, which is still an open topic to be solved. In this paper, we focus on the communication scheduling problem on RSUs, and propose an LSTM-based communication scheduling algorithm for RSUs in IoVs. The scheduling algorithm is composed of three parts - deep learning clustering, LSTM-based traffic prediction, and a vehicle access scheduling algorithm. At last, we conduct an extensive simulation, and the simulation results indicate that our algorithm can achieve a better performance than the no scheduling mechanism.

The 5th generation (5G) wireless network with Unmanned aerial vehicle (UAV) is considered to be one of the most effective solutions for improving the communication coverage. However, UAV is easily affected by the wind, accompanied by a certain time delay during the air communication. Thus the inaccurate beamforming will be performed by the base station (BS), resulting in the unnecessary capacity loss. To address this issue, we propose a novel Recurrent Neural Networks (RNN)-based arrival angle predictor to predict the specific communication location of UAV under the 5G Internet of Things (IoT) networks in this paper. Specifically, a grid-based coordinate system is applied during the data preprocessing to make the training process easier and more effective. Moreover, the RNN model with the highest accuracy can be saved during the training process to ensure the real-time prediction. Simulation results reveal that the RNN-based predictor we proposed is of high prediction accuracy, which is 98% in average. Therefore, a more precise beamforming can be performed by BS to reduce the unnecessary capacity loss, resulting in a more effective and reliable communication system.

With recent advances in wireless communications, vehicular networks have attracted great interests in both industry and academia. This work aims at proposing a novel vehicular fog computing paradigm including both the system architecture and the scheduling algorithm. Specifically, we present a hierarchical architecture, which integrates the paradigm of both fog computing and the software defined networking (SDN). Then, we formulate a novel problem called Cooperative Service in Vehicular Fog Computing (CS-VFC), which aims at maximizing the bandwidth efficiency by coordinating the service in both the fog layer and the cloud layer. We prove that CS-VFC is NP-hard. On this basis, we propose an on-line scheduling algorithm, which incorporates with the network coding and makes scheduling decisions at SDN controller. In particular, it will determine the coding policy for each cloud node, and then it will implement both the intra and inter cooperation strategies at the fog layer. Finally, we build the simulation model by implementing NS3 simulator and SUMO. A comprehensive simulation is carried out to demonstrate the superiority of the proposed system architecture and the solution.

With the development of the era of internet, an increasing number of images flow into people’s daily life, and it’s really a challenge to quickly search interesting images in such a huge image database. The most advanced method is using a deep neural network to get hash code of images to achieve fast image retrieval at present. However, people usually use the single pooling method to screen the image pixels when designing the neural network, and some effective information of the image will be gradually lost due to the single pooling method with the number of network deepening. Aiming at this problem, this paper proposes a convolutional neural network combining multiple pooling methods to preserve the effective information of the image as much as possible. To verify the effectiveness of the proposed method, many experiments are carried out on the CIFRA-10, NUS-WIDE and MNIST datasets. The experimental results show that the proposed method is better than most existing hash-based image retrieval methods.

Heterogeneous network resources are expected to cooperate with each other to support data services in vehicular networks. However, individual wireless interface cannot complete services within short vehicular dwelling time. Further, the network heterogeneity further complicates the transmission task assignment among multiple wireless interfaces. To resolve such an issue, we propose a novel architecture, where a scheduler is able to manage heterogeneous network resources in a centralized way. Then, we formulate the heterogeneous wireless interface management (HWIM) problem by considering both the heterogeneities of wireless interfaces and the delay constraints of service requests. On this basis, we design a heuristic algorithm called Adaptive Task Assignment (ATA), which synthesizes mobility feature, broadcast efficiency and service deadline into priority design. Accordingly, ATA is able to adaptively distribute broadcast task of each request among multiple interfaces, so as to improve overall system performance. Last but not the least, we build the simulation model and implement the proposed algorithm. The comprehensive simulation results show the superiority of the proposed algorithm.

Efficient data dissemination is critical for enabling emerging applications in vehicular ad hoc networks. As a typical traffic scenario, the bidirectional road scenario of highways bring unique challenges on well exploiting the benefit of vehicle-to-vehicle (V2V) communication for data sharing among vehicles driving in opposite directions. This paper is dedicated to investigating the characteristics of data services in such a scenario and exploring new opportunities for enhancing overall system performance. Specifically, we present a system architecture to enable the road-side unit assisted data scheduling via vehicle-to-infrastructure communication. Then, we give a theoretical analysis on the opportunity of successful data sharing among vehicles driving in opposite directions based on the analysis of signal-to-interference-noise-ratio of V2V communication. On this basis, we propose a clustering mechanism based on the design of a time division policy and the derivation of the optimal cluster length. In addition, a cluster association strategy is designed to enable vehicles to dynamically join or leave a cluster based on their real-time velocities. Furthermore, a two-phase backoff mechanism is designed for distributed data sharing based on V2V communication, and a cooperative scheduling algorithm is proposed for selecting sender vehicles as well as the corresponding data items for broadcasting. Finally, we build the simulation model and give a comprehensive simulation study, which demonstrates that the proposed solutions can effectively improve the overall system performance.