The space-air-ground integrated network has emerged as a critical enabler to achieve high-capacity 6 G communications. However, frequent handovers between satellites and gateways, along with unbalanced gateway traffic, significantly degrade the overall transmission capacity. To address these issues, this paper proposes a balanced satellite-ground scheduling architecture based on the analytic hierarchy process, called AHP-BSA. First, three spatiotemporal parameters (interconnection time $R(t)$, transmission capacity $C(t)$, and propagation delay $D_{p}$) are defined. The rationality and consistency of AHP-BSA is proved using the spatiotemporal parameters. In the parameter calculation phase of AHP-BSA, this paper further proves the relationship between $R(t)$ and outage probability, as well as between $D_{p}$ and transmission efficiency. Then, a parameter optimization algorithm is designed in AHP-BSA. These spatiotemporal parameters are normalized, weighted, and incorporated into the parameter optimization algorithm. Through stability-aware adjustment, it adjusts scheduling decisions in response to link dynamics and real-time fluctuations. Simulation results confirm that AHP-BSA outperforms existing methods in transmission capacity, time complexity, and long-term traffic balance.

Vehicle trust management is closely related to the identity security of intra-domain vehicles and has garnered widespread attention. However, there is a significant conflict between vehicle trust and identity privacy, which has led to the emergence of novel trust link attack issues. To address the problem, this paper proposes a vehicle identity trust management algorithm based on differential privacy, called DITDP. In DITDP, we first design an identity trust evaluation model based on D-S evidence, which is used to quantify vehicle trust values, including the direct trust, the recommended trust, and the aggregated trust. Then, we design the dynamic trust evaluation and identity privacy protection modules in DITDP. The dynamic trust evaluation module corrects conflict between vehicle trust components and can dynamically update vehicle trust values in the time domain. The identity privacy protection module achieves indistinguishable trust while ensuring the availability of trust value by using Differential privacy. Finally, the simulations verify that the DITDP algorithm performs well in terms of identity protection ability, trust availability, and other aspects.

The application of vehicular ad hoc networks (VANETs) in intelligent transportation systems (ITS) enables the collection and dissemination of traffic event information, thereby helping to improve transportation efficiency and security. However, due to the open nature of VANETs, authenticated nodes are not completely trustworthy, posing challenges in determining the authenticity of events. Existing studies overlook the impact of node density on event validation performance, resulting in insufficient reliability in low-node-density scenarios and a lack of detailed quantitative assessments of attack resistance. To address this issue, this article proposes an attack-resistant and adaptive node density event validation scheme called AR-NDA. In AR-NDA, the roadside unit (RSU) can assess the trustworthiness of opinions on event authenticity from various nodes using the designed trustworthiness assessment model. Based on the assessment results, the RSU can reasonably leverage the trustworthiness of nodes’ opinions while considering other relevant factors to ultimately infer the authenticity of the event. The effectiveness of the AR-NDA scheme is validated through extensive experiments under varying node densities and proportions of malicious nodes. The scheme is capable of coping with attacks from malicious nodes, maintaining an event validation accuracy of over 0.8 even under the extreme conditions of low node density and high proportions of malicious nodes.

The rapid growth of low-power Internet of Things (IoT) applications has created an urgent demand for compact, battery-free power solutions. However, most existing RF energy harvesters rely on active rectifiers, multi-phase topologies, or complex tuning networks, which increase circuit complexity and static power overhead while struggling to maintain high efficiency under microwatt-level inputs. To address this challenge, this work proposes a harmonic-recycling, passive, RF-energy-harvesting system with integrated power management (HR-P-RFEH). The system adopts a planar microstrip architecture compatible with MEMS fabrication, integrating a dual-stage voltage multiplier rectifier (VMR) and a stub-based harmonic suppression–recycling network. The design was verified through combined electromagnetic/circuit co-simulations, PCB prototyping, and experimental measurements. Operating at 915 MHz under a 0 dBm input and a 2 kΩ load, the HR-P-RFEH achieves a stable 1.4 V DC output and a peak rectification efficiency of 70.7%. Compared with a conventional single-stage rectifier, it improves the output voltage by 22.5% and the efficiency by 16.4%. The rectified power is further regulated by a BQ25570-based unit to provide a stable 3.3 V supply buffered by a 47 mF supercapacitor, ensuring continuous operation under intermittent RF input. In comparison with the state of the art, the proposed fully passive, harmonic-recycling design achieves competitive efficiency without active bias or adaptive tuning while remaining MEMS- and LTCC-ready. These results highlight HR-P-RFEH as a scalable and fabrication-friendly building block for next-generation energy-autonomous IoT and MEMS systems.

In agricultural production, fixed-wing UAVs are widely used for image acquisition of pest detection because of their long endurance. However, due to its characteristic of being constrained by the turning radius, it causes the long turning path and much energy consumption. Thus, this article designs an algorithm based on turning span selection (TSS) for fixed-wing UAV, which plans a coverage path with the shortest turning path as possible. First, the target region model of the farmland is established. Then, by analyzing the relationship between turning radius and turning span of fixed-wing UAV, three different turning strategies are proposed. Finally, according to the pointer network model and the actor-critic algorithm in reinforcement learning, the TSS algorithm is designed. Simulation results show that the proposed TSS algorithm can effectively plan a flight path with a shorter turning path, and has obvious performance improvement compared with the existing algorithms

For non-independent and identically distributed (Non-IID) client data, clustered federated learning (CFL) addresses data heterogeneity by grouping clients with similar data distributions and training specialized models for each cluster. However, the existing CFL methods have the risk of privacy leakage due to the sharing of model parameters. Worse, they rely on rigid clustering schemes that struggle with boundary clients. To overcome these limitations, this paper proposes a Pseudo-Label based Clustered Federated Learning (PL-CFL) framework. PL-CFL computes client similarity from pseudo-label datasets generated by applying client models to a public unlabeled dataset. This eliminates any exchange of sensitive data or model parameters. The soft hierarchical clustering algorithm with virtual connections is designed in PL-CFL. It allows each client to belong to multiple clusters and effectively handles boundary cases. Additionally, cluster-wise consistency regularization aligns predictions within clusters, thereby reducing model divergence and improving generalization. Through theoretical analysis, the statistical stability of the pseudo-label similarity measure is established. And it shows that consistency regularization reduces intra-cluster generalization error. Finally, experiments are conducted on the MNIST and CIFAR-10 datasets with three Non-IID scenarios: Dirichlet-based partitioning, structured clustered partitioning, and label preference shifting. It demonstrates that PL-CFL consistently outperforms existing federated learning baselines in personalized accuracy and convergence speed.

The secure sharing of massive data for federal vehicle networking has gradually become a research hotspot. Federated learning allows users to train models without sharing local raw data, which is beneficial for protecting privacy. However, attackers can infer users’ sensitive information by stealing the local model parameters uploaded by Vehicle Users (VUs). Meanwhile, due to differences in vehicle performance, lower-performance vehicles require more time for local training, which hinders the aggregation of the global model. To address these issues, this paper proposes an enhanced privacy training architecture based on federated learning, named FL-EPTA. FL-EPTA introduces Laplace noise into the objective function of local training using a functional mechanism to achieve ϵ -privacy protection. Taking into account the differences in computing resources between VUs, FL-EPTA designs a VU selection algorithm on the Roadside Unit (RSU) to optimize the selection process. By formulating VU selection as a maximization problem with knapsack constraints, a greedy approach is employed to minimize training time. Theoretical analysis proves that the proposed FL-EPTA architecture satisfies ϵ -differential privacy and ensures convergence. The simulation results further demonstrate that FL-EPTA achieves faster convergence, lower training loss, and shorter training time compared to existing methods.

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink's traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

With the advent of the Fourth Industrial Revolution, the use of the smart grid is becoming more and more widespread. However, a large number of distributed smart grid devices currently have insecure authentication and low information sharing. As the use of blockchain technology can provide a secure and trustworthy interaction environment for smart grids, this paper proposes a blockchain-based smart grid security architecture, which resolves the conflict between distributed smart grid devices and centralized management is resolved. In the proposed architecture, we have newly designed blocks and gateway nodes, which improve the security and credibility of smart grid device identity authentication. Then we design a Computing Balance based Exchange (CBE) algorithm to improve the interaction efficiency between smart grid devices. In addition, the multi-layer smart contract based on smart grid is proposed to solve the problems of lack of mutual trust and information sharing between smart grid devices. Finally, the blockchain-based smart grid security architecture and multi-layer smart contract can achieve secure interaction and efficient identity authentication among smart grid devices.

In modern warfare, the use of UAVs for reconnaissance, search and rescue missions is very common, and it is essential to plan the flight path of UAVs. However, in the face of complex battlefield environment, the existing flight path planning algorithms have the problems of long time consumption and unstable path. Therefore, this paper studies the UAV flight path planning optimization in complex battlefield environment. First, we construct the battlefield environment model. Then, by analyzing the UAV flight constraints existing in battlefield environment, the objective function is obtained. And the problem of UAV flight path planning optimization is transformed into a nonlinear combinatorial optimization problem. On this basis, an Adaptive Adjustment Flight Path Planning algorithm (AA-FPP) is proposed. The AA-FPP algorithm adaptively adjusts the absorption coefficient of fireflies by using chaotic strategy. It adjusts the control position updating formula by using time-varying inertia weight to enhance its global searching ability. Then, random factors based on Boltzmann selection strategy are introduced to perturb the iterative solutions in AA-FPP. It expands the search space of the path and enhances the convergence efficiency. Finally, simulation results show that the AA-FPP algorithm can successfully plan a flight path that reduces static/dynamic threat intensity. And it has greater advantages in path stability and planning time consumption.

To address the problems of identity forgery attack and low authentication efficiency, this paper proposes a cross-domain vehicle identity authentication algorithm based on the master-slave multi-chain, called CAMS. It utilizes the master-slave multi-chain to achieve cross-domain storage and sharing of vehicle data, thereby improving cross-domain authentication efficiency. Moreover, the CAMS algorithm introduces pseudonym generation and verification parameters in the cross-domain authentication process. It further verifies the vehicle identity before verifying the message, ensuring the anonymity of authentication identity and resisting the identity forgery attack. Finally, the simulation results show that the superior performance of the CAMS in terms of computational overhead and authentication efficiency.

Outsourcing encrypted data to a powerful cloud is an efficient way to provide Location Based Service (LBS) in the Internet of Vehicles (IoV) while reducing the local overhead for vehicular LBS queries. However, existing schemes do not account for the numerous concurrent connections to the cloud while querying encrypted data on cloud servers. It poses huge challenges to the privacy-preserving and request efficiency of vehicle users. The purpose of this article is to identify the privacy and request efficiency concerns. Then, a Fog-Assisted Privacy Preserving (FAPP) scheme for vehicular LBS requests is proposed. By introducing the fog device to aggregate requests, the FAPP scheme solves the congestion problem of simultaneous massive requests. Additionally, while executing a vehicular LBS query, it uses R-tree and homomorphic encryption techniques to safeguard user privacy. The experimental findings demonstrate that, in comparison to other query strategies, the FAPP scheme is more efficient for vehicular LBS query and privacy preservation.

The crowdsourcing application improves the data collection capability of the IoV. However, the identity of crowdsourcing participant is public relative to the crowdsourcing server in IoV crowdsourcing. The identity private of vehicle users is easy to leak. Worse, due to the large number of crowdsourcing participants, crowdsourcing data collection will produce relatively large communication loss. In order to solve these problems, this paper proposes a batch authentication-based privacy protection scheme suitable for the IoV crowdsourcing. First, in order to ensure the privacy and availability of data, this paper selects the homomorphic encryption algorithm for encryption processing. Then, by using the properties of homomorphic encryption, the encrypted data is aggregated to effectively reduce the calculation and communication overhead of roadside units. In addition, for the authentication between crowdsourcing participants, this paper adopts bilinear pairing technology to realize batch authentication of identities and reduce the authentication overhead. Finally, it is verified by simulation that the proposed scheme can effectively reduce communication cost while protecting privacy information of vehicle users.

The satellite Internet of Things (IoT) is an ideal solution for enabling various IoT devices to access networks anytime and anywhere. In traditional satellite IoT architecture, IoT devices are allowed to access satellites directly. However, with the explosion of IoT devices and IoT data, direct connections can bring serious problems for the satellite IoT, such as network congestion and link overhead. To solve these problems, we study the architecture and routing algorithm of the satellite IoT based on a content-centric network (CCN). First, we design the agent super-router (SR) between satellites and IoT devices using the CCN and propose a CCN-based satellite IoT architecture. With the help of the CCN, IoT data can be cached anywhere and transmitted through interest packets and content packets. The IoT data are no longer available only through a direct connection. Second, we design a tree-based ant colony (TAC) routing algorithm in the CCN-based satellite IoT architecture. By combining the advantages of the spanning tree and ant colony algorithm, the TAC routing algorithm speeds up data transmission and data processing between interest packets and content packets. Finally, simulations demonstrate that our proposed architecture and TAC algorithm can effectively solve the network congestion and link overhead problems.

The transmission of massive data between connected drones makes it an important issue to ensure the efficient data sharing and user privacy security. The introduction of federated learning can effectively solve the problem of privacy protection. However, it is difficult to maintain a continuous and stable synchronous communication mechanism in the process of training. There are still problems of data redundancy and low sharing efficiency. Thus, this paper proposes a Multi-level Asynchronous Federated Learning (MAFL) architecture, realizing efficient data sharing in the Internet of Drones (IoD). The MAFL architecture deploys different federated learning training participation strategies for different IoD entities. The drone entity is deployed with the distributed local training by using initiative inquiry mechanism. For the edge entity of IoD, on the one hand, it enhances the performance of drone local training by designing data set delivery scheme; On the other hand, the weighted aggregation training is deployed to improve the convergence speed and accuracy of MAFL. For the central entity of IoD, the global aggregation training is deployed to accelerate the synchronization of MAFL. The simulation analysis shows that the MAFL can support training of different entities with fast convergence and high accuracy in IoD.

An intrusion detection system (IDS) ensures cybersecurity. However, the existing IDSs face challenges, such as low detection accuracy, complex data feature extraction and high resource consumption costs. Therefore, this paper proposes an IDS based on grayscale and entropy, called the GE-IDS. The GE-IDS performs flow preprocessing based on filtering and grayscale conversion to realize traffic visualization. It improves real-time performance and reduces resource consumption. Moreover, the GE-IDS can effectively analyze and cluster traffic grayscales. On the basis of the obtained traffic grayscale clusters, the GE-IDS can detects known cyberattacks with a higher accuracy. By defining cluster entropy, the GE-IDS can detect unknown cyberattacks. We use the latest CICIIDS 2017 dataset to verify the performance of the GE-IDS. Simulation results show that the GE-IDS has high precision in terms of detecting known attacks. It also has a strong unknown attack detection ability.

Abstract The existing research on the Internet of Vehicles (IoV) based on blockchain adopts the single chain mode. However, in the face of large-scale data environment, it is difficult to ensure the security and efficiency of large-scale data storage by using single chain. Thus, this paper is devoted to the research on the master-slave multi-chain IoV architecture. In the proposed architecture, there are two major innovations: 1) By defining three different data structures, micro-block, key-block and verification-block, the master-slave multi-chain model based on hash anchoring is designed to break through the data of the whole network. It ensures the consistency and integrity of IoV data, and solves the problem of privacy disclosure caused by insecure data storage; 2) A data upload and access mechanism based on attribute encryption is designed between users and RSU nodes. It solves the problem of privacy disclosure caused by insecure transmission outside the data chain. Finally, we verify that the proposed architecture is more suitable for large-scale data scenario by simulation analysis of system response time and encryption/decryption overhead.

Abstract In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry. However, in order to improve the accuracy of detection, the existing static detecting methods sacrifice the excessively high analysis complexity and time cost. Moreover, the correlation between static features leads to redundancy of a large amount of data. Therefore, this paper proposes a static detecting method of Android malware based on sensitive pattern. It uses an improved FP-growth algorithm to mine frequent combinations of sensitive permissions and API calls in malicious apps and benign apps, which avoids the generation of redundant information. In addition, this paper adopts multi-layered gradient boosting decision trees algorithm to train the detection model. And a dual similarity combination method is proposed to measure the similarity between different sensitive patterns. The experimental results show that our proposed detection method has high accuracy and great generalization ability.

Secure routing algorithm plays an important role in the performance of LEO satellite network survivability. However, due to the instability, openness and exposure of inter-satellite links, the internal routing of LEO satellite network is vulnerable to malicious attacks. Considering that trust management has a better performance in solving the internal attacks, this paper proposes a secure routing algorithm based on node trust for LEO satellite network, called SLT. The SLT is based on the distributed trust evaluation model, which is used to calculate the direct trust, indirect trust and aggregate trust value between satellite nodes through D-S evidence theory. Then, combining the low cost OPSPF routing protocol with the trust evaluation, the SLT algorithm is designed. Through timely detection and isolation of malicious nodes in the satellite network, STL algorithm can reduce the impact of malicious packet dropping caused by internal attacks. Finally, the simulations verifies that SLT algorithm has an average increase of 27% in packet delivery rate and an average decrease of 70% in packet loss rate, compared with OPSPF.

Security in Internet of Things (IoT) remains a significant concern within academia and industry. With the great potential of IoT data, the traditional centralized architecture of IoT system is limited and cannot afford security solutions. In this paper, to address the issue of IoT data security, we propose a blockchain-based data acquisition and processing architecture. The proposed architecture ensures IoT data security through data consistency. It supports distributed IoT nodes to negotiate consensus on the processed data, and decides to write the consensus data to blockchain ledger. Since distributed nodes are non-peer and have different voting weights in the proposed architecture, traditional consensus algorithms are not applicable. Therefore, we design a novel consensus algorithm for data consistency between non-peer nodes: Byzantine Fault-Tolerant consensus algorithm based on Dynamic Permission Adjustment (DPA-PBFT) algorithm. The DPA-PBFT algorithm works in the consensus domain of different weight nodes with the ability of self-optimize. It improves consensus efficiency and reduces communication overhead for data consistency. Finally, we conduct numerous experiments to evaluate the performance improvement of the DPA-PBFT algorithm under the proposed distributed architecture.