LiDAR is one of the most important sensors in the field of autonomous driving and allows for better and more accurate perception of the changes in the surrounding environment. Most of the existing 3D object detection methods use data augmentation and feature fusion enhancement to improve the performance of detection, but the majority of the methods ignore the handling of sample imbalance problems during data augmentation. Also, the designed feature fusion and enhancement methods were not well suited to work with the enhancement methods. To this end, we developed a combined method involving data augmentation and feature enhancement. The designed approach has two main objectives: 1) to address the problem of unbalanced sample distribution in detection scenes through data augmentation, and 2) to enhance feature perception using a special feature enhancement module. Our proposed method solves the problem of class imbalance by directly increasing the number of pedestrian samples in the scene through mixed data augmentation, i.e., RG-Aug. In addition, we introduce the Decoupling and Attention Fusion module (DAF), which combines classification headers with high-level features and prediction branches with low-level features. Leverage data features between different layers of features to get a more robust feature representation. Finally, the multi-scale pyramid attention enhancement module is designed to achieve feature enhancement of multi-scale features by means of attention to improve the detection ability of small objects in the scene, especially the detection ability of pedestrians. Our method can achieve 1.57%, 2.16%, and 2.05% performance improvement on the KITTI dataset for Easy, Mod, and Hard samples, respectively. Furthermore, for the detection of pedestrians, our method has a significant competitive advantage over other state-of-the-art techniques with a mAP of 73.42%.

Blockchain-based information storage and retrieval systems face significant challenges in achieving efficiency, privacy, and auditability when operating across heterogeneous blockchain platforms. Existing solutions often struggle to balance these requirements, particularly in cross-chain environments involving both public and consortium blockchains. This paper proposes a novel framework that leverages cross-chain technology to address these limitations. The framework integrates multi-party threshold cross-chain consensus to optimize verification efficiency and reduce the computational burden on trusted nodes. To ensure privacy-preserving information querying and retrieval, advanced cryptographic techniques are employed. Additionally, a dedicated auditor set within the consortium blockchain is introduced to detect malicious behavior and enforce regulatory compliance. Comparative evaluations demonstrate that the proposed framework outperforms existing methods in terms of privacy protection, efficiency, and auditability. Experimental results on Hyperledger Fabric demonstrate significant improvements in throughput, achieving at least 20 Transactions Per Second (TPS), along with latency below 3.5 seconds and 300MB memory utilization under standard PC configurations. These findings validate the framework's practical viability for secure and efficient cross-chain information retrieval while maintaining superior performance compared to existing solutions.

Unsupervised anomaly detection in multivariate time series can prevent large-scale system failures and is crucial for various applications. Most existing methods only consider a single temporal pattern and insufficiently model the normal pattern, causing the model to learn incorrect temporal patterns from anomalous or noisy data. This poses a significant challenge for accurate anomaly detection. To overcome these challenges, we introduce a new dynamic decomposition and reconstruction anomaly detection algorithm, DMRAD. DMRAD captures various regular patterns of multivariate time series by designing a dynamic decomposition module that learns trend and seasonal features. By integrating improved channel and temporal attention mechanisms, DMRAD effectively learns the correlations within the sequence and dependencies across different sequences, thereby enhancing the model's capacity to distinguish between features and extract relevant information. DMRAD incorporates a latent anomaly-noise detection algorithm to identify and suppress the influence of noise and latent anomalies, elevating the overall accuracy of anomaly detection. Extensive experimental comparisons demonstrate that DMRAD achieves state-of-the-art performance on a variety of datasets for real-world application scenarios.

The exponential growth of mobile network traffic makes accurate traffic prediction essential for network optimization. However, this remains challenging due to complex spatial-temporal dependencies and diverse traffic patterns across base stations. Existing methods often rely on single-factor modeling or fail to effectively handle this multi-pattern heterogeneity. We propose the Meta-Optimized Multi-Period Spatial-Temporal Transformer (MMSTT), a novel framework integrating dynamic graph-based spatial modeling and multi-period temporal fusion within a Transformer architecture. To address the multi-pattern nature of traffic data, we incorporate a clustering algorithm and a meta-learning optimization process. This process captures shared features across patterns during meta-training and adapts to pattern-specific characteristics during meta-testing. Experimental results on a real-world dataset demonstrate that MMSTT significantly outperforms existing baselines, reducing the mean absolute error (MAE) to 28.39, root mean square error (RMSE) to 42.83, and mean absolute percentage error (MAPE) to 0.37. Compared to the standard Transformer and GCN baselines, MMSTT achieves improvements of 38.71% in MAE, 33.57% in RMSE, and 60.22% in MAPE.

The smart environment is a crucial application of the Internet of Things(IoT). Due to its growing security and efficiency needs, recent years have seen the proposal of numerous authentication and key agreement (AKA) protocols. Unfortunately, most of existing AKA protocols only support one-to-one AKA and rely on the elliptic curve cryptosystem, resulting in huge overhead. In addition, these protocols fail to consider the resource-asymmetric characteristics of this scenario. That is, the resources on the gateway side are abundant, while the resources on user sides and device sides are limited. In order to achieve efficient and secure one-to-many AKA establishment in this scenario, where one-to-many means that users can realize key agreements with multiple smart devices at the same time. For the first time, this paper uses the one-to-many computing structure of the Chinese Remainder Theorem (CRT) to design an efficient one-to-many AKA establishment, which is perfectly adapted to resource-asymmetric allocation in smart environments. Compared with existing solutions, this solution has the following advantages. Firstly, our protocol is suitable for resource-asymmetric environments, where the gateway acts as an intermediate node and uses rich resources to integrate multiple AKA requests. Secondly, the solution supports users to negotiate session keys with multiple smart devices at the same time. Thirdly, we prove the protocol’s security under the Real-or-Random (ROR) model. In addition, we perform formal security verification of the protocol using the Automated Validation of Internet Security Protocols and Applications(AVISPA) tool. Finally, the security and efficiency of this solution are superior to similar solutions. Specifically, our solution can meet 18 security and functionality requirements. Compared with the latest similar scheme, assuming that the number of smart devices is 10, our scheme reduces the computational cost by 75.75%. At the same time, in terms of communication cost, our protocol reduces it by 37.78%.

Mobile opportunistic networks (MONs) have gained considerable attention in enabling impromptu communication due to the progress in communication technologies over the past decade. One of the main characteristics of MONs is their inconsistent connectivity and implicit end-to-end routing paths. This presents a challenge in making optimal routing decisions during message transmission, as we need to identify relay nodes that can reliably transport messages to their destinations quickly and cost-effectively. Here, we propose a novel multi-criteria fusion routing decision making algorithm (MFRD). Initially, we establish four metrics that measure the message transmission capability (MTC) of a node in terms of message throughput, message tolerance, geographic connectivity, and time connectivity. Next, we use the game theory mechanism to merge the DEMATEL subjective weights and the CRITIC objective weights of each metric, resulting in a more thorough integrated weight for them. Then, we integrate the benefits of two MCDM techniques, TOPSIS and GRA, to provide a new relative proximity measure for evaluating the MTC of alternative nodes. Finally, during message propagation, we select nodes with higher MTC as next-hop relays. The simulation results demonstrate that MFRD not only significantly improves the delivery rate and reduces the average latency, but it also shows excellent performance in network overhead and average hop count.

Opportunistic Mobile Social Networks (OMSNs) represent a unique class of Delay-Tolerant Networks (DTNs) comprised of mobile nodes equipped with communication devices. Data transmission within these networks hinges on interactions between mobile nodes, characterized by unstable connectivity and frequent node mobility. Existing DTN routing algorithms, which incorporate social attributes such as degree centrality, contact frequency, and connection duration, build upon traditional approaches. However, many of these algorithms overlook the mobile social characteristics of nodes, limiting their practical applicability in real-world scenarios. In this work, we utilize the SMOOTH and SPMBM mobility models to simulate realistic movement trajectories in a temporary shelter context, while deeply exploring the mobile social attributes of nodes. We propose a methodology for categorizing friend relationships based on node contact scenarios and predict the mobility of message destinations using node friendships. Our validation results indicate a prediction accuracy of 70% in a GPS-denied environment. Furthermore, through rigorous controlled experiments, our approach has demonstrated superiority over comparable algorithms in terms of delivery rate, network load, and forwarding hop counts.

Delay-Tolerant Networks (DTNs) are commonly utilized in the challenging environment, serving as a valuable complement to conventional networks. In such a context, energy efficiency is of paramount significance. The existing researches on energy saving algorithms in DTNs neglect the influence of different stages of message transmission on the energy consumption. This paper categorizes node energy consumption into two distinct categories, namely, basic and effective energy consumption. On this basis, a segmentation Energy-Saving routing strategy based on Effective Energy Consumption Perception (EECP-SES) is further developed. The strategy segments the service status of nodes, and enhances the scanning interval function of the exponential distribution function via regulating the basic energy consumption, monitoring the effective energy consumption rate, and introducing an energy consumption balance mechanism. The EECP-SES strategy was then implemented on two classical routing algorithms in the conducted simulation experiments. Experiments have demonstrated that the implementation of the EECP-SES yields favorable outcomes in terms of prolonging network service duration and achieving successful message delivery. Compared with six other strategies, EECP-SES strategy helps the Epidemic algorithm extend network service time by 26.57% to 104.89%, helps Prophet algorithm extend network service time by 42.56% to 114.67%.

Reconfigurable intelligent surface (RIS) is playing a growing and ever-more significant role in constructing six-generation (6G) wireless networks due to its properties of low-cost and easy-integration. Current studies about RIS-assisted communication generally assume the RISs are deployed at fixed position or devices, however with the rapid development of unmanned aerial vehicle (UAV) technology, this readily flying wireless access platform is increasingly used to realize reliable communication. If the RISs are mounted on random 3-D (three-dimension) mobile UAVs, how to investigate the uplink transmission of RIS-assisted UAV communication systems would be a great challenge. To resolve this open issue, we establish a novel theoretical model to analyze the uplink performance of RIS-assisted UAV communication systems with random 3-D mobile pattern. In the modeling process, we firstly provide a random 3-D mobile model for UAVs, where the random waypoint and uniform mobility models are simultaneously used. Next we build an end-to-end (E2E) transmission model for RIS-assisted UAV communication system, where the impacts of channel fading type, RIS configuration, UAV’s mobility and association policies are comprehensively considered. Combining the above two models, we derive the analytical expressions of uplink transmission metrics, and make a bound performance analysis on this base. Finally, we evaluate the uplink performance of RIS-assisted UAV communication system with random 3-D mobile pattern, and verify the proposed theoretical model.

Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in the coarse-grained traffic graph can guide feature learning in the fine-grained traffic graph. To this end, we propose a Spatiotemporal Multiscale Graph Convolutional Network (SMGCN) that explores spatiotemporal correlations on a multiscale graph. Specifically, given a fine-grained traffic graph, we first generate a coarse-grained traffic graph by graph clustering, and extract spatiotemporal correlations on both fine-grained and coarse-grained traffic graphs. Then we propose a cross-scale fusion (CF) to implement information diffusion between the fine-grained and coarse-grained traffic graphs. Moreover, we employ an adaptive dynamic graph convolution network to mine both static and dynamic spatial features. We evaluate SMGCN on real-world datasets and obtain a$1.18\% -3.32\%$improvement over state-of-the-arts.

Fully Homomorphic Encryption (FHE) provides a good solution to directly operate on the ciphertext, and the decryption result is equivalent to the corresponding operation on the plaintext. As a technique suitable for distributed environments, multi-key Fully Homomorphic Encryption (MKFHE) scheme is the most common variant of the FHE scheme since it allows encrypted data to be computed under different keys. Unfortunately, the existing dynamic MKFHE schemes based on learning with errors (LWE) still suffer from the inefficiency of long public keys, which typically grow cube in size along the lattice dimension. Moreover, there current constructions fail to provide reliable and fast algorithms to simultaneously expand ciphertexts with multiple additional keys. In order to solve the above problems, a new faster dynamic MKFHE scheme with shorter public key in asymmetric key setting from LWE is proposed in this paper, in which the size of the public key is further reduced from $\tilde O\left( {{n^3}{{(K + L)}^2}} \right)$ to $\tilde O\left( {{n^2}{{(K + L)}^2}} \right)$. In addition, our scheme cleverly adopts the dual-user cooperation method in distributed system to realize the ciphertext expansion locally, thereby reducing the computing overhead of the cloud server. More interestingly, we design a flexible parallel ciphertext expansion algorithm for the first time based on the basic algorithm. This algorithm realizes the ciphertext expansion when multiple keys are added at the same time, thus significantly improving the computational efficiency of ciphertext expansion in the dynamic MKFHE scheme. Finally, the CPA-secure of our scheme based on standard LWE assumptions is proven.

Abstract The next generation of Internet of Things (IoT) networks and mobile communications (5G IoT networks) has the particularity of being heterogeneous, therefore, it has very strong ability to compute, store, etc. Group-oriented applications demonstrate its potential ability in 5G IoT networks. One of the main challenges for secure group-oriented applications (SGA) in 5G IoT networks is how to secure communication and computation among these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G IoT networks since multiparty joint computation in this environment requires lightweight communication and computation overhead. Furthermore, the primary task of SGA is to securely transmit various types of jointly computing data. Hence, membership authentication and secure multiparty joint arithmetic computation become two fundamental security services in SGA for 5G IoT networks. The membership authentication allows communication entities to authenticate their communication partners and the multiparty joint computations allow a secret output to be shared among all communication entities. The multiparty joint computation result can be used to protect exchange information in the communication or be used as a result that all users jointly compute by using their secret inputs. A novel construction of computation/communications-efficient membership authenticated joint arithmetic computation is proposed in this paper for 5G IoT networks, which not only integrates the function of membership authentication and joint arithmetic computation but also realizes both computation and communication efficiency on each group member side. Our protocol is secure against inside attackers and outside attackers, and also meets all the described security goals. Meanwhile, in this construction the privacy of tokens can be well protected so tokens can be reused multiple times. This proposal is noninteractive and can be easily extended to joint arithmetic computation with any number of inputs. Hence, our design has more attraction for lightweight membership authenticated joint arithmetic computation in 5G IoT networks.

With the rapid developments of Internet of Things (IoT) technologies, the security of sensitive data has attracted more and more attention for many resource-asymmetric smart environments, such as smart home, smart agriculture and so on. The resource-asymmetry environment refers to the uneven distribution of resources on different devices side, which is specifically manifested as gateway side is resource-rich, user side and device side are resource-restricted. Hence, a secure and practical authentication key establishment scheme for such smart environments is urgently needed. Recently many researchers have designed authentication and key establishment schemes for security purpose, however most of them cannot consider the excess of gateway resources and guarantee the anonymity of user, and further, they are not suitable for resource-asymmetric smart environments because they are not lightweight enough in user side and smart device side. Due to the fact that Rabin cryptosystem has the large difference in time-consuming between encryption and decryption, it is extremely suitable for constructing authentication and key establishment scheme for resource-asymmetric smart environments. So, a new practical authentication and key establishment scheme based on the Rabin cryptosystem for resource-asymmetric smart environments is proposed, which can make better use of the advantages of abundant gateway resources and realize the lightweight operations on device side and user side, and at the same time can provide user anonymity. With Proverif and BAN logic, we can prove that our solution not only provides anonymity, but also satisfies all defined security features. Simultaneously, compared with latest similar protocols in computation cost and communication overhead, the results show that our scheme is more effective. Hence, our design has more attraction for authentication and key establishment scheme in resource-asymmetric smart environments.

With the increasing popularity and wide application of the Internet, the users (such as managers and data consumers) in the Industrial Internet of Things (IIoT) can remotely analyze and control real-time data collected by various smart sensor devices. However, there are many security and privacy issues in the process of transmitting collected data through public channels in IIoT environment. In order to against the illegal access by opponents, a novel anonymous user authentication and key agreement scheme based on hash and elliptic curve encryption is proposed in this article, which not only uses a pseudonym tuple database in control nodes to realize the functions of user dynamic joining and anonymity protection, but also resists key loss and device capture attacks through fuzzy biometric extraction technology. In addition, the formal secure analysis of the proposed scheme is carried out using the BAN logic model and ROR model, which proves the security of the proposed scheme. Meanwhile, we also prove the scheme can against the described existing attacks and meet the design goals by a detailed informal security discussion. Compared with the latest similar IIoT authentication proposals, our solution has a very obvious advantage in communication efficiency and realizes more functions. Hence, our scheme is more suitable for the IIoT environment, and can also generate greater benefits.

In delay-tolerant networks(DTN), the timeliness of the node’s social circle and encounter time between nodes have a different effect in designing router algorithms. Considering these factors, this paper proposes an improved spray and wait algorithm based on the node social tree (TNST). Specifically, we will first combine the node’s own attributes with social ability to calculate the node’s delivery capability value. Followed by it, we build and update the social node tree with the delivery capability for each node. Finally, we will predict the encounter time between nodes, which based on their motion information. The node will select the encounter node as the relay node if the message’s destination node is in the node social tree. Otherwise, the node whose social tree with higher propagation capacity will be selected as the relay node. Simulation results show that the TNST algorithm improves the delivery rate and reduces network overhead.

Delay-Tolerant Networks (DTNs), a supplementary means of communication network in extreme situations, have aroused wide attention from scholars. However, it is challenging to efficiently utilize DTNs since they have intermittent and high-latency characteristics. In the design of DTNs routing scheme, the selection of relay nodes takes on a great significance in efficient communication. However, existing research has either considered only one of the node features, or simply fused node attributes without fully using their potential correlations. If the above problems are not effectively solved, the propagation of messages between nodes will become blind, and a considerable number of caches will be occupied and wasted by invalid copies. To solve the above challenges, a novel routing, “Traffic Light Routing Based on Node State Awareness (TLRNSA)”, is proposed for efficient communication. To be specific, the node's own state, the environmental state, and the historical encounter state are synthesized. The traffic value of the node is obtained based on the adaptive weight adjustment mechanism. The node is divided into three traffic light states, including red, green, and yellow, in accordance with the traffic value. Different routing strategies are developed for the above three states to enhance their performance. The results of the comprehensive experiments suggested that TLRNSA outperforms other state-of-the-art algorithms in delivery rate and latency. Compared with the two classic algorithms and the two optimized algorithms, the proposed method increases the delivery rate by 109.1%, 84.12%, 5.09%, and 1.09%, respectively, it reduces the delay by 32.16%, 36.46%, 32.77%, and 6.77%, respectively.

This work studies the emerging C-RAN market in a 5G wireless network where mobile operators lease computation and communication resources from the tower company to serve wireless users. We propose an online C-RAN auction where each mobile operator bids for three types of resources in a future time window: wireless spectrum at base stations (BSs), front-haul link capacities, and mobile BS instances at the mobile cloud. We target an online C-RAN auction that executes in polynomial time, elicits truthful bids from mobile operators, and maximizes the social welfare of the C-RAN eco-system with both spectrum cost at BSs and server cost at the mobile cloud considered. We show how the marriage of (i) a new Fenchel dual approach to convex optimization with (ii) the posted pricing framework for online auction design can help achieve the three goals simultaneously, and evaluate the efficiency of our online C-RAN auction through both theoretical analysis and empirical studies.

Application Layer Multicast (ALM) systems can easily be deployed compared with IP multicast because they do not require any modification to the current Internet infrastructure. Topology-aware ALM systems make multicast forwarding path as far as possible to match the underlying physical paths. It can reduce redundant data packets and forwarding delay. So the source path topology map construction is more important in topology-aware ALM systems. We find that only the routers directly connecting to terminal nodes (usually as gateway) and their connection information are required to be added to the source path topology map. This kind of topology map is named as gateway-level map. In this paper, we first present a delay coarse-grained matching method to generate the gateway-level topology map. After that, the paper discusses some details such as how to get the topology information and how to build the map. Finally, performance analysis and simulation experiments demonstrate the conclusion that the method not only simplifies the information required by topology construction, but also accelerates the speed of accessing topological information. The time of application layer multicast nodes joining the multicast tree is reduced. This method also provides users the higher quality of service.

In advance reservation environment, failure discovery strategy is very important to keep the normal running of whole system. One job's overtime (exceeding its booked time) may lead to a serious of jobs' abnormal termination in co-allocation environment. So there should be some strategies to solve this problem. In this paper, a novel overtime-tolerance strategy is introduced. The strategy can tolerate active job exceeding its booking time and redirect transparently later inactive jobs to other resources. The architecture of implementing our strategy is described in this paper too. Simulation shows our overtime-tolerance strategy can decrease the abnormal termination ratio and improve the system throughput

Reservation in advance provides a solution for the need of reserving the network resource for future. Advance reservation for global grids becomes an important research area as it allows users to gain concurrent access for their applications to be executed in parallel, and guarantees the availability of resources at specified future times. But performance of reservation for grid was seldom considered. In this paper, an adaptive advance reservation is introduced. It can modify not only the parameters of latest request but also that of admitted requests because sometimes the latest request is fixed with parameters. Particular admission control algorithm for this new type of reservation is provided too. Simulation shows that it can improve performance of resource reservation in terms of both call acceptance rate and resource utilization when the percentage of fixed requests is not very high.