Ad Hoc Networks最新文献

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Nanotechnology has recently emerged as a pivotal field with wide-ranging implications. Its integration into the 6G-enabled Internet of Things (IoT) has given rise to the 6G-enabled IoNT (Internet of Nano Things) paradigm, impacting sectors such as healthcare, industries, smart homes, aerospace, and defense. This technology offers opportunities to revolutionize existing methodologies and enhance efficiency. Research efforts are now focusing on developing secure, scalable network infrastructures tailored for the healthcare sector at the nanoscale, leading to the concept of the Internet of Nano Medical Things (IoNMT). However, the unique characteristics of nanotechnology pose security challenges, particularly concerning privacy, confidentiality, dependability, latency, and the expensive consequences of blockchain-based storage. Authentication and transparency are vital for ensuring secure data handling in IoNMT networks, necessitating a secure access mechanism resistant to unauthorized interference. To tackle these challenges, this study proposes a smart contract-based authentication protocol developed specifically for 6G-IoNMT networks. The framework aims to manage real-time information with minimal latency through decentralized peer-to-peer cloud servers while addressing security and privacy concerns. Thorough security and privacy assessments, including ROR model evaluations, Scyther tool analysis, and informal security evaluations, validate the protocol’s effectiveness. Moreover, the simulation highlights that this protocol offers superior security and efficiency as well as energy consumption compared to existing protocols.

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

The fast advancement of quantum computing poses a substantial challenge to the privacy and security of critical scientific research data. This is because the standard cryptography methods, which have been proven effective in classical computers, are rendered less secure in the face of quantum computing approaches. Previously, numerous endeavors have been made to safeguard confidential information through the utilization of different standards and quantum cryptographic methods. However, there remains a research void with several challenges and limitations, including excessive computational burden, vulnerability to various attacks, and limited hardware compatibility for implementation. We propose a modern hybrid cryptographical approach to secure sensitive data from various attacks and vulnerabilities to address the existing limitations. The suggested standard integrates traditional cryptographic standards with quantum-resistant standards to boost sensitive scientific data privacy and security and address various classical cyber-attacks and critical quantum attacks. For the context of scientific data privacy and security, our work depicts a hybrid standard structure by performing a systematic exploration of current encipherment model challenges and issues such as the investigation of various susceptibilities of mathematical cryptographic models. In this work, we apply lattice-based coding as the outer layer and Advanced Encryption Standard (AES) as the inner layer to improve security and efficacy. The proposed security theorem launches the operational veracity of lattice-based coding in the face of quantum attacks, while a complete investigation of the proposed algorithm efficacy vitrines the enhanced security and scalability of the anticipated hybrid standard transversely diverse input sensitive data volumes. Furthermore, this proposed work offers the security confidence score of the hybrid model by the amalgamation of AES and lattice-based cryptography (LBC), hence guaranteeing strength next to both quantum and traditional computing weaknesses. The investigational results prove the improved efficiency of the proposed hybrid model in contrast to traditional and past quantum-resistant models.

Latency is a critical aspect for a broad spectrum of applications that relies on the internet, such as, voice over IP (VoIP) or teleconferencing, and the lack of ultra-fast and highly reliable communications is prominent in rural areas even in mature economies. Our proposal focuses on optimizing the deployment of microservice-oriented architectures (MSA) in computing and routing enabled unmanned aerial vehicles (UAVs). For that matter, an information system which gathers all the information of the flying ad hoc network (FANET) is developed. From there, we propose multiple approaches, based on integer linear programming (ILP) and heuristics, to tackle the minimization of end-to-end latency by deploying multiple instances of microservices in the UAVs that are close to the users that make use of them. Extensive experiments based on network emulation prove the performance of our ILP formulation of the problem and address the optimality gap between the ILP-based approach and the heuristics ones, which are highly scalable and usable in real-time for large-scale scenarios.

In the digital era, our lives are intrinsically linked to the daily use of mobile applications. As a consequence, we generate and transmit a large amount of personal data that puts our privacy in danger. Despite having encrypted communications, the DNS traffic is usually not encrypted, and it is possible to extract valuable information from the traffic generated by mobile applications. This study focuses on the analysis of the DNS traffic behavior found in mobile application traces, developing a methodology capable of identifying mobile applications based on the domains they query. With this methodology, we were able to identify apps with 98% accuracy. Furthermore, we have validated the effectiveness of the characterization obtained with one dataset by identifying traces from other independent datasets. The evaluation showed that the methodology provides successful results in identifying mobile applications.

Ensuring secure authentication between participating entities in VANETs has emerged as a critical challenge. Most of existing schemes mainly consider authentication issue in single administrative domain and suffer from various limitations that include privacy-preserving and malicious entity tracking. This paper proposes a double-layer blockchain-assisted conditional privacy-preserving cross-domain authentication scheme (DBCPCA) that leverages blockchain technology and certificate-less signatures to address these challenges. In DBCPCA, the upper-layer blockchain is used in cross-domain authentication by sharing inter-domain information among multiple different administrative domains. The lower-layer blockchain is employed in intra-domain authentication. In DBCPCA, we also introduce an anonymity mechanism to protect the real identity of a vehicle while enabling the system to trace a malicious vehicle, thereby addressing conditional privacy-preserving concerns. In addition, a security analysis of the proposed scheme demonstrates that it can meet our specified security objectives. Finally, we make a detailed experimental comparison with the most relative solutions such as BCPPA and BCGS. The results show that the DBCPCA scheme reduces the time cost by at least 66.68 % compared to the BCPPA scheme during the signature generation phase. During the signature verification phase, the DBCPCA scheme reduces the time cost by at least 62.39 % compared to the BCGS scheme.

The operations of unmanned aerial vehicles (UAVs) are susceptible to cybersecurity risks, mainly because of their firm reliance on the Global Positioning System (GPS) and radio frequency (RF) sensors. GPS and RF sensors are vulnerable to potential threats, such as spoofing attacks that can cause the UAVs to behave erratically. Since these threats are widespread and potent, it is imperative to develop effective intrusion detection systems. In this paper, we propose an image-based intrusion detection system for detecting GPS spoofing cyberattacks based on a deep learning methodology. We combine convolutional neural networks with Principal Component Analysis (PCA) to reduce the dimensionality of the dataset features, data augmentation to increase the size and diversity of the training dataset, and transfer learning to improve the proposed model’s performance with limited data to design a fast, accurate, and general method. Extensive numerical experiments demonstrate the effectiveness of the proposed solution carried out using benchmark datasets. We achieved an accuracy of 100% within a running time of 120.64 s at 0.3529 ms latency and a detection time of 2.035 s in the case of the training dataset. Further, using this trained model, we achieved an accuracy of 99.25% within a detection time of 2.721 s on an unseen dataset that was unrelated to the one used for training the model. In contrast, other models, such as Inception-v3, showed lower accuracy on unseen datasets. However, Inception-v3 performance improved significantly after Bayesian optimization, with the Tree-structured Parzen Estimator reaching 99.06% accuracy. Our results demonstrate that the proposed image-based intrusion detection method outperforms the existing solutions while providing a general model for detecting cyberattacks included in unseen datasets.

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively.

With the rapid growth in access demand for Internet of Things (IoT) devices, effective utilization of spectrum resource has become a key challenge to ensure reliable communications. Traditional dynamic spectrum access methods are inefficient when there are too many device accesses, channel reductions, and channel quality deterioration. In this paper, we propose a dynamic spectrum access method based on a fusion algorithm of graph neural network (GNN) and deep Q network (DQN), improving spectrum access efficiency while maintaining a good access success accuracy. Compared with traditional DQN, the computation time can be reduced by over 35%. Our approach first uses GNN to interact with the environment and predict the state of the IoT spectrum environment. Subsequently, automatic learning and optimization of spectrum access policies are achieved by selecting the mobile IoT user’s actions based on these predicted states using the DQN’s target network, experience playback, and reinforcement learning techniques. Simulation results show that the system model based on the proposed method can operate with better efficiency than the conventional method while maintaining a good channel access rate and channel quality.