Ad Hoc Networks最新文献

In conventional studies on vehicular edge computing, researchers frequently overlook the high-speed mobility of vehicles and the dynamic nature of the vehicular edge environment. Moreover, when employing deep reinforcement learning to address vehicular edge challenges, insufficient attention is given to the potential issue of the algorithm converging to a local optimal solution. This paper presents a content caching solution tailored for vehicular edge cloud computing, integrating content prediction and deep reinforcement learning techniques. Given the swift mobility of vehicles and the ever-changing nature of the vehicular edge environment, the study proposes a content prediction model based on Informer. Leveraging the Informer prediction model, the system anticipates the vehicular edge environment dynamics, thereby informing the caching of vehicle task content. Acknowledging the diverse time scales involved in policy decisions such as content updating, vehicle scheduling, and bandwidth allocation, the paper advocates a dual time-scale Markov modeling approach. Moreover, to address the local optimality issue inherent in the A3C algorithm, an enhanced A3C algorithm is introduced, incorporating an $\varepsilon$-greedy strategy to promote exploration. Recognizing the potential limitations posed by a fixed exploration rate $\varepsilon$, a dynamic baseline mechanism is proposed for updating $\varepsilon$ dynamically. Experimental findings demonstrate that compared to alternative content caching approaches, the proposed vehicle edge computing content caching solution substantially mitigates content access costs. To support research in this area, we have publicly released the source code and pre-trained models at https://github.com/JYAyyyyyy/Informer.git.

With the steep growth of Electric Vehicles (EV's), the consequent demand of energy for charging puts significant load to powergrids. Renewable Energy Sources enabled microgrids can alleviate the problem of energy demand and trade the energy locally in Peer-to-Peer (P2P) manner, where seller (microgrid) and buyer (EV's) “meet” to trade electricity directly on agreed term without any intermediary. However, a foolproof system required for audit and verification of transaction record between seller and buyer to address privacy and security in untrusted and opaque local energy trading market (LETM). Centralized public blockchain enabled system (for audit the transaction records and storage) based on conventional learning models faces mainly two issues in the LETM. (a) if, centralize system runs out of energy and tear down then whole energy trading plunges treated as single point of failure (b) Conventional learning models fail to converge optimal point in case of large state and action space (large number of EV's and their energy demand). The primary objective of this paper to provide secure system for LETM, 1) Distributed nature of Consortium Blockchain used that solve the problem of single point of failure to audit and storage of transaction and profile info of microgrids and EV's. 2) Quantum based Reinforcement Learning (QRL) easily handles the large number of EV's energy supply and demand for smoothly run LETM. In this context, this paper presents Blockchain and Quantum Machine Learning driven energy trading model for EVs (B-MET). A utility maximization problem formulated as Markov Decision Process (MDP) and their solution provided by using QRL focusing on join optimization of selling price, loan amount and quantity of shared energy. MDP is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker, i.e., the future state depends only on the current state and action, not on the sequence of events that preceded it. QRL method combines quantum theory with traditional RL. It is inspire by the principles of state superposition and quantum parallelism. Convergence analysis and performance results attest that B-MET convergences faster, maximizes the utility with lower confirmation delay in P2P energy trading as compare to state of the art techniques.

For 6G non-terrestrial communications, drones will offer uninterrupted connectivity for surveillance, sensing, and localization. They will also serve as drone base stations to support terrestrial base stations, providing large bandwidth, high-rate, and ultra-reliable low latency services. In this paper, for the first time in the literature, we depict the true performance of Terahertz (THz) band communications among drones by applying various channel selection and power allocation schemes with practical THz antennas within (0.75–4.4) THz under realistic mobility and misalignment scenarios. Through numerical simulations, we unveil the capacity of drone links under different channel selection and power allocation schemes within 10s to 100s of Gbps at distances (1–100) m, when drones are in motion and subject to (mis)alignment due to mobility and even under beam misalignment fading. However, when exposed to real drone mobility traces, the performance of all channel selection schemes drops significantly, sometimes by up to six orders of magnitude, due to the occasional reverse orientations of antennas. In addition to the capacity analysis, we report available frequency bands (transmission windows) considering all schemes and mobility patterns. We also identify a band that is commonly available under all considered mobility and misalignment settings, and we evaluate its performance in terms of spectral and energy efficiencies, which can be useful in designing THz transceivers for drone communications. Our findings emphasize the essence of active beam control solutions to achieve the desired capacity potential of THz drone communications, while also highlighting the challenges of utilizing the THz band for drone communications.

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyse the challenges and opportunities to make IoE greener using the discussed technologies.

This paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted multi-UAV data collection system, in which unmanned aerial vehicles (UAVs) collect data from Internet of Things (IoT) devices. The RIS, mounted on building surfaces, plays a vital role in preventing obstruction and improving the communication quality of the IoT-UAV transmission link. Our aim is to minimize the energy consumption of this system, including the transmission energy consumption of IoT devices and the hovering energy consumption of UAVs, by optimizing the deployment of UAVs and the phase shifts of RIS. To achieve this goal, a multi-UAV deployment and phase shift of RIS optimization algorithm (MUDPRA) is proposed that consists of two phases. In the first phase, a joint differential evolution (DE) algorithm with a two-layer structure featuring a variable population size, namely DEC-ADDE, is proposed to optimize the UAV deployment. Specifically, each UAV’s location is encoded as an individual, with the whole UAV deployment is considered as the population in DEC-ADDE. Thus, a differential evolution clustering (DEC) algorithm is employed initially to initialize the population, which allows for obtaining better initial UAV deployment without the need for a predefined number of UAVs. Subsequently, an adaptive and dynamic DE algorithm (ADDE) is employed to produce offspring population to further optimize UAV deployment. Finally, an adaptive updating strategy is adopted to adjust the population size to optimize the number of UAVs. In the second phase, a low-complexity method is proposed to optimize the phase shift of RIS with the aim of enhancing the IoT-UAV data transmission rate. Experimental results conducted on eight instances involving IoT devices ranging from 60 to 200 demonstrate the effectiveness of MUDPRA in minimizing energy consumption of this system compared to six alternative algorithms and three benchmark systems.

Recently, the use of unmanned aerial vehicles (UAV)s for accomplishing various tasks has gained a significant interest from both civilian and military organizations due to their adaptive, autonomous, and flexibility nature in different environments. The characteristics of UAV systems introduce new threats from which cyber attacks may benefit. Adaptive security solutions for UAVs are required to counter the growing threat surface. The security of UAV systems has therefore become one of the fastest growing research topics. Machine learning based security mechanisms have a potential to provide effective countermeasures that complement traditional security mechanisms. The main motivation of this survey is to the lack of a comprehensive literature review about reinforcement learning based security solutions for UAV systems. In this paper, we present a comprehensive review on the security of UAV systems focusing on deep-reinforcement learning-based security solutions. We present a general architecture of an UAV system that includes communication systems to show potential sources of vulnerabilities. Then, the threat surface of UAV systems is explored. We explain attacks on UAV systems according to the threats in a systematic way. In addition, we present countermeasures in the literature for each attack on UAVs. Furthermore, traditional defense mechanisms are explained to highlight requirements for reinforcement based security solutions on UAVs. Next, we present the main reinforcement algorithms. We examine security solutions with reinforcement learning algorithms and their limitations in a holistic approach. We also identify research challenges about reinforcement based security solutions on UAVs. Briefly, this survey provides key guidelines on UAV systems, threats, attacks, reinforcement learning algorithms, the security of UAV systems, and research challenges.

The fast evolutions of Internet of Things (IoT) technologies have been accelerating their applicability in different sectors of life and becoming a pillar for sustainable development. However, this revolutionary expansion led to a substantial increase in attack surface, raising many concerns about security threats and their possible consequences. Machine learning has significantly contributed to designing intrusion detection systems (IDS) but suffers from critical limitations such as data privacy and sovereignty, data imbalance, concept drift, and catastrophic forgetting. This collectively makes existing IDSs an improper choice for securing IoT environments. This paper presents a federated learning approach called FIDWATCH to continuously monitor and detect a broad range of IoT security threats. The local side of FIDWATCH introduces contrastive focal loss to enhance the ability of the local model (teacher) to discriminate between diverse types of IoT security threats while putting an increased emphasis on hard-to-classify samples. A fine-grained Knowledge Distillation (KD) is introduced to allow the client to distill the required teacher's knowledge into a lighter, more compact model termed the pupil model. This greatly assists the competence and flexibility of the model in resource-constrained scenarios. Furthermore, an adaptive incremental updating method is introduced in FIDWATCH to allow the global model to exploit the distilled knowledge and refine the shared dataset. This helps generate global anchors for improving the robustness of the mode against the distributional shift, thereby improving model alignment and compliance with the dynamics of IoT security threats. Proof-of-concept simulations are performed on data from two public datasets (BoT-IoT and ToN-IoT), demonstrating the superiority of FIDWATCH over cutting-edge performance with an average f1-score of 97.07% and 95.63%, respectively.

Due to the impact of turbulence, atmospheric optical wireless channel exhibits characteristics such as time-varying, space-varying and natural randomness, which can be used as a common natural random source for shared secret key extraction. Wireless laser communication technology boasts advantages like high bandwidth and fast transmission, which is conducive to improving key generation rate. Additionally, the strong anti-interference of laser signal helps to reduce key disagreement rate. Moreover, the laser beam’s good directionality effectively decreases the risk of eavesdropping on key information. Given its advantages and a scarcity of research in this regard, this paper proposes a scheme of shared secret key extraction from atmospheric optical wireless channels with multi-scale information reconciliation. In the scheme, to increase the cross-correlation coefficient of signal samples at the two legitimate parties, a preprocessing algorithm is designed based on a denoising algorithm and a threshold-based outliers elimination algorithm, and the denoising algorithm is inspired by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDan); moreover, a multi-level quantization algorithm based on Equilibrium-Optimizer(EO) is developed to balance and optimize distribution of sample points in the sample space; furthermore, to simplify the process of and decrease the computational complexity of information reconciliation, a concept of a multi-scale information reconciliation is formed, on the basis of which three algorithms, B-MSIR, I-MSIR and C-MSIR, are formulated. Finally, its performance is verified by numerical simulations and experiments, and the results show it has better performance in terms of the key disagreement rate, the key generation rate and the key randomness compared with several state-of-the-art algorithms.

Vehicle edge computing (VEC) enhances the distributed task processing capability within intelligent vehicle-infrastructure cooperative systems (i-VICS) by deploying servers at the network edge. However, the proliferation of onboard sensors and the continual emergence of new applications have exacerbated the inadequacy of wireless spectrum resources and edge server resources, while the high mobility of vehicles reduces reliability in task processing, resulting in increased communication and task processing delays. To address these challenges, we propose a mobile-aware Many-to-Many Parallel (MTMP) offloading scheme that integrates: a) millimeter-wave (mmWave) and cellular vehicle-to-everything (C-V2X) to mitigate excessive communication delays; and b) leveraging the underutilized resources of surrounding vehicles and parallel offloading to mitigate excessive task processing delays. To minimize the average completion delay of all tasks, this paper formulates the objective as a min-max optimization problem and solves it using the maximum entropy method (MEM), the Lagrange multiplier method, and an iterative algorithm. Extensive experimental results demonstrate the superior performance of the proposed scheme in comparison with other baseline algorithms. Specifically, our proposal achieves a 47 % reduction in task completion delay under optimal conditions, a 31.3 % increase in task completion rate, and a 30 % decrease in program runtime compared to the worst-performing algorithm.