Ad Hoc Networks最新文献

Opportunistic Mobile Ad Hoc Networks (MANETs) offer versatile solutions in contexts where the Internet is unavailable. These networks facilitate the transmission between endpoints using a store-carry-forward strategy, thereby allowing information to be stored during periods of disconnection. Consequently, selecting the next hop in the routing process becomes a significant challenge for nodes, particularly because of its impact on Quality of Service (QoS). Therefore, routing strategies are crucial in opportunistic MANETs; however, their deployment and evaluation in real scenarios can be challenging. In response to this context, this paper introduces a monitoring software-driven tool designed to evaluate the QoS of routing algorithms in physical opportunistic MANETs. The implementation and its components are detailed, along with a case study and the outcomes provided by an implementation of the proposed solution. The results demonstrate the effectiveness of the implementation in enabling the analysis of routing protocols in real scenarios, highlighting significant differences with simulation results: mobility patterns in simulations tend to be inaccurate and overly optimistic, leading to a higher delivery probability and lower latency than what is observed in the real testbed.

The dynamic nature of the atmosphere, especially wind gust, poses a crucial challenge to efficient and real-time drone operations. This article presents a novel MQTT based software-defined drone network for trajectory correction of drone flights in gusty wind conditions using Glowworm Swarm Optimization (GSO). By imposing the GSO to the software-defined drone network, our proposed model SoftWind has optimized the navigation and control capabilities of drones by correcting the trajectories in a gusty wind environment. We have analyzed the trajectories and convergence of drones due to wind gusts. As wind disturbances affect the trajectories of drones, we have corrected it by our trajectory correction model and evaluated the direction of the drones must fly to mitigate the wind gust and the resultant velocity compared to the no-wind environment. This study analyzed the trajectories of 100 drone flights due to various wind gust lengths (i.e., 40 m, 10 m, 6 m, and 3 m) for a fixed gust amplitude of 15 m/s and various gust amplitude (i.e., 0 m/s, 5 m/s, 15 m/s, and 40 m/s) for a fixed gust length 5 m. We observed that all the drones are converged to a single point due to low gust length (≤ 5 m) and high gust amplitude (≥ 35 m/s). It is also found that the direction of the drone must fly 28.87°. East of South to mitigate the effect of wind gusts having 10 m gust length and 15 m/s gust amplitude and the resultant velocity of the drone is 22.38 m/s. The result shows that SoftWind reduces the convergence time by 26 %-54 % as compared to other existing models.

This paper introduces a new priority-aware routing protocol for mobile Ad-hoc networks to be utilized in emergencies, which is based on AODV. Mobile Ad-hoc networks find extensive use in various domains including military operations, environmental monitoring, healthcare, disaster response, smart transportation systems, unmanned aerial vehicles, and smart homes. During emergencies, communication can be severely restricted or even impossible due to the congestion of physical communication channels and unexpected technical failures in the infrastructure. Mobile Ad-hoc networks offer a solution to maintain continuous and reliable communication under such challenging conditions. In emergency scenarios, it is crucial for any node in the network to promptly deliver urgent messages to the intended destination, especially when certain nodes require ongoing active communication. The proposed routing protocol effectively addresses this requirement through its priority-aware mechanisms. The protocol ensures that nodes not involved in emergency tasks select the least congested route to prevent any delays or disruptions in the transmission of critical emergency data. This approach guarantees seamless communication for emergency nodes while allowing non-emergency nodes to communicate with each other as well. The study proposed in this paper introduces a new priority-aware routing protocol based on AODV for mobile Ad-hoc networks in emergencies. The packet transmission ratio of emergency nodes within the network is improved while maintaining the overall network performance unaffected. The adoption of proposed mechanisms to enhance performance does not necessitate an expansion in the size of data and control packets. These mechanisms do not inflict any supplementary latency or incur packet loss expenses on the network. The proposed protocol has been implemented and evaluated using ns-3 simulation software across various emergency scenarios. The results show that emergency nodes using the proposed protocol, achieve better packet delivery ratios compared to the original AODV, DSR, P-AODV, and AOMDV protocols, with improvements of 10.8%, 15.9%, 6.2%, and 5.9% respectively. This improvement in the packet delivery ratio for emergency data traffic is achieved without causing any disruptions in the overall network communication flow.

This paper proposes a new method for predicting downlink traffic volume in mobile networks, aiming to minimize overprovisioning while meeting specified service-level agreement (SLA) violation rates. We introduce a multivariate and multi-step prediction approach and compare four machine learning (ML) architectures: long short-term memory (LSTM), convolutional neural network (CNN), transformer, and light gradient-boosting machine (LightGBM). Our models predict up to 24 steps ahead and are evaluated under both single-step and multi-step conditions. Additionally, we propose parametric loss functions to adhere to SLA violation rate constraints.

Our results emphasize the importance of using parametric loss functions to meet SLA constraints. We discovered that LSTM when paired with our custom multivariate feature sets, outperforms the transformer architecture in short-term forecasting up to 4 h ahead. For these short-term predictions, we demonstrate that methods based on domain knowledge, like our custom feature sets combined with simpler models such as LSTM, surpass more complex models like transformers. However, for long-term forecasting (8 to 24 h ahead), transformers outperform all other models.

Compared with upper layer authentication, physical layer authentication (PLA) is essential in unmanned Industrial Internet of Things (IIoT) scenarios, owing to its low complexity and lightweight. However, in dynamic environments, as the amount of users expands, the accuracy of single-attribute-based authentication decreases drastically, which becomes an urgent issue for IIoT. Accordingly, this paper proposes a novel multi-attribute-based convolutional attention neural network (CANN) for multiuser PLA. Using characteristics such as amplitude, phase, and delay, the multiple attributes from a real industrial scene are first constructed into three-dimensional matrices fed into CANN. Then, attention blocks are designed to learn the correlation between attributes and extract the attribute parts that are more instrumental in the CANN to improve authentication accuracy. In addition, to avoid confusing multiple users, a center confidence loss is introduced, which adaptively adjusts the weight of the center loss and works together with the softmax loss to train the CANN. The effectiveness of the proposed CANN-based multiuser PLA and center confidence loss is supported by experimental results. Compared with the recently proposed latent perturbed convolutional neural network (LPCNN), the CANN-based scheme improves the authentication accuracy by 8.11%, which is superior to the existing learning-based approaches. As the CANN is further trained with the loss function that combines center confidence loss, the authentication accuracy can be improved by at least 2.22%.

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its $ϵ$-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.

Identifying individuals with similar behaviors and mobility patterns has become essential to improving the functioning of urban services. However, since these patterns can vary over time, such identification needs to be done periodically. Furthermore, once mobility data expresses the routine of individuals, privacy must be guaranteed. In this work, we propose a framework for periodically detecting and grouping individuals with behavioral similarities into communities. To accomplish this, we built an autoencoder model to extract spatio-temporal mobility features from raw user data at periodic intervals. We used Federated Learning (FL) as a training approach to preserve privacy and alleviate time-consuming training and communication costs. To determine the number of communities without risking an arbitrary number, we compared the choices of two probabilistic methods, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Since the communities are updated periodically, we also analyzed the impact of aged samples on the proposed framework. Finally, we compared the performance of our FL-based solution to a centralized training approach. We analyzed similarity and dissimilarity metrics on mobility samples and the contact time of individuals in three different scenarios. Our results indicate that AIC outperforms BIC when choosing the number of communities, although both satisfy the evaluation metrics. We also found that using older samples benefits more complex spatio-temporal scenarios. Finally, no significant losses were detected when compared to a centralized training approach, reinforcing the advantages of using the FL-based method.

Traditional ground-based illumination equipment is limited in mobility and light source height, making it difficult to adapt to diverse living scenarios such as camping that require quick and flexible illumination solutions. With the rapid development of Unmanned Aerial Vehicle (UAV) technology, particularly in illumination services, UAVs have demonstrated unique advantages. Addressing the inadequacies of conventional illumination, this study proposes a prototype of an autonomously deployed illumination system based on the RoboMaster Tello Talent (Tello) UAV, designed to provide quick and flexible on-site illumination solutions. The system’s design encompasses three complementary modules to enhance its overall functionality and efficiency. Firstly, the illumination module equips the Tello UAV with a specialized illumination extension, ensuring flight stability and effective illumination. Secondly, the addressing module employs iterative algorithms to identify optimal UAV deployment locations and precisely plan flight paths. Lastly, the flight control module, guided by the results from the addressing module, scripts flight commands, integrates with the Tello UAV’s Application Programming Interface (API), and executes flight plans optimized for path efficiency, ensuring the UAV quickly and accurately reaches designated locations, coordinating with the illumination module to deliver effective illumination. Experimental results demonstrate that the proposed illumination system can swiftly respond to various user demands, autonomously deploy UAVs to optimal illumination positions, and provide high-quality service.