On-Demand Routing for Urban VANETs using Cooperating UAVs

Vehicular ad hoc networks (VANETs) are characterized by frequent routing path failures due to the high mobility caused by the sudden changes of the direction of vehicles. The routing paths between two different vehicles should be established with this challenge in mind. Stability and connectedness are a mandatory condition to ensure a robust and reliable data delivery. The idea behind this work is to exploit a new reactive routing technique to provide regulated and well-connected routing paths. Unmanned Aerial Vehicles (UAVs) or what are referred to as drones can be both involved in the discovery process and be full members in these discovered paths in order to avoid possible disconnections on the ground when the network is sparsely connected. The different tests of this technique are performed based on NS-2 simulator and the outcomes are compared with those of related on-demand routing protocols dedicated for VANETs. Interesting results are distinguished showing a reduced end-to-end delay and a high delivery ratio, which proving that this heterogeneous communication between vehicles and UAVs is able to extend the network connectivity.Comment: 6 pages, 7 figures, conferenc

Leveraging Communicating UAVs for Emergency Vehicle Guidance in Urban Areas

International audienceThe response time to emergency situations in urban areas is considered as a crucial key in limiting material damage or even saving human lives. Thanks to their "bird's eye view" and their flexible mobility, Unmanned Aerial Vehicles (UAVs) can be a promising candidate for several vital applications. Under these perspectives, we investigate the use of communicating UAVs to detect any incident on the road, provide rescue teams with their exact locations, and plot the fastest path to intervene, while considering the constraints of the roads. To efficiently inform the rescue services, a robust routing scheme is introduced to ensure a high level of communication stability based on an efficient backbone, while considering both the high mobility and the restricted energy capacity of UAVs. This allows both predicting any routing path breakage prior to its occurrence, and carrying out a balanced energy consumption among UAVs. To ensure a rapid intervention by rescue teams, UAVs communicate in an ad hoc fashion with existing vehicles on the ground to estimate the fluidity of the roads. Our system is implemented and evaluated through a series of experiments. The reported results show that each part of the system reliably succeeds in achieving its planned objective

UAV-Assisted Reactive Routing for Urban VANETs

International audienceVehicular ad hoc networks (VANETs) are characterized by frequent path failures due to the high mobility caused by the sudden changes of vehicles direction. The routing paths between two different vehicles should be established with this challenge in mind. It must be stable and well connected in order to guarantee a reliable and safe delivery of packets. The aim of this work is to present a new reactive routing technique providing effective and well-regulated communication paths. These discovered paths are created based on a robust flooding discovery process involving UAVs (Un-manned Aerial Vehicles) to ensure the connectivity when the network is sparsely connected. The evaluation of this technique is performed using NS-2 simulator and its performances are compared with on-demand protocols dedicated for VANET. Simulation results show clearly that our approach gives interesting outcomes ensuring a high delivery ratio with a minimum delay. This hybrid communication between the vehicles and UAVs is attractive to initiate more smart connected nodes in the near future

Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

A trust management system (TMS) is an integral component of any IoT network. A reliable trust management system must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviours of network entities and subsequently judge the reliability of different network entities in IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks, and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this paper, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, suck as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94\% mitigation rate in various network scenarios.Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

U2RV: UAV-assisted reactive routing protocol for VANETs

When it comes to keeping the data routing robust and effective in Vehicular Ad hoc Networks (VANETs), stable and durable connectivity constitutes the keystone to ensure successful point-to-point communication. Since VANETs can comprise all kinds of mobile vehicles moving and changing direction frequently, this may result in frequent link failures and network partitions. Moreover, when VANETs are deployed in a city environment, another problem arises, that is, the existing obstructions (e.g., buildings, trees, hoppers, etc.) preventing the line-of-sight between vehicles, thus degrading wireless transmissions. Therefore, it is more complicated to design a routing technique that adapts to frequent changes in the topology. In order to settle all these problems, in this work, we design a flooding scheme that automatically reacts at each topology variation while overcoming the present obstacles while exchanging data in ad hoc mode with drones that are commonly called Unmanned Aerial Vehicles (UAVs). Also, the aim of this work is to explore well-regulated routing paths providing a long lifetime connectivity based on the amount of traffic and the expiration time of each discovered path, respectively. A set of experiments is carried out using simulation, and the outcomes are confronted with similar protocols based on a couple of metrics. The results clearly show that the assistance of UAVs to vehicles is capable to provide high delivery ratios and low delivery delays while efficiently extending the network connectivity

BRT: Bus-based Routing Technique in Urban Vehicular Networks

International audienceRouting data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to RoadSide Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis

UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication

[EN] Ensuring the desired level of security is an important issue in all communicating systems, and it becomes more challenging in wireless environments. Flying Ad Hoc Networks (FANETs) are an emerging type of mobile network that is built using energy-restricted devices. Hence, the communications interface used and that computation complexity are additional factors to consider when designing secure protocols for these networks. In the literature, various solutions have been proposed to ensure secure and reliable internode communications, and these FANET nodes are known as Unmanned Aerial Vehicles (UAVs). In general, these UAVs are often detected as malicious due to an unintentional misbehavior related to the physical features of the UAVs, the communication mediums, or the network interface. In this paper, we propose a new context-aware trust-based solution to distinguish between intentional and unintentional UAV misbehavior. The main goal is to minimize the generated error ratio while meeting the desired security levels. Our proposal simultaneously establishes the inter-UAV trust and estimates the current context in terms of UAV energy, mobility pattern, and enqueued packets, in order to ensure full context awareness in the overall honesty evaluation. In addition, based on computed trust and context metrics, we also propose a new inter-UAV packet delivery strategy. Simulations conducted using NS2.35 evidence the efficiency of our proposal, called UNION., at ensuring high detection ratios > 87% and high accuracy with reduced end-to-end delay, clearly outperforming previous proposals known as RPM, T-CLAIDS, and CATrust.This research is partially supported by the United Arab Emirates University (UAEU) under Grant no. 31T065.Barka, E.; Kerrache, CA.; Lagraa, N.; Lakas, A.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J. (2018). UNION: A Trust Model Distinguishing Intentional and Unintentional Misbehavior in Inter-UAV Communication. Journal of Advanced Transportation. 1-12. https://doi.org/10.1155/2018/7475357S112Ghazzai, H., Ben Ghorbel, M., Kadri, A., Hossain, M. J., & Menouar, H. (2017). Energy-Efficient Management of Unmanned Aerial Vehicles for Underlay Cognitive Radio Systems. IEEE Transactions on Green Communications and Networking, 1(4), 434-443. doi:10.1109/tgcn.2017.2750721Sharma, V., & Kumar, R. (2016). Cooperative frameworks and network models for flying ad hoc networks: a survey. Concurrency and Computation: Practice and Experience, 29(4), e3931. doi:10.1002/cpe.3931Sun, J., Wang, W., Kou, L., Lin, Y., Zhang, L., Da, Q., & Chen, L. (2017). A data authentication scheme for UAV ad hoc network communication. The Journal of Supercomputing, 76(6), 4041-4056. doi:10.1007/s11227-017-2179-3He, D., Chan, S., & Guizani, M. (2017). Drone-Assisted Public Safety Networks: The Security Aspect. IEEE Communications Magazine, 55(8), 218-223. doi:10.1109/mcom.2017.1600799cmSeong-Woo Kim, & Seung-Woo Seo. (2012). Cooperative Unmanned Autonomous Vehicle Control for Spatially Secure Group Communications. IEEE Journal on Selected Areas in Communications, 30(5), 870-882. doi:10.1109/jsac.2012.120604Singh, A., Maheshwari, M., Nikhil, & Kumar, N. (2011). Security and Trust Management in MANET. Communications in Computer and Information Science, 384-387. doi:10.1007/978-3-642-20573-6_67Kerrache, C. A., Calafate, C. T., Cano, J.-C., Lagraa, N., & Manzoni, P. (2016). Trust Management for Vehicular Networks: An Adversary-Oriented Overview. IEEE Access, 4, 9293-9307. doi:10.1109/access.2016.2645452Li, W., & Song, H. (2016). ART: An Attack-Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems, 17(4), 960-969. doi:10.1109/tits.2015.2494017Raghunathan, V., Schurgers, C., Sung Park, & Srivastava, M. B. (2002). Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40-50. doi:10.1109/79.985679Feeney, L. M. (2001). Mobile Networks and Applications, 6(3), 239-249. doi:10.1023/a:1011474616255De Rango, F., Guerriero, F., & Fazio, P. (2012). Link-Stability and Energy Aware Routing Protocol in Distributed Wireless Networks. IEEE Transactions on Parallel and Distributed Systems, 23(4), 713-726. doi:10.1109/tpds.2010.160Hyytia, E., Lassila, P., & Virtamo, J. (2006). Spatial node distribution of the random waypoint mobility model with applications. IEEE Transactions on Mobile Computing, 5(6), 680-694. doi:10.1109/tmc.2006.86Wang, Y., Chen, I.-R., Cho, J.-H., Swami, A., Lu, Y.-C., Lu, C.-T., & Tsai, J. J. P. (2018). CATrust: Context-Aware Trust Management for Service-Oriented Ad Hoc Networks. IEEE Transactions on Services Computing, 11(6), 908-921. doi:10.1109/tsc.2016.2587259Kumar, N., & Chilamkurti, N. (2014). Collaborative trust aware intelligent intrusion detection in VANETs. Computers & Electrical Engineering, 40(6), 1981-1996. doi:10.1016/j.compeleceng.2014.01.00