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

Wireless Communications Challenges to Flying Ad Hoc Networks (FANET)

The increasing demand for Internet access from more and more different devices in recent years has provided a challenge for companies and the academic community to research and develop new solutions that support the increasing flow in the network, applications that require very low latencies and more dynamic and scalable infrastructures, in this context the mobile ad hoc networks (MANETs) emerged as a possible solution and applying this technology in unmanned aerial vehicles (UAVs) was developed the flying ad hoc networks (FANETs) which are wireless networks independent, its main characteristics are to have high mobility, scalability for different applications and scenarios and robustness to deal with possible communication failures. However, they still have several constraints such as limited flight time of UAVs and routing protocols that are capable of supporting network dynamics. To analyze this scenario, two simulations were developed where it was possible to observe the behavior of FANET with different routing protocols both during data transmission and video transmission. The results show that the choice of the best routing protocol must take into account the mobility of the UAVs and the necessary communication priority in the network

Resources Efficient Dynamic Clustering Algorithm for Flying Ad-Hoc Network

Unmanned Aerial Vehicles, often known as UAVs, are connected in the form of a Flying Ad-hoc Network, or FANET, and placed to use in a variety of applications to carry out efficient remote monitoring. Their great mobility has an adverse effect on their energy consumption, which in turn has a detrimental effect on the network's stability and the effectiveness of communication. To manage the very dynamic flying behavior of UAVs and to keep the network stable, novel clustering algorithms have been implemented. In this context, a novel clustering technique is developed to meet the rapid mobility of UAVs and to offer safe inter-UAV distance, reliable communication, and an extended network lifespan. It also provides a detailed analysis of the similarities and distinctions between AODV, AOMDV, DSDV, and DumbAgent.The performance of these protocols is analyzed using the NS-2 simulator. The simulation results are shown in our study with AODV, AOMDV, DSDV, and DumbAgent. The results of the simulation make it abundantly evident that the AODV routing protocol outperforms the other routing protocols DSDV, AOMDV, and DumbAgent in terms of the number of packets lost, the amount of throughput achieved, the amount of routing overhead generated, and the amount of delay

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

A Tri-Phases Message Oriented Trust Model in FANET

With advantages of convenience and flexibility, Unmanned Aerial Vehicles (UAVs) are widely applied in various fields, such as inspection, agriculture, transportation, and so on. However, due to characteristics of Flying Ad Hoc Network (FANET) consisting of UAVs, such as dynamic topology and limited bandwidth, the security of messages is threatened by cyber attacks. A Tri-Phases Message Oriented Trust Model for FANET is proposed, called TPMOTM, to secure messages. In the proposed work, the message collection process is divided into three phases, including message generation, message transmission, and message integration. In each phase, by analyzing potential attacks and the circumstance of network, the TPMOTM quantifies the specific detection factors to obtain the trust value of messages, including timeliness, detection accuracy, message error rate, relay performance, authentication result, and message loss rate. After messages have been received by the Ground Station (GS), the GS integrates all messages in relation to the detected event, employing the message trust value as the weight, to obtain the state of detected event. Extensive simulations are conducted based on the Opportunistic Network Environment (ONE) simulator, with attack message detection rates reaching up to 95% ( ±0.89% ) and event detection accuracy rates typically above 85% ( ±1.21% ).Wuhan Knowledge Innovation Program (Grant Number: 2022010801010117); Hubei Province International Science and Technology Collaboration Program (Grant Number: 2023EHA044); MIC/SCOPE (Grant Number: #JP235006102)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization

Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies with wide ranging applications, including surveillance, mapping, remote sensing, search and rescue, and disaster management. As sophisticated Unmanned Aerial Vehicle (UAV) increasingly operate in collaborative swarms, joint optimization challenges arise, such as flight trajectories, scheduling, altitude, Aerial Base Stations (ABS), energy harvesting, power transfer, resource allocation, and power consumption. However, the widespread adoption of UAV networks has been hindered by challenges related to optimal Three-Dimensional (3D) deployment, trajectory optimization, wireless and computational resource allocation, and limited flight durations when operating as ABSs. Crucially, the broadcast nature of UAV-assisted wireless networks renders them susceptible to privacy and security threats such as Distributed Denial-of-Service (DDoS) replay, impersonation, message injection, spoofing, malware infection, eavesdropping, and line of-interference attacks. This study aims to address these privacy and security challenges by leveraging blockchain technology’s potential to secure data and delivery in UAV communication networks. With amalgamation of blockchain, this study seeks to harness its inherent immutability and cryptographic properties to ensure secure and tamper-proof data transmission, promote trust and transparency among stakeholders, enable automated Smart Contract (SC) for secure delivery, and facilitate standardization and interoperability across platforms. Specifically, blockchain can secure UAV network privacy and security through data privacy and integrity, secure delivery and tracking, access control, identity management, and resilience against cyber-attacks. Furthermore, this study explores the synergies among blockchain, UAV networks, and Federated Learning (FL) for privacy-preserving intelligent applications in healthcare and wireless networks. FL enables collaborative training of Machine Learning (ML) models without sharing raw data, ensuring data privacy. By integrating FL with blockchain-assisted UAV networks, this study aims to revolutionize future intelligent applications, particularly in time-sensitive and privacy-critical domains. Overall, this thesis contributes to the field by providing a comprehensive analysis of integrating blockchain, FL, and UAV networks, beyond Fifth-Generation (5G) communication networks. It addresses privacy and security concerns related to data and delivery, thereby enabling secure, reliable, and intelligent applications in various sectors

Trustworthiness Mechanisms for Long-Distance Networks in Internet of Things

Aquesta tesi té com a objectiu aconseguir un intercanvi de dades fiable en un entorn hostil millorant-ne la confiabilitat mitjançant el disseny d'un model complet que tingui en compte les diferents capes de confiabilitat i mitjançant la implementació de les contramesures associades al model. La tesi se centra en el cas d'ús del projecte SHETLAND-NET, amb l'objectiu de desplegar una arquitectura d'Internet de les coses (IoT) híbrida amb comunicacions LoRa i d'ona ionosfèrica d'incidència gairebé vertical (NVIS) per oferir un servei de telemetria per al monitoratge del “permafrost” a l'Antàrtida. Per complir els objectius de la tesi, en primer lloc, es fa una revisió de l'estat de l'art en confiabilitat per proposar una definició i l'abast del terme de confiança. Partint d'aquí, es dissenya un model de confiabilitat de quatre capes, on cada capa es caracteritza pel seu abast, mètrica per a la quantificació de la confiabilitat, contramesures per a la millora de la confiabilitat i les interdependències amb les altres capes. Aquest model permet el mesurament i l'avaluació de la confiabilitat del cas d'ús a l'Antàrtida. Donades les condicions hostils i les limitacions de la tecnologia utilitzada en aquest cas d’ús, es valida el model i s’avalua el servei de telemetria a través de simulacions en Riverbed Modeler. Per obtenir valors anticipats de la confiabilitat esperada, l'arquitectura proposada es modela per avaluar els resultats amb diferents configuracions previ al seu desplegament en proves de camp. L'arquitectura proposada passa per tres principals iteracions de millora de la confiabilitat. A la primera iteració, s'explora l'ús de mecanismes de consens i gestió de la confiança social per aprofitar la redundància de sensors. En la segona iteració, s’avalua l’ús de protocols de transport moderns per al cas d’ús antàrtic. L’última iteració d’aquesta tesi avalua l’ús d’una arquitectura de xarxa tolerant al retard (DTN) utilitzant el Bundle Protocol (BP) per millorar la confiabilitat del sistema. Finalment, es presenta una prova de concepte (PoC) amb maquinari real que es va desplegar a la campanya antàrtica 2021-2022, descrivint les proves de camp funcionals realitzades a l'Antàrtida i Catalunya.Esta tesis tiene como objetivo lograr un intercambio de datos confiable en un entorno hostil mejorando su confiabilidad mediante el diseño de un modelo completo que tenga en cuenta las diferentes capas de confiabilidad y mediante la implementación de las contramedidas asociadas al modelo. La tesis se centra en el caso de uso del proyecto SHETLAND-NET, con el objetivo de desplegar una arquitectura de Internet de las cosas (IoT) híbrida con comunicaciones LoRa y de onda ionosférica de incidencia casi vertical (NVIS) para ofrecer un servicio de telemetría para el monitoreo del “permafrost” en la Antártida. Para cumplir con los objetivos de la tesis, en primer lugar, se realiza una revisión del estado del arte en confiabilidad para proponer una definición y alcance del término confiabilidad. Partiendo de aquí, se diseña un modelo de confiabilidad de cuatro capas, donde cada capa se caracteriza por su alcance, métrica para la cuantificación de la confiabilidad, contramedidas para la mejora de la confiabilidad y las interdependencias con las otras capas. Este modelo permite la medición y evaluación de la confiabilidad del caso de uso en la Antártida. Dadas las condiciones hostiles y las limitaciones de la tecnología utilizada en este caso de uso, se valida el modelo y se evalúa el servicio de telemetría a través de simulaciones en Riverbed Modeler. Para obtener valores anticipados de la confiabilidad esperada, la arquitectura propuesta es modelada para evaluar los resultados con diferentes configuraciones previo a su despliegue en pruebas de campo. La arquitectura propuesta pasa por tres iteraciones principales de mejora de la confiabilidad. En la primera iteración, se explora el uso de mecanismos de consenso y gestión de la confianza social para aprovechar la redundancia de sensores. En la segunda iteración, se evalúa el uso de protocolos de transporte modernos para el caso de uso antártico. La última iteración de esta tesis evalúa el uso de una arquitectura de red tolerante al retardo (DTN) utilizando el Bundle Protocol (BP) para mejorar la confiabilidad del sistema. Finalmente, se presenta una prueba de concepto (PoC) con hardware real que se desplegó en la campaña antártica 2021-2022, describiendo las pruebas de campo funcionales realizadas en la Antártida y Cataluña.This thesis aims at achieving reliable data exchange over a harsh environment by improving its trustworthiness through the design of a complete model that takes into account the different layers of trustworthiness and through the implementation of the model’s associated countermeasures. The thesis focuses on the use case of the SHETLAND-NET project, aiming to deploy a hybrid Internet of Things (IoT) architecture with LoRa and Near Vertical Incidence Skywave (NVIS) communications to offer a telemetry service for permafrost monitoring in Antarctica. To accomplish the thesis objectives, first, a review of the state of the art in trustworthiness is carried out to propose a definition and scope of the trustworthiness term. From these, a four-layer trustworthiness model is designed, with each layer characterized by its scope, metric for trustworthiness accountability, countermeasures for trustworthiness improvement, and the interdependencies with the other layers. This model enables trustworthiness accountability and assessment of the Antarctic use case. Given the harsh conditions and the limitations of the use technology in this use case, the model is validated and the telemetry service is evaluated through simulations in Riverbed Modeler. To obtain anticipated values of the expected trustworthiness, the proposal has been modeled to evaluate the performance with different configurations prior to its deployment in the field. The proposed architecture goes through three major iterations of trustworthiness improvement. In the first iteration, using social trust management and consensus mechanisms is explored to take advantage of sensor redundancy. In the second iteration, the use of modern transport protocols is evaluated for the Antarctic use case. The final iteration of this thesis assesses using a Delay Tolerant Network (DTN) architecture using the Bundle Protocol (BP) to improve the system’s trustworthiness. Finally, a Proof of Concept (PoC) with real hardware that was deployed in the 2021-2022 Antarctic campaign is presented, describing the functional tests performed in Antarctica and Catalonia

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)