TANTO: An Effective Trust-Based Unmanned Aerial Vehicle Computing System for the Internet of Things

Jing, Bai; Zeng, Zhiwen; Wang, Tian; Zhang, Shaobo; Xiong, Neal N.; Liu, Anfeng

doi:10.1109/jiot.2022.3150765

Cited by 27 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the single-UAV-based model, the UAV follows the trajectory based on the task generation of IoT devices. The IoT devices broadcast task offloading notifications, facilitating the UAV to know where tasks need to offload without aimless search, which can effectively minimize the UAV’s flight distance and task completion latency [ 59 ]. IoT devices offload their tasks to the UAV as the UAV hovers over them along its trajectory.…”

Section: Background Studymentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

View full text Add to dashboard Cite

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

show abstract

Section: Background Studymentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Trust management mechanisms have been considered by some authors to detect malicious UAVs [22][23][24]. Keshavarz et al [22] proposed a trust monitoring framework that can detect DDoS and GPS spoofing attacks by observing task completion, path deviation, and energy consumption characteristics of UAVs.…”

Section: Secure Mechanisms In Multi-uav Systemsmentioning

confidence: 99%

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Medhi,

Liu,

Wang

et al. 2023

Information

View full text Add to dashboard Cite

Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness.

show abstract

“…The subject of security via trust was examined in [72]. A UAV could act either as a relay interconnecting resourcelimited IoT devices with an edge server or as a MEC node itself.…”

Section: A Uavs As Mec Serversmentioning

confidence: 99%

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

et al. 2022

View full text Add to dashboard Cite

As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software-and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.INDEX TERMS Blockchain, Internet of Things (IoT), Machine Learning (ML), Mobile Edge Computing (MEC), Physical-Layer Security (PLS), Unmanned Aerial Vehicle (UAV).

show abstract

TANTO: An Effective Trust-Based Unmanned Aerial Vehicle Computing System for the Internet of Things

Cited by 27 publications

References 47 publications

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

Contact Info

Product

Resources

About