Security-Aware Synthesis of Human-UAV Protocols

Elfar, Mahmoud; Zhu, Haibei; Cummings, Mary L.; Pajić, Miroslav

doi:10.1109/icra.2019.8794385

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Since automatons are asset compelled and constrained, adaptable coordination procedures such those proposed can improve the system execution keeping up higher proficiency in the assignment execution. Elfar et al (2019) combined cooperation protocol for human-unmanned aircraft vehicle (H-UAV) order and control frameworks, where the human administrator helps in verifying the UAV by discontinuously performing geo area errands to affirm its announced area. They originally exhibited a stochastic game-based model for the framework that records for both the administrator and a foe fit for propelling stealthy false-information infusion at-tacks, making the UAV veer off from its way.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid and Robust Delay and Link Stability Aware (DLSA) Routing Protocol for Unmanned Aerial Ad-hoc Networks (UAANETs)

Hussain

2021

Preprint

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Unmanned Aerial Ad-hoc Network (UAANET) also knows as by the name of Flying Ad-hoc NETwork (FANET) is a new class of Mobile Ad-hoc NETwork (MANET) in which the nodes move in three dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by pre-defined mechanism which involve no human personnel. Due to high mobility of nodes and dynamic topology, the link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with highest threshold value by maximizing Packet Delivery Ratio (PDR) and minimizing End-to-End Delay (E2ED). In this research, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing (DPTR) and Link Stability Estimation-based Routing (LEPR) FANET’s existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features in collaborative data forwarding and link stability by merging the positive features of DPTR and LEPR. The link stability via maximum threshold value has been introduced to acquire and select the most feasible route from source to destination. The simulation was carried out using Matrix Laboratory (MATLAB) tool for the concerned research. Simulation results have showed improved performance of the proposed protocol in contrast to the selected existing ones in terms of E2ED, PDR, Network Lifetime and Transmission Loss. Average E2ED in (milliseconds) of DLSA measured 0.457, while DPTR was 1.492 and LEPR was 1.006. Similarly, Average PDR (in %age) of DLSA measured 3.106, while DPTR was 2.303 and LEPR was 0.682. Average Network Lifetime in (seconds) for DLSA measured 62.141, while DPTR was 23.036 and LEPR was 27.298. Average Transmission Loss in (dBm) for DLSA measured 0.975, while DPTR was 1.053 and LEPR was 1.227.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid and Robust Delay and Link Stability Aware (DLSA) Routing Protocol for Unmanned Aerial Ad-hoc Networks (UAANETs)

Hussain

2021

Preprint

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“…Following the system described in Example 2, we now consider the composed HIG G H = M adv M uav M as shown in Fig. 4; the HIG-based model incorporates standard models of a UAV (M uav ), an adversary (M adv ), and a geolocation-task advisory system (M as ) (e.g., as introduced in [11,12]). Here, the probability of a successful detection p(v T , v B ) is a function of both the location the UAV believes to be its current location (v B ) as well as the ground truth location that the UAV actually occupies (v T ).…”

Section: Stochastic Gamesmentioning

confidence: 99%

Security-Aware Synthesis Using Delayed-Action Games

Elfar

Wang

Pajić

2019

Computer Aided Verification

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Stochastic multiplayer games (SMGs) have gained attention in the field of strategy synthesis for multi-agent reactive systems. However, standard SMGs are limited to modeling systems where all agents have full knowledge of the state of the game. In this paper, we introduce delayed-action games (DAGs) formalism that simulates hiddeninformation games (HIGs) as SMGs, where hidden information is captured by delaying a player's actions. The elimination of private variables enables the usage of SMG off-the-shelf model checkers to implement HIGs. Furthermore, we demonstrate how a DAG can be decomposed into subgames that can be independently explored, utilizing parallel computation to reduce the model checking time, while alleviating the state space explosion problem that SMGs are notorious for. In addition, we propose a DAG-based framework for strategy synthesis and analysis. Finally, we demonstrate applicability of the DAG-based synthesis framework on a case study of a human-on-the-loop unmanned-aerial vehicle system under stealthy attacks, where the proposed framework is used to formally model, analyze and synthesize security-aware strategies for the system.

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“…Unmanned airborne systems (UASs), especially small unmanned airborne systems (S-UASs), are used in military reconnaissance, search, and intelligence work in areas that are not safe for humans, as well as in remote sensing areas; data collection is used due to its data storage, analysis, and decision capabilities [ 9 ]. Work performed in areas that are not safe for humans and difficult to access can be performed thanks to human–UAV interactions [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. One of the areas where human UAV–interactions are used is computer-vision-based hand gesture recognition applications, which enable users to determine hand gestures using pattern recognition algorithms [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…As UAVs can work alone or move in a herd, they have achieved successful results in challenging missions [ 11 , 29 ]. Work that needs to be performed in unsafe and difficult-to-access areas can now be done using human–UAV interactions [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 43 ]. Human–UAV interactions are only used for information exchange in some applications, while some applications employ physical contact or force exchange.…”

Section: Introductionmentioning

confidence: 99%

“…Human–UAV interactions are only used for information exchange in some applications, while some applications employ physical contact or force exchange. As can be seen in Figure 1 , this interaction can be performed using a software interface alone or by using physical [ 11 ], tactile [ 12 ], auditory [ 44 ], and human kinetics techniques or creating a specific rule table [ 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

Müezzinoğlu

Karaköse

2021

Sensors

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The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.

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Security-Aware Synthesis of Human-UAV Protocols

Cited by 8 publications

References 23 publications

A Hybrid and Robust Delay and Link Stability Aware (DLSA) Routing Protocol for Unmanned Aerial Ad-hoc Networks (UAANETs)

A Hybrid and Robust Delay and Link Stability Aware (DLSA) Routing Protocol for Unmanned Aerial Ad-hoc Networks (UAANETs)

Security-Aware Synthesis Using Delayed-Action Games

An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves

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