AoI Optimization in the UAV-Aided Traffic Monitoring Network Under Attack: A Stackelberg Game Viewpoint

Yang, Yaoqi; Wang, Weizheng; Liu, Lingjun; Dev, Kapal; Qureshi, Nawab Muhammad Faseeh

doi:10.1109/tits.2022.3157394

Cited by 35 publications

(9 citation statements)

References 20 publications

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“…To defend potential device compromised and sever signal interference during device-free localization, we propose a CNN-based attack defense device-free localization method in this paper. e multiple experiments verify that our proposed method can achieve better localization accuracy compared with other deep learning-based technique and defend most of the possible attacks [23,24]. In the future work, we will explore the popular CNN framework such as AlexNet or ResNet, which can maintain the localization accuracy under more sever circumstances.…”

Section: Discussionmentioning

confidence: 77%

CNN-Based Attack Defense for Device-Free Localization

Han

Lin

Wang

et al. 2022

Mobile Information Systems

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Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances.

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Section: Discussionmentioning

confidence: 77%

CNN-Based Attack Defense for Device-Free Localization

Han

Lin

Wang

et al. 2022

Mobile Information Systems

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“…Wang et al [ 19 ] proposed a game-based model to optimize the channel access-based AoI minimization problem in UAV-based mobile edge computing networks. Yang et al [ 20 ] proposed a game-based model for AoI optimization in the UAV-aided traffic monitoring network under attack. Akram et al [ 21 ] have developed a deep learning-based technique for Urdu short text clustering.…”

Section: Related Workmentioning

confidence: 99%

A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization

Rajabi Kouchi,

Oftadeh Balani,

Esmaeilpour

et al. 2023

Computational Intelligence and Neuroscience

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In this study, a new algorithm for recommending movies to viewers has been proposed. To do this, the suggested method employs data mining techniques. The proposed method includes three steps for generating recommendations: “preprocessing of user profile information,” “feature extraction,” and “recommendation.” In the first step of proposed method, the user information will be examined and transformed into a form that can be handled in the next phases. In the second step of the proposed method, user attributes are then extracted as a collection of their individual qualities, as well as the average rating of each user for various genres. The bee colony optimization algorithm is then used to select the optimal features. Finally, in the third step of the proposed method, the ratings of similar users are utilized to offer movies to the target user, and the similarities between various users are determined using the characteristics calculated for them, as well as the Euclidean distance criteria. The proposed method was evaluated using the MovieLens database, and its output was assessed in terms of precision and recall criteria; these results show that the proposed method will increase the precision by an average of 1.39% and the recall by 0.8% compared to the compared algorithms.

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“…Game theory is a study of mathematical models, and in particular interactive decisionmaking, where the outcome of each participant or "player" depends on the actions of all [8], is of interest in this work. In addition, Stackelberg is one of the game theory models that can be adopted to solve some UAV-related problems in smart cities, including traffic monitoring and communication [9]. In this game, one player serves as the leader, while the others act as followers.…”

Section: Introductionmentioning

confidence: 99%

Stackelberg Game Approach for Service Selection in UAV Networks

Hadjkouider

Kerrache

Korichi

et al. 2023

Sensors

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Nowadays, mobile devices are expected to perform a growing number of tasks, whose complexity is also increasing significantly. However, despite great technological improvements in the last decade, such devices still have limitations in terms of processing power and battery lifetime. In this context, mobile edge computing (MEC) emerges as a possible solution to address such limitations, being able to provide on-demand services to the customer, and bringing closer several services published in the cloud with a reduced cost and fewer security concerns. On the other hand, Unmanned Aerial Vehicle (UAV) networking emerged as a paradigm offering flexible services, new ephemeral applications such as safety and disaster management, mobile crowd-sensing, and fast delivery, to name a few. However, to efficiently use these services, discovery and selection strategies must be taken into account. In this context, discovering the services made available by a UAV-MEC network, and selecting the best services among those available in a timely and efficient manner, can become a challenging task. To face these issues, game theory methods have been proposed in the literature that perfectly suit the case of UAV-MEC services by modeling this challenge as a Stackelberg game, and using existing approaches to find the solution for such a game aiming at an efficient services’ discovery and service selection. Hence, the goal of this paper is to propose Stackelberg-game-based solutions for service discovery and selection in the context of UAV-based mobile edge computing. Simulations results conducted using the NS-3 simulator highlight the efficiency of our proposed game in terms of price and QoS metrics.

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AoI Optimization in the UAV-Aided Traffic Monitoring Network Under Attack: A Stackelberg Game Viewpoint

Cited by 35 publications

References 20 publications

CNN-Based Attack Defense for Device-Free Localization

CNN-Based Attack Defense for Device-Free Localization

A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization

Stackelberg Game Approach for Service Selection in UAV Networks

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