An SAT‐Based Method to Multithreaded Program Verification for Mobile Crowdsourcing Networks

Zhang, Long; Qu, Wanxia; Huo, Yinjia; Guo, Yang; Li, Sikun

doi:10.1155/2018/3193974

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…The trajectory prediction module in our anti-spoofing model plays the role of navigation after the target UAV is affected by deception signal, so we expect the prediction accuracy to be as high as possible. On the same dataset, in addition to linear regression, we also try to use neural network, LSTM, in the selection of trajectory prediction module [32]. The result is inferior to the current linear regression.…”

Section: Comparison Of Different Methods' Performance For Uavmentioning

confidence: 99%

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

Meng

Yang²,

Ren³

et al. 2021

Wireless Communications and Mobile Computing

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A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

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Section: Comparison Of Different Methods' Performance For Uavmentioning

confidence: 99%

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

Meng

Yang²,

Ren³

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

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“…Monitoring the site from proximity. 3D map of the infrastructure under fire along with the position and extent of fire will help the determination of escape routes (Zhang et al 2018)for inmates in real-time, which will be communicated to entrapped people. Thus, presenting it as creative fire detecting, monitoring, and rescuing mode.…”

Section: Introductionmentioning

confidence: 99%

Developing an Intelligent Fire Alarming, Monitoring and Rescuing System Using Uav

Nembhnani

Yadav

Biswas

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Fire alarm methods become increasingly complicated and functionally more competent and responsible in recent years. All are intended to fulfill the conditions: protection of property and assets and protection of life. Audio and visual signs are sent to notify people about concerning fire or CO concentration in range zone along with UAV this all is part of fire alarm assembly. Fire sirens and sensors are usually set in fire alarm operations for more effective safety for nearby formations. The advanced fire alarm sign comprises using either a sound or a flash and some additional features such as sending a message or a phone call or e-mail and it will also help us know about the smoke level and temperature of the fire. Its important part is that it will have the priority to save the life of people as well as prevent the infrastructure from the fire so to do the job simultaneously the advanced features are utilized this also includes using IoT to use it wirelessly with UAV and CCTV which will help in audio and visuals for the accident site along with that it will also send message to people living near the accident location so that they can help people by rescuing them.

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An SAT‐Based Method to Multithreaded Program Verification for Mobile Crowdsourcing Networks

Cited by 2 publications

References 36 publications

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

Developing an Intelligent Fire Alarming, Monitoring and Rescuing System Using Uav

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