Triangle and GA Methods for UAVs Jamming

Zhang, Yu; Yang, Lisheng

doi:10.1155/2014/713430

Cited by 5 publications

(12 citation statements)

References 10 publications

(14 reference statements)

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“…Five jamming methods are included in the simulations. Random, GA and Triangle jamming methods come from [15]. PSO and AMN-PSO jamming methods is newly presented by this paper.…”

Section: B Amn-pso Jamming Methodsmentioning

confidence: 99%

“…In our simulations, PSO and AMN-PSO jamming methods together with Random, GA and Triangle methods from [15] are used to jam the UAVs network with 20, 40, 60, 80, 100 and 120 nodes respectively. The number of used jammers is from1 to 20.…”

Section: Simulationmentioning

confidence: 99%

“…UAVs [3], ZigBee, 802.15.4, Mica-2 [4], IEEE 802.11 [5], IEEE 802.11p [6], IEEE 802. 15.4a [7], Cognitive Radio Networks [8], etc. are prone to such attack.…”

Section: Introductionmentioning

confidence: 99%

“…An optimal jamming energy allocation scheme was presented analytically in [14]. The authors of [15] studied the problem of using multi jammers to do damage to the UAVs network. They introduced Triangle method and GA (Genetic Algorithm) based method for the jamming of UAVs network.…”

Section: Introductionmentioning

confidence: 99%

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Amn-Pso Method For Jamming Unmanned Aerial Vehicle Network

Zhang

Feng

Jie

2015

International Journal on Smart Sensing and Intelligent Systems

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“…Five jamming methods are included in the simulations. Random, GA and Triangle jamming methods come from [15]. PSO and AMN-PSO jamming methods is newly presented by this paper.…”

Section: B Amn-pso Jamming Methodsmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

“…UAVs [3], ZigBee, 802.15.4, Mica-2 [4], IEEE 802.11 [5], IEEE 802.11p [6], IEEE 802. 15.4a [7], Cognitive Radio Networks [8], etc. are prone to such attack.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Amn-Pso Method For Jamming Unmanned Aerial Vehicle Network

Zhang

Feng

Jie

2015

International Journal on Smart Sensing and Intelligent Systems

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“…[6] provide motion strategies for an jammer to jam the communication between a pair of UAVs. Our previous work [11] studies the problem of jamming UAVs network. Triangle method and GA (Genetic Algorithm) based method are introduced.…”

Section: Introductionmentioning

confidence: 99%

Achieving Maximal NJRL Method for Jamming UAVs Network

Zhang

Peng²,

Feng

2016

IJFGCN

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Different from others, we focus on designing jamming methods for disrupting UAVs network efficiently. The jamming problem is formulated. Then we introduce two new methods (AMN-U2C and AMN-U2U) for jamming UAVs network. Both of them adopt the idea intending to achieve maximal number of jammed receiving links. AMN-U2C method searches locations for jammer by moving jammers along the path from the point of a UAV to the center point of others. AMN-U2U method searches by shifting jammers from the point of a UAV to another UAV. Finally we simulate the jamming attack on UAVs network using AMN-U2C, AMN-U2U, TP, GA and Random methods. The result shows AMN-U2U does best at most cases.

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Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

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Summary A drone or unmanned aerial vehicles (UAVs) is becoming a trending area for researchers worldwide. UAV's contribution is increasing in day‐to‐day life, whether it is in a military zone, disaster management, healthcare sector, smart cities, Internet of Things (IoT), urban air mobility, and many more. In contrast, UAV's limited computational capability and low‐energy sources pose significant challenges for real‐time data processing, storage, networking, and security that are critical in emergencies such as floods, earthquakes, and cyclones. UAVs are rapidly used to satisfy user requirements as well as services. As the demand for UAVs aided heterogeneous wireless networks increases in critical emergencies, fog computing serves several benefits to fulfill users' demands in terms of low latency, support, data storage, mobility, availability, scalability, and so on. This study aims to present a comprehensive study with their technical aspects for understanding fog computing, security issues, privacy concerns, and risks, along with its solutions. This paper suggests the collaboration of UAV‐Fog architecture based on the four‐tier network consisting of smart things, local UAVs, UAV‐Fog, and cloud server, to control UAV's data and also described some of the security issues faced by this cloud infrastructure. Further, this research article also sheds new light on some scenarios of UAV‐Fog for such deployments, applications, opportunities, challenges, and their major security threats and their countermeasures. Afterward, we design taxonomy of the collaboration of UAV‐Fog with their respective approaches.

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Triangle and GA Methods for UAVs Jamming

Cited by 5 publications

References 10 publications

Amn-Pso Method For Jamming Unmanned Aerial Vehicle Network

Amn-Pso Method For Jamming Unmanned Aerial Vehicle Network

Achieving Maximal NJRL Method for Jamming UAVs Network

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

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