Jamming and anti-jamming techniques in wireless networks: a survey

Grover, Kanika; Lim, Alvin; Yang, Qing

doi:10.1504/ijahuc.2014.066419

Cited by 337 publications

(215 citation statements)

References 57 publications

(49 reference statements)

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“…Jamming attack is a serious threat in wireless networks, and various anti-jamming methods have been developed in recent years [1]- [8]. Due to factors of the jammers' activities, the quality of channels varies between "good" and "poor" dynamically.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

Yao

Jia

2019

IEEE Wireless Commun. Lett.

130

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In this letter, we investigate the anti-jamming defense problem in multi-user scenarios, where the coordination among users is taken into consideration. The Markov game framework is employed to model and analyze the anti-jamming defense problem, and a collaborative multi-agent anti-jamming algorithm (CMAA) is proposed to obtain the optimal antijamming strategy. In sweep jamming scenarios, on the one hand, the proposed CMAA can tackle the external malicious jamming. On the other hand, it can effectively cope with the mutual interference among users. Simulation results show that the proposed CMAA is superior to both sensing based method and independent Q-learning method, and has the highest normalized rate.

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Section: Introductionmentioning

confidence: 99%

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

Yao

Jia

2019

IEEE Wireless Commun. Lett.

130

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“…where Pr{X SF (t)| X RP (t)} is 1 because the entire layer fails if the relay experiences a PFGE. q RP (t) is determined using (7), where f RP (t) is the probability density function (PDF) of time-to-PFGE of relay R.…”

Section: Competing Reliability Analysismentioning

confidence: 99%

A hierarchical combinatorial reliability model for smart home systems

Zhao

Xing

Zhang

et al. 2017

Quality & Reliability Eng

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As an application of the Internet of Things, smart home systems have received significant attentions in recent years due to their precedent advantages, eg, in ensuring efficient electricity transmission and integration with renewable energy. This paper proposes a hierarchical and combinatorial methodology for modeling and evaluating reliability of a smart home system. Particularly, the proposed methodology encompasses a multi-valued decision diagram-based method for addressing phased-mission, standby sparing, and functional dependence behaviors in the physical layer; and a combinatorial procedure based on the total probability theorem for addressing probabilistic competing failure behavior with random propagation time in the communication layer. The methods are applicable to arbitrary types of time-to-failure and time-to-propagation distributions for system components. A detailed case study of an example smart home system is performed to demonstrate applications of the proposed method and effects of different component parameters on the system reliability. (IoT) is an internetworking of myriad of "smart" objects and devices augmented with sensors and actuators (such as cell phones, smart appliances, electrical devices, etc.). [1][2][3] In other words, an IoT smart system incorporates sensing, actuation, and control functions for monitoring and analyzing a situation, and Abbreviations: EMS, Energy management system; FCE, Failure competition event; FDEP, Functional dependence; HSP, Hot spare; IFP, Isolation factor pair; IoT, Internet of Things; LF, Local failure; LS, Local sensing failure; LT, Local transmitting failure; MDD, Multi-valued decision diagram; PDEP, Probabilistic function dependence; PDF, Probability density function; PFDC, Probabilistic function dependence case; PFGE, Propagated failure with global effect; PMS, Phased mission system; PT, Propagation time. Notations: h, Phase number; f iP (t), f iLS (t), f iLT (t), PDF of time-to-PFGE/LS/LT of component i; f iPT (t), PDF of time-to-propagation of component i; FCE j , Failure competition event j, j = 1,2,3; ψ i , The failure function of the i-th phase in the physical layer; ψ xi;h , Unreliability of component i at the end of h; I, Union of set of components causing PFGE and set of components being isolated under an FCE; P FCEj t ð Þ, Probability of no PFGE to components in I; q iLS (t), q iLT (t), Occurrence probability of LS, LT to component i; q iP (t), Occurrence probability of PFGE to component i; q iS (t), q iT (t), Occurrence probability of LS, LT given that no PFGE to i; Q FCEj t ð Þ, Failure probability given that no PFGE happens to components in I; T h , Duration of phase h; x i = 0, Component i functions in all the phases; x i = h, Component i fails during h given that i is operational at the beginning of h; X iP , X iLS , X iLT , Event that component i has a PFGE, LS, LT; X iPT , Event that the propagation time is less than the time difference between the relay LT and the component i PFGE; U comm (t), Unreliability of the communicat...

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“…Proof: From (15) and (17), it is noted that CRLB k,i , defined in the proposition, provides an upper bound for the problem in (15). If the conditions in (18) are satisfied, the objective function in (15) becomes equal to the upper bound, CRLB k,i , for z = z opt k,i . Therefore, if z opt k,i satisfies the distance constraints (i.e., if it is feasible for (15)), it becomes the solution of (15).…”

Section: Propositionmentioning

confidence: 99%

Optimal Jammer Placement in Wireless Localization Systems

Gezici

Bayram

Kurt

et al. 2016

IEEE Trans. Signal Process.

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In this study, the optimal jammer placement problem is proposed and analyzed for wireless localization systems. In particular, the optimal location of a jammer node is obtained by maximizing the minimum of the Cramer-Rao lower bounds (CRLBs) for a number of target nodes under location related constraints for the jammer node. For scenarios with more than two target nodes, theoretical results are derived to specify conditions under which the jammer node is located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two of the target nodes. Also, explicit expressions are provided for the optimal location of the jammer node in the presence of two target nodes. In addition, in the absence of distance constraints for the jammer node, it is proved, for scenarios with more than two target nodes, that the optimal jammer location lies on the convex hull formed by the locations of the target nodes and is determined by two or three of the target nodes, which have equalized CRLBs. Numerical examples are presented to provide illustrations of the theoretical results in different scenarios.Comment: To appear in IEEE Transactions on Signal Processin

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Jamming and anti-jamming techniques in wireless networks: a survey

Cited by 337 publications

References 57 publications

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

A hierarchical combinatorial reliability model for smart home systems

Optimal Jammer Placement in Wireless Localization Systems

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