“See Something, Say Something” Crowdsourced Enforcement of Spectrum Policies

Dutta, Aveek; Chiang, Mung

doi:10.1109/twc.2015.2466550

Cited by 42 publications

(13 citation statements)

References 13 publications

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“…1 (b). In some cases, a selfish attacker may break the upper bound of the transmit power constraint for a higher data rate [20] [21]. Similarly, some malicious attackers may intentionally apply for spectrum exploitation with a large coverage, quoting a high transmit power, but in fact they work with a low transmit power, which decreases the spectrum efficiency of spatial reuse [22].…”

Section: A Background and Motivationmentioning

confidence: 99%

“…On the other side, few works have been done for the rogue power emission problem. In [20], crowdsourced enforcement, where a crowd of mobile users collaterally make detection, is exploited to find out the spectrum misuse behavior and the related characteristics, e.g., the signal strength, are analyzed.…”

Section: B Related Workmentioning

confidence: 99%

“…When the condition is not satisfied in Theorem 1, i.e., there are no mutual effects between the two subproblems, based on (34), we can obtain the asymptotic value of Pr(H 2 |H 2 ) just as formulated in (38), where Pr(H 0 |H 2 ) is obtained based on (20). As µ > 0 and µ increases with the number of samples N , Pr(H 1 |H 2 ) decreases with N .…”

Section: Proposition 2 When N Is Sufficiently Large and The Hypothesismentioning

confidence: 99%

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Spectrum Sensing Under Spectrum Misuse Behaviors: A Multi-Hypothesis Test Perspective

Zhang

Ding

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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Abstract-Spectrum misuse behaviors, brought either by illegitimate access or by rogue power emission, endanger the legitimate communication and deteriorate the spectrum usage environment. In this paper, our aim is to detect whether the spectrum band is occupied, and if it is occupied, recognize whether the misuse behavior exists. One vital challenge is that the legitimate spectrum exploitation and misuse behaviors probabilistically coexist and the illegitimate user (IU) may act in an intermittent and fast-changing manner, which brings about much uncertainty for spectrum sensing. To tackle it, we firstly formulate the spectrum sensing problems under illegitimate access and rogue power emission as a uniform ternary hypothesis test. Then, we develop a novel test criterion, named the generalized multi-hypothesis Neyman-Pearson (GMNP) criterion. Following the criterion, we derive two test rules based on the generalized likelihood ratio test (GLRT) and the Rao test, respectively, whose asymptotic performances are analyzed and an upper bound is also given. Furthermore, a cooperative spectrum sensing scheme is designed based on the global GMNP criterion to further improve the detection performances. In addition, extensive simulations are provided to verify the proposed schemes' performance under various parameter configurations.

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Section: A Background and Motivationmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Proposition 2 When N Is Sufficiently Large and The Hypothesismentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Sensing Under Spectrum Misuse Behaviors: A Multi-Hypothesis Test Perspective

Zhang

Ding

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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“…There is also a crowdsourcing-based framework named SpecGuard [21] which explores dynamic power control at SUs to contain the spectrum permit in physical layer signals. Another crowdsourced enforcement framework [22] improves the probability of detection while reducing the likelihood of false positives for spectrum misuses and it can detect misuses caused by mobile users. Moreover, applying big data analysis and machine learning to cloud-based radio access networks also provide an appropriate approach to enable long-term spectrum monitoring [23].…”

Section: Introductionmentioning

confidence: 99%

SpecWatch: A framework for adversarial spectrum monitoring with unknown statistics

Yang

Lin

et al. 2018

Computer Networks

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In cognitive radio networks (CRNs), dynamic spectrum access has been proposed to improve the spectrum utilization, but it also generates spectrum misuse problems. One common solution to these problems is to deploy monitors to detect misbehaviors on certain channel. However, in multi-channel CRNs, it is very costly to deploy monitors on every channel. With a limited number of monitors, we have to decide which channels to monitor. In addition, we need to determine how long to monitor each channel and in which order to monitor, because switching channels incurs costs. Moreover, the information about the misuse behavior is not available a priori. To answer those questions, we model the spectrum monitoring problem as an adversarial multi-armed bandit problem with switching costs (MAB-SC), propose an effective framework, and design two online algorithms, SpecWatch-II and SpecWatch-III, based on the same framework. To evaluate the algorithms, we use weak regret, i.e., the performance difference between the solution of our algorithm and optimal (fixed) solution in hindsight, as the metric.We prove that the expected weak regret of SpecWatch-II is O(T 2/3 ), where T is the time horizon.Whereas, the actual weak regret of SpecWatch-III is O(T 2/3 ) with probability 1 − δ, for any δ ∈ (0, 1).Both algorithms guarantee the upper bounds matching the lower bound of the general adversarial MAB-SC problem. Therefore, they are all asymptotically optimal. Index TermsCognitive Radio Networks, Spectrum Monitoring, Multi-armed Bandit Problem. This is an extended and enhanced version of the paper [50] that appeared in INFOCOM 2016.

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“…Recently, there has been a great deal of interest in leveraging crowd-sourcing of mobile phones as a kind of ad-hoc sensor network to provide robust, dynamic, and, most importantly, rapid detection of spectrum violators [1,11,19]. While promising, a key challenge to the study of such sensor network data is the coverage problem.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Sensor Network Coverage with Location Uncertainty

Sodergren

Hair

Phillips

et al. 2017

2017 IEEE Visualization in Data Science (VDS)

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Figure 1: The visual interface in exploring sensor network coverage with location uncertainty: (a) control panel, (b) viewing panel, (c) interaction panel and (d) color panel. In the interaction panel, the coverage of a randomly generated sensor network is visualized by the union of disk-like sensor regions overlaid with a Rips complex representation. ABSTRACTWe present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks. *

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“See Something, Say Something” Crowdsourced Enforcement of Spectrum Policies

Cited by 42 publications

References 13 publications

Spectrum Sensing Under Spectrum Misuse Behaviors: A Multi-Hypothesis Test Perspective

Spectrum Sensing Under Spectrum Misuse Behaviors: A Multi-Hypothesis Test Perspective

SpecWatch: A framework for adversarial spectrum monitoring with unknown statistics

Visualizing Sensor Network Coverage with Location Uncertainty

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