Adaptive low power detection of sparse events in wireless sensor networks

Alwakeel, Mohammed M.; Abdelkader, Mohamed F.; Seddik, Karim G.; Ghuniem, Atef M.

doi:10.1109/wcnc.2014.6953130

Cited by 6 publications

(19 citation statements)

References 17 publications

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“…4 shows PCD versus the sampling ratio at different SNR values, when n = 1000 event sources are randomly distributed in an area of 1000 m by 1000 m, k = 10 events are simultaneously active, and propagation loss factor is α = 3. As can be seen in this figure, the proposed SED-AFC scheme outperforms both BCS and SCS [9] in terms of PCD for different SNRs. Moreover, the number of sensors in SCS is relatively large (500), which significantly increases the total number of transmissions; leading to poor energy efficiency.…”

Section: Simulation Resultsmentioning

confidence: 69%

“…In this section, we compare the performance of our proposed scheme with BCS [8] and SCS [9] at different SNRs and sampling ratios. Here, sampling ratio, β, is defined as the ratio of the number of sensors, m, and that of event sources, n. We use the probability of correct detection (PCD) and probability of false detection (PFD) as performance metrics.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Here, sampling ratio, β, is defined as the ratio of the number of sensors, m, and that of event sources, n. We use the probability of correct detection (PCD) and probability of false detection (PFD) as performance metrics. PCD is defined as the percentage of correctly detected active events and PFD is referred to as the percentage of non-active AFCS, Uniform deployment, SNR=20 dB AFCS, Uniform deployment, SNR=10 dB SCS [9], SNR=20 dB SCS [9], SNR=10 dB BCS [8], SNR=20 dB BCS [8], SNR=10 dB events that are falsely detected as active [9]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We further analyze the probability of false detection for the proposed scheme and show that this probability is very low at different SNRs. Simulation results show that the proposed scheme outperforms the SCS [9] and BCS [8] approaches at different SNRs and different sampling ratios.…”

Section: Introductionmentioning

confidence: 98%

“…Bayesian compressive sensing (BCS) has been used in [8] to solve the SPE problem in WSNs; however, BCS is known to be sensitive to noise, leading to degraded performance in low signal to noise ratios (SNRs). To solve this problem, authors in [9] proposed the use of sequential compressive sensing (SCS) to model the SED problem as a support recovery problem and solve it in an adaptive manner. However, a large number of sensor nodes (about half that of event nodes) need to be deployed in the sensing field to guarantee its performance.…”

Section: Introductionmentioning

confidence: 99%

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Sparse event detection in wireless sensor networks using analog fountain codes

Shirvanimoghaddam

Vucetic

2014

2014 IEEE Global Communications Conference

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In this paper, we focus on the sparse event detection (SED) problem in wireless sensor networks (WSNs), where a set of sensor nodes are placed in the field of interest to capture sparse active event sources. The SED problem in WSNs is represented from a coding theory perspective by using capacity approaching analog fountain codes (AFCs) and further solved with a standard belief propagation (BP) decoding algorithm. We show that the sensing process in WSNs produces an equivalent analog fountain code at the sink node, with code parameters determined based on the sensing capability of sensor nodes and channel gains. We analyze the probability of false detection of the proposed approach and show it to be negligible. Simulation results show that the proposed approach, namely sparse event detection with AFC (SED-AFC), achieves a significantly higher probability of correct detection (PCD) compared to existing literature at various SNR values, with a negligible probability of false detection (PFD). Moreover, we show that the minimum sampling ratio in high SNRs for the proposed scheme reaches the lower bound which is mainly characterized by the sensing coverage of the sensors and field dimensions.

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Section: Simulation Resultsmentioning

confidence: 69%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Sparse event detection in wireless sensor networks using analog fountain codes

Shirvanimoghaddam

Vucetic

2014

2014 IEEE Global Communications Conference

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Model assisted compressive data gathering in dense IoT monitoring of water distribution networks

Eltabie

Ghuniem

Abdelkader

2019

2019 IEEE International Smart Cities Conference (ISC2)

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Sparse spectrum sensing in infrastructure-less cognitive radio networks via binary consensus algorithms

Seif

ElBatt

Seddik

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common "centralized approach" for spectrum sensing. In this paper, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its neighbors, and at every new iteration each SU will take a new decision based on its current decision and the decisions it receives from its neighbors; in the next iteration, each SU will share its new decision with its neighbors. We show via simulations that the detection performance can tend to the performance of majorityrule Fusion Center based CRNs.

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Adaptive low power detection of sparse events in wireless sensor networks

Cited by 6 publications

References 17 publications

Sparse event detection in wireless sensor networks using analog fountain codes

Sparse event detection in wireless sensor networks using analog fountain codes

Model assisted compressive data gathering in dense IoT monitoring of water distribution networks

Sparse spectrum sensing in infrastructure-less cognitive radio networks via binary consensus algorithms

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