Distributed Detection in Sensor Networks Over Fading Channels With Multiple Antennas at the Fusion Centre

Nevat, Ido; Peters, Gareth W.; Collings, Iain B.

doi:10.1109/tsp.2013.2293970

Cited by 64 publications

(68 citation statements)

References 31 publications

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“…Therefore, some research is on the distribution of adaptable power resources in each sensor network for transforming to multiantenna receivers especially. The authors Nevat et al developed optimal algorithms for distributed detection in sensor networks over fading channels with multiple receive antennas at the FC using without EH and collaboration between the sensors.…”

Section: Introductionmentioning

confidence: 99%

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Choubin

Taherpour

2020

Trans Emerging Tel Tech

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This paper addresses the detection improvement using multiple antenna fusion center (FC) and optimal collaboration among sensors with energy harvesting (EH) capability in WSNs. Using multiple antennas at FC, we benefit from the available fundamental spatial diversity in distributed WSN. We formulate to maximize the probability of collaborative detection based on parameters of collaboration among sensors and the energy of each sensor for the different scenarios as the optimization problem. In particular, we consider several practical constraints for EH and battery energy storage to maintain network connectivity. The objective function is the nonconvex function so the optimization problem is obtained as the nonconvex problem. To resolve this issue, the objective function is converted to a convex function using a relaxation method, and an optimum solution is obtained for this problem using local optimum values. The numerical results show the impact of the different parameters on the performance of the collaborative decision. These results also demonstrate that using distributed spatial diversity at FC by multiple antennas leads to a better decision in terms of error probability, and distributed wireless sensor‐enabled with EH capability results to more robust network connectivity in practice.

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Section: Introductionmentioning

confidence: 99%

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Choubin

Taherpour

2020

Trans Emerging Tel Tech

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“…In [3,4], the optimal fusion rule is derived under the conditional independence assumption. In [5], the authors develop a new and optimal algorithm for distributed detection in sensor networks over fading channels with multiple receiving antennas at the FC. It derives the optimal decision rules and the associated probabilities of detection and false alarm for three scenarios of channel state information availability.…”

Section: Introductionmentioning

confidence: 99%

“…The performances of different approaches, the Chair-Varshney rule, generalized likelihood ratio test, and Bayesian view, are compared through simulations in [11]. Decision fusion with fading channels which are non-ideal channels is studied in [5,15]. The optimal power allocation between training and data at each sensor over orthogonal channels that are subject to path loss, Rayleigh fading, and Gaussian noise is derived in [16].…”

Section: Introductionmentioning

confidence: 99%

An algorithm based on logistic regression with data fusion in wireless sensor networks

Liu

Luo

Qin

et al. 2017

J Wireless Com Network

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A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report "1" to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors' detection probabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity.

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“…In fact, quantifying the effect of imperfect channel state information (CSI) and channel estimation error on the design and performance of wireless communication systems is a challenging problem, that has attracted the attention of researchers over the past decade [4]. Recently, channel-aware binary distributed detection with imperfect CSI was studied in [5]- [7]. Compared with binary distributed detection, the literature on channel-aware M -ary distributed detection falls short [8]- [10], where the communication channels are modeled as additive white Gaussian noise (AWGN) [8] and Rayleigh fading with perfect CSI available at the FC [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Impact of wireless channel uncertainty upon M-ary distributed detection systems

Hajibabaei

Vosoughi

2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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We consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an M -ary hypothesis testing problem. Sensors make M -ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel state information (CSI) on the optimal maximum a posteriori probability (MAP) fusion rules and detection performance, when the sum of training and data symbol transmit powers is fixed. Our results show that, when sensors employ M -FSK modulation, the error probability is minimized when training symbol transmit power is zero (regardless of the reception mode at the FC). However, for coherent reception and M -PSK modulation the error probability is minimized when half of transmit power is allocated for training symbol.

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Distributed Detection in Sensor Networks Over Fading Channels With Multiple Antennas at the Fusion Centre

Cited by 64 publications

References 31 publications

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

An algorithm based on logistic regression with data fusion in wireless sensor networks

Impact of wireless channel uncertainty upon M-ary distributed detection systems

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