Distributed Estimation in a Wireless Sensor Network Using Hybrid MAC

Liu, Ju-Chieh; Chung, Char-Dir

doi:10.1109/tvt.2011.2160302

Cited by 23 publications

(18 citation statements)

References 21 publications

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“…On the other hand, for digital transmission, each sensor performs source and channel coding before transmitting the encoded information over the fading channel (see [6] and references therein). According to the studies in [7][8][9][10][11][12][13][14][15][16][17], analog transmission generally outperforms digital transmission. This is because the fidelity of the source's parameter is always compromised in the source coding (quantization) process required for digital transmission, while it is preserved with analog transmission.…”

Section: Introductionmentioning

confidence: 99%

“…The major disadvantage of the orthogonal MAC is that it requires larger transmission bandwidth or latency to accommodate K orthogonal channels. More recently, a hybrid MAC is investigated in [17], where all sensors are divided into groups and the coherent MAC is used for sensors within each group, whereas the orthogonal MAC is used across different groups. A flexible trade-off between the coherent and orthogonal MACs can therefore be obtained by changing the number of groups and the number of sensors in each group.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, considered is a scenario where N orthogonal channels are shared by K sensors to transmit the their observations to the FC, where the N is much smaller than K due to bandwidth constraint. While the semi-orthogonal MA scheme is designed based on the similar idea of sensor grouping in [17], the key difference is that, in the proposed MA scheme, the sensors in one group transmit simultaneously without the expensive phase synchronization operation. This means that the signals from sensors within one group are directly superimposed instead of coherently combined as in the hybrid MAC.…”

Section: Introductionmentioning

confidence: 99%

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Distributed estimation in wireless sensor networks with semi-orthogonal MAC

Nguyen

2016

J Wireless Com Network

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This paper is concerned with distributed estimation of a scalar parameter using a wireless sensor network (WSN) that employs a large number of sensors operating under limited bandwidth resource. A semi-orthogonal multiple-access (MA) scheme is proposed to transmit observations from K sensors to a fusion center (FC) via N orthogonal channels, where K ≥ N. The K sensors are divided into N groups, where the sensors in each group simultaneously transmit on one orthogonal channel (and hence the transmitted signals are directly superimposed at the FC as opposed to be coherently combined). Under such a semi-orthogonal multiple access channel (MAC), performance of the linear minimum mean squared error (LMMSE) estimation is analyzed in terms of two indicators: the channel noise suppression capability and the observation noise suppression capability. The analysis is performed for two versions of the proposed semi-orthogonal MA scheme: fixed sensor grouping and adaptive sensor grouping. In particular, the semi-orthogonal MAC with fixed sensor grouping is shown to have the same channel noise suppression capability and two times the observation noise suppression capability when compared to the orthogonal MAC under the same bandwidth resource. For the semi-orthogonal MAC with adaptive sensor grouping, it is determined that N = 4 is the most favorable number of orthogonal channels when taking into account both performance and feedback requirement. In particular, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to that of the hybrid MAC, while requiring only log 2 N = 2 bits of information feedback instead of the exact channel phase for each sensor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed estimation in wireless sensor networks with semi-orthogonal MAC

Nguyen

2016

J Wireless Com Network

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“…The problem of decentralized estimation and detection has seen a vast research effort in the last decade, especially in the design of optimal schemes for parameter estimation [26], [28], [29], hypothesis testing [4], [20], [27], tracking [7], [19] and random field estimation [8]. Distributed estimation in bandwidth-energy constrained environments has been considered in [11], [12], [17], [21], for a static setting. Estimation and detection problems exploiting feedback information from the FC have been investigated in [6], [9], [10], [25], e.g., enabling adaptation of the SNs' quantizers in the estimation of a finite state Markov chain [9].…”

Section: Introductionmentioning

confidence: 99%

Cross-Layer Design of Distributed Sensing-Estimation With Quality Feedback— Part I: Optimal Schemes

Michelusi

Mitra

2015

IEEE Trans. Signal Process.

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This two-part paper presents a feedback-based crosslayer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this first part, the optimization complexity is reduced by exploiting the statistical symmetry and large network approximation of the WSN. Structural properties of the optimal policy are derived for a coordinated and a decentralized scheme. It is proved that a dense WSN provides sensing diversity, so that only a few SNs with the best local observation quality need to be activated, despite the fluctuations of the WSN. The optimal policy dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. The costs of coordination and feedback are evaluated, revealing the scalability of the decentralized scheme to large WSNs, at the cost of performance degradation. Simulation results demonstrate cost savings from 30% to 70% over a nonadaptive scheme, and significant gains over a previously proposed estimator which does not consider these cross-layer factors.

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“…The base station is usually a powerful machine like a PC. Internet Protocol version 6 (IPv6) is needed for the PC in the network [15]. IPv6 is a version of the internet protocol (IP) that is designed to succeed Internet Protocol version 4 (IPv4) and is also the basic technology of internet [16].…”

Section: Introductionmentioning

confidence: 99%

IPv6-Based Smart Metering Network for Monitoring Building Electricity

Dong

Liu

2013

Advances in Mechanical Engineering

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A smart electricity monitoring system of building is presented using ZigBee and internet to establish the network. This system consists of three hardware layers: the host PC, the router, and the sensor nodes. A hierarchical ant colony algorithm is developed for data transmission among the wireless sensor nodes. The wireless communication protocol is also designed based on IPv6 protocol on IEEE 802.15.4 wireless network. All-IP approach and peer-to-peer mode are integrated to optimize the network building. Each node measures the power, current, and voltage and transmits them to the host PC through the router. The host software is designed for building test characteristics, having a tree hierarchy and a friendly interface for the user. The reliability and accuracy of this monitoring system are verified in the experiment and application.

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Distributed Estimation in a Wireless Sensor Network Using Hybrid MAC

Cited by 23 publications

References 21 publications

Distributed estimation in wireless sensor networks with semi-orthogonal MAC

Distributed estimation in wireless sensor networks with semi-orthogonal MAC

Cross-Layer Design of Distributed Sensing-Estimation With Quality Feedback— Part I: Optimal Schemes

IPv6-Based Smart Metering Network for Monitoring Building Electricity

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