On the cooperative MIMO communication for energy-efficient cluster-to-cluster transmission at wireless sensor network

Islam, Mohammad Rakibul; Kim, Dong Ha

doi:10.1007/s12243-009-0151-9

Cited by 14 publications

(14 citation statements)

References 28 publications

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“…The total energy in the case of node selection is

\begin{matrix} E_{DA + MIMO} = E_{DA + MISO} = \sum_{i = 1}^{M_{t}} \frac{L_{i}}{F} E_{ch} \\ + L_{ch} \sum_{i = 1}^{M_{t} - 1} \frac{L_{i}}{F} E_{i}^{normalt} + L_{ch} \frac{L_{i}}{F} E_{normals}^{normalt} + \sum_{i = 1}^{M_{t} - 1} L_{i} E_{i}^{normalt} \\ + E_{d a} \sum_{i = 1}^{M_{t}} L_{i} + (M_{normalt} - 1) E_{i}^{t 0} \sum_{i = 1}^{M_{t}} L_{i} y_{i} \\ + L_{normalc} P_{normals} \frac{L_{i}}{F} \sum_{i = 1}^{x} E_{i}^{t 0} + E_{normalb}^{normalr} \sum_{i = 1}^{M_{r}} L_{i} y_{i} \end{matrix}

where E ch is the channel estimation energy L ch is the number of bits needed to transmit the channel estimation result. Data size L i is divided by the frame size F to find out the number of channel estimations required for the transmitted data size.…”

Section: Energy Modelmentioning

confidence: 99%

Energy efficiency of virtual multi-input, multi-output based on sensor selection in wireless sensor networks

Rizk

Magdy

Zaki

2012

Wirel. Commun. Mob. Comput.

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In this paper, a weighted node selection technique in wireless sensor networks is proposed. It is an energy‐efficient cooperative technique where a selected number of sensors at the transmitting end are connected with a selected number of sensors at the receiving end to form a virtual multi‐input multi‐output. The proposed technique is based on a weighted selection function that combines geographical location, inter‐sensor distance in a cluster, channel estimate energy, power circuit, channel loss, mobility factor, and residual energy of each sensor. The weight of each of these parameters in the selection function depends on the degree that this parameter affects the energy consumption. Then, the cluster head selects the sensors with better selection parameters that reduce the overall energy consumption. The numerical results show that the proposed weighted node selection technique achieves a significant improvement in the energy consumption, delay, and network lifetime than the conventional techniques with and without the selected number of sensors. Its improvement reaches 15% in the energy consumption that leads to an increase in the network lifetime by four times the network lifetime of other techniques. Copyright © 2012 John Wiley & Sons, Ltd.

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“…The total energy in the case of node selection is

\begin{matrix} E_{DA + MIMO} = E_{DA + MISO} = \sum_{i = 1}^{M_{t}} \frac{L_{i}}{F} E_{ch} \\ + L_{ch} \sum_{i = 1}^{M_{t} - 1} \frac{L_{i}}{F} E_{i}^{normalt} + L_{ch} \frac{L_{i}}{F} E_{normals}^{normalt} + \sum_{i = 1}^{M_{t} - 1} L_{i} E_{i}^{normalt} \\ + E_{d a} \sum_{i = 1}^{M_{t}} L_{i} + (M_{normalt} - 1) E_{i}^{t 0} \sum_{i = 1}^{M_{t}} L_{i} y_{i} \\ + L_{normalc} P_{normals} \frac{L_{i}}{F} \sum_{i = 1}^{x} E_{i}^{t 0} + E_{normalb}^{normalr} \sum_{i = 1}^{M_{r}} L_{i} y_{i} \end{matrix}

Section: Energy Modelmentioning

confidence: 99%

Energy efficiency of virtual multi-input, multi-output based on sensor selection in wireless sensor networks

Rizk

Magdy

Zaki

2012

Wirel. Commun. Mob. Comput.

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“…Data aggregation reduces the data size but increase the steps in cooperative MIMO communication. The issue is explained in many research papers [1][2][3][4][5][6]. …”

Section: Sensor Networkmentioning

confidence: 99%

“…Moreover, physical implementation of multiple antennas at a small node may not be feasible. As solutions to the latter problem cooperative MIMO (C-MIMO) and virtual antenna array concepts have been proposed to achieve MIMO capability in a network of single antenna [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Secrecy capacity analysis in a cooperative MIMO based wireless sensor network

Islam

Tanni

Itha

et al. 2011

14th International Conference on Computer and Information Technology (ICCIT 2011)

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Although conventional cryptographic security mechanisms are essential to the overall problem of security, the openness of wireless medium poses both threats and opportunities for secure transmission. Information theoretic security attracts researchers for its robust nature and multiple input multiple output (MIMO) channel in the presence of multiple eavesdropper offers an unveiled research area. In this paper, information theoretic security is analyzed in the form of ergodic capacity in a wireless sensor network (WSN) scenario. Analytical calculations are performed for diff erent channel state information (CS!) and number of antennas. Simulation results show that MIMO configurations with both CSI for main channel and eavesdropper channel are known at the transmitter shows better performance where secrecy capacity is the measure of performance. IndexTerms-Cooperative technique, secrecy capacity, CSI, MIMO, wireless sensor networks I.

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“…The goal is to reduce the energy consumption of sensors since energy efficiency optimization is a very important critical issue in system design of WSN. The results have shown that in some cases, MIMO-based cooperative systems for WSN lead to better energy optimization and smaller end-to-end delay compared to the traditional singleinput single-output (SISO) approach even after taking into account the additional circuit power, communications, and training overheads [10]. These cooperative systems were based on space-time block codes (STBC).…”

Section: Introductionmentioning

confidence: 99%

Impacts of impulsive noise from partial discharges on wireless systems performance: application to MIMO precoders

Madi

Sacuto

Vrigneau

et al. 2011

J Wireless Com Network

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To satisfy the smart grid electrical network, communication systems in high-voltage substations have to be installed in order to control equipments. Considering that those substations were not necessarily designed for adding communication networks, one of the most appropriate solutions is to use wireless sensor network (WSN). However, the high voltage transported through the station generates a strong and specific radio noise. In order to prepare for such a network, the electromagnetic environment has to be characterized and tests in laboratories have to be performed to estimate the communication performances. This paper presents a method for measuring the noise due to high voltage and more particularly the impulsive noise. In the laboratory, we generate the impulsive noise using two specimens, and we show that these laboratory measurements validate the field measurements of Pakala et al. For the two specimens, it aims to link the noise characteristics (magnitude and frequency) with the specimen parameters (power supply and geometric dimensions) to predict the environments where wireless communications can be troublesome. By using different sets of this measured noise, we show that the statistical model of Middleton Class A can be used to model the impulsive noise in high-voltage substations better than the Gaussian model. We consider a cooperative multiple-input-multiple-output (MIMO) system to achieve the wireless sensor communication. This system uses recent MIMO techniques based on precoding like max-d min and P-OSM precoders. The MIMO precoder-based cooperative system is a potential candidate for energy saving in WSN since energy efficiency optimization is a very important critical issue. Since MIMO precoders are with Gaussian noise assumption, we evaluate the performance of several MIMO precoders in the presence of impulsive noise using estimated parameters from the measured noise.

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On the cooperative MIMO communication for energy-efficient cluster-to-cluster transmission at wireless sensor network

Cited by 14 publications

References 28 publications

Energy efficiency of virtual multi-input, multi-output based on sensor selection in wireless sensor networks

Energy efficiency of virtual multi-input, multi-output based on sensor selection in wireless sensor networks

Secrecy capacity analysis in a cooperative MIMO based wireless sensor network

Impacts of impulsive noise from partial discharges on wireless systems performance: application to MIMO precoders

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