Compressed Sensing-based data gathering in wireless Home Area Network for smart grid

Islam, Tahidul; Koo, Insoo

doi:10.1109/iciev.2012.6317430

“…The WSNs are also widely used in a variety of applications to monitor the physical world via a spatially distributed network of small wireless sensors that have the ability to self-organize into a well-connected network. The network data transmission is accomplished through multihop routing from individual wireless sensors to the wireless sensors in the sink layer [11]. Figure 2.2 shows a block diagram of WSNs.…”

Section: Overview Of Wsnsmentioning

confidence: 99%

Wireless Body Area Networks Based on Compressed Sensing Theory

Asli¹

2021

Preprint

0

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In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

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“…Lower-level approaches have also been presented. In another work focusing on HAN wireless systems, Islam and Koo [40] proposes that since sensor activity can usually be expected to be sparse in many WSN applications such as premise monitoring, it would be desirable if the sparsity could be exploited in the form of maximizing data compression and minimizing the communication overhead during the data aggregation process. As a result, the idea of compressed sensing [41] is utilized to achieve a significant save on the energy consumption of the data gathering process.…”

Section: B Fdias Against Other Generic Grid Wsn Applicationsmentioning

confidence: 99%

From Wired to Wireless: Challenges of False Data Injection Attacks Against Smart Grid Sensor Networks

Liu

¹

,

Labeau

²

2018

2018 IEEE Canadian Conference on Electrical &Amp; Computer Engineering (CCECE)

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Existing studies on False Data Injection Attacks (FDIAs), a type of stealth attacks against power grids aimed at compromising the system cyber-physical security, have primarily been conducted on wired systems in which state estimation is represented by overdetermined DC power flow models. The emerging trend of Smart Grid (SG) assisted by the widespread deployment of Wireless Sensor Networks (WSNs) for various new functionalities, on the other hand, calls for a review of how some certain well-established premises need to be adjusted in the new context. In addition to related studies based on traditional busbased systems, certain broad changes brought by the use of WSNs in grid systems will be introduced in this paper. Subsequently, differences in terms of bad data detection (BDD), false data injection attack strategy or physical feasibility of attack methods caused by the shift of scenario will be compared and briefly analyzed. A summary of new or previously overlooked requirements for FDIA studies will then be appended. By presenting a comprehensive review of related studies we will shed light on potential future research directions.

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“…11 compares BER of recovered ECG signal in non-CS and CS scenarios for a random sensing matrix. The simulation results…”

mentioning

confidence: 99%

Wireless Body Area Networks Based on Compressed Sensing Theory

Asli¹

2021

Preprint

0

View full text Add to dashboard Cite

In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

show abstract

Compressed Sensing-based data gathering in wireless Home Area Network for smart grid

Cited by 5 publications

References 20 publications

Wireless Body Area Networks Based on Compressed Sensing Theory

Wireless Body Area Networks Based on Compressed Sensing Theory

From Wired to Wireless: Challenges of False Data Injection Attacks Against Smart Grid Sensor Networks

Wireless Body Area Networks Based on Compressed Sensing Theory

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