Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks

Ghaderi, Mohammad Reza; Vakili, Vahid Tabataba; Sheikhan, Mansour

doi:10.1007/s11235-020-00748-9

Cited by 15 publications

(5 citation statements)

References 45 publications

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“…1 illustrates the concept of compressed sensing data collection. Wireless sensor networks are characterized by dense sensor node deployment and high signal sampling frequencies, making the original signals compressible [14]. However, in most cases of transmission line monitoring networks, signal values are not zero, indicating a lack of sparsity [15].…”

Section: Compressed Sensing Theorymentioning

confidence: 99%

Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network

YIN,

YU,

WANG

2024

IJACSA

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Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.

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Section: Compressed Sensing Theorymentioning

confidence: 99%

Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network

YIN,

YU,

WANG

2024

IJACSA

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“…The time-spectral techniques, sharable resource spectrum is harvested and allocated by geospatial, frequency-spectral province, and temporal when receiving the application for spectrum use. [17][18][19] A prototype called a web-based spectrum managing system has been applied by using sensors and 10 servers. The present approach will reduce traffic data between the servers and sensors by 97%, reaching an average data of 10 kbps.…”

Section: Related Workmentioning

confidence: 99%

“…The author makes it for reducing the cost of the communication sensors because repeated transmission data are not required while enabling the server edge. The time‐spectral techniques, sharable resource spectrum is harvested and allocated by geospatial, frequency‐spectral province, and temporal when receiving the application for spectrum use 17–19 . A prototype called a web‐based spectrum managing system has been applied by using sensors and 10 servers.…”

Section: Related Workmentioning

confidence: 99%

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Perumal¹,

Nagarajan²

2022

Int J Communication

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Summary In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%.

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“…Based on Spatio-temporal correlations between measurements, SCS gathers samples from nodes to monitor climatic data, and the use of spatio-temporal sparsification helped to reduce the energy usage of nodes that are associated in space and time. [ 48 ] introduced a model for energy consumption analysis. Relying on this model, the sources for energy usage in CS based WSNs are grouped into two categories: communication and computation, and are modeled using their components.…”

Section: Related Workmentioning

confidence: 99%

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

et al. 2021

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Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.

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Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks

Cited by 15 publications

References 45 publications

Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network

Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

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