Compressive Sensing-Based Adaptive Top-k Query over Compression Domain in Wireless Sensor Networks

Chen, Yushun; Tsou, Yao-Tung

doi:10.1109/wcnc.2017.7925506

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…It consists in getting information based on signal sparsity, statistical analysis and other related theory . However, in general, the data decompression process in sensor nodes is inefficient . In addition, the sparsity of the sensor data may change significantly due to their time varying nature, affecting the recovery of the measured signal .…”

Section: Related Workmentioning

confidence: 99%

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Silva

Carvalho

Bispo

et al. 2019

Int J Communication

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Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure.Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments. KEYWORDSadaptive sensing, data collection, energy-aware sensing, wireless sensor networks INTRODUCTIONAs a key enabler of the Internet of Things (IoT) and Smart Cities paradigms, wireless sensor networks (WSNs) are currently a technology capturing much attention, both from the academic community and industry. Sustained by the development of multifunctional low-cost wireless sensors, WSNs are applied in a large range of scenarios, many of then requiring operation without human intervention. 1,2 This versatility and difficult maintenance urge for mechanisms of self-management and power saving in order to allow a cost-effective adaptation of the sensing process to the application area, while optimising WSN lifetime. 3 Reducing energy consumption of sensor nodes for improving WSN lifetime has been a major topic of research in the last decade, being identified three major subsystems impacting on energy consumption-communication, sensing, and processing. The communication subsystem revealed to be the most demanding, even for devices able of energy harvesting. 4 This motivated solutions such as data gathering aggregation in order to reduce the number of transmission events.However, several works revealed that, depending on the operational scenario, acquiring and processing data may be more demanding than communicating. 5,6 This suggests that a versatile data gathering process should avoid acquiring redundant information, even when data aggregation strategies are in place.Int J Commun Syst. 2020;33:e4153.wileyonlinelibrary.com/journal/dac

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Section: Related Workmentioning

confidence: 99%

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Silva

Carvalho

Bispo

et al. 2019

Int J Communication

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“…This approach consists of compressing the data to the minimum factor that allows the recovery of only the application-defined principal components. In addition, Chen et al [128] a two-stage compression scheme. First, the data are compressed at the sensor level with an M samples.…”

Section: Cs-based Cloud Storagementioning

confidence: 99%

“…• CS-based Top-k query data retrieval approach [127] [126][127][128] • A 2-stage compression architecture [128] Traffic Monitoring Reduce number • Multiple linear regression (MLR) mode based on CS to estimate the entire road traffic from a small [136][137][138] of required probes set of measurements low cost…”

Section: Iot Layer Approach Features Main Attributementioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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“…Because of the convenience in networking, low cost in maintenance, and strong resistance to natural disasters, Wireless Visual Sensor Networks (WVSN) are suitable for various applications such as traffic management and intelligent monitoring. Recently, continuous progress has been made in the security and compressibility of image encryption systems owing to the development of chaos and compressive sensing (CS) technology [1][2][3][4]. However, some applications might have specific requirements.…”

Section: Introductionmentioning

confidence: 99%

An Improved Image Steganography Scheme Based on Partial Preservation Embedding Algorithm for Wireless Visual Sensor Networks

Shen

Jiang

Zhu

et al. 2021

Mathematical Problems in Engineering

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With the continuous improvement of encryption algorithms, some applications based on the architecture of wireless visual sensor networks have gradually shifted their attention to the imperceptibility and antijamming performance of secret images. To reduce the probability of secret images being detected, the current research focuses on hiding secret data in the least-significant bit of the cover image in the spatial domain or embedding data into the coefficients of the high-frequency band in the transformational domain, which usually leads to poor performance in a hostile environment. Therefore, some researchers proposed to substitute the coefficients of the medium-frequency band in the transformational domain with secret information to enhance the anti-interference performance. However, this idea would severely affect the imperceptibility of secret images. As a result, an improved version based on the partial preservation embedding algorithm was designed in this paper. Theory analysis and simulation results indicate that the proposed scheme performs better than the existing methods by directly substituting the coefficients of the medium-frequency band in the transformational domain, especially in the case of strong noise interference.

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Compressive Sensing-Based Adaptive Top-k Query over Compression Domain in Wireless Sensor Networks

Cited by 8 publications

References 16 publications

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Compressive Sensing-Based IoT Applications: A Review

An Improved Image Steganography Scheme Based on Partial Preservation Embedding Algorithm for Wireless Visual Sensor Networks

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