Adaptive compressive sensing for energy efficient smart objects in IoT applications

Fragkiadakis, Alexandros; Charalampidis, Pavlos; Τράγος, Ηλίας

doi:10.1109/vitae.2014.6934488

Cited by 25 publications

(24 citation statements)

References 17 publications

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“…In this section, we present a novel adaptive CS scheme for energy efficient data compression and transmission in IoT applications, building on the assumption that the sparsity level of the data processed is of time-varying nature. The proposed adaptive CS scheme is an extension of our previous work ( [15]) and is depicted in Figure 1. We discuss the design of adaptive-rate "hardware-friendly" sensing matrices that are tailored for extremely resource-limited devices.…”

Section: B Change Point Methods Based On Ks Statisticmentioning

confidence: 99%

Rate-Adaptive Compressive Sensing for IoT Applications

Charalampidis

Fragkiadakis

Τράγος

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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Internet of Things (IoT) interconnects resourceconstrained devices for providing smart applications to citizens. These devices have to be able to ensure both a minimum Quality of Service (QoS) and a minimum level of security when gathering and transmitting data. Compressive Sensing (CS) is a relatively new theory that performs simultaneous lightweight compression and encryption and can be used to prolong the battery lifetime of devices. In this paper, we stress the fact that on the contrary with most previous approaches, the sparsity of the signals can change significantly due to their time-varying nature. We propose a rate-adaptive scheme for maintaining a maximum level of reconstruction error at the receiver, and ensure the QoS requirements. This scheme uses a change point detection method, detecting the change in the sparsity, and estimating the optimum compression rate for maintaining a minimum reconstruction error. Performance is evaluated using real experimental data.

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Section: B Change Point Methods Based On Ks Statisticmentioning

confidence: 99%

Rate-Adaptive Compressive Sensing for IoT Applications

Charalampidis

Fragkiadakis

Τράγος

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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“…In [10] energy efficient nodes and relay nodes are placed in hierarchical manner to avoid uneven efficient routing mechanism is implemented to achieve balanced efficient inter-organizational wireless sensor data collection Framework (EnIF) dynamic inter-organizational collaborative topology towards saving oriented architecture. In [13] a popular technique for achieving described. CS's major advantage is that it can sample and compress information in a single step.…”

Section: Related Workmentioning

confidence: 99%

“…In [8] a state-of-the art review is presented, with a particul architectures, models and algorithms in Clouds and inter-Clouds for energy-efficient energy-efficient dynamic packet downloading from a cloud storage to Internet efficient network architecture is proposed to prolong the netw nodes and relay nodes are placed in hierarchical manner to avoid uneven energy drainage (energy routing mechanism is implemented to achieve balanced energy consumption. In [12] an organizational wireless sensor data collection Framework (EnIF) in designed organizational collaborative topology towards saving energy from data transmissions using a service n [13] a popular technique for achieving energy efficiency called Compressive Sensing (CS) . CS's major advantage is that it can sample and compress information in a single step.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient architecture for internet of things systems

2016

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In this paper some of the motivations for energy-efficient communications in wireless systems are described by highlighting emerging trends and identifying some challenges that need to be addressed to enable novel, scalable and energy-efficient communications. So an architecture for Internet of Things systems is presented, the purpose of which is to minimize energy consumption by communication devices, protocols, networks, end-user systems and data centers.Some electrical devices have been designed with multiple communication interfaces, such as RF or WiFi, using open source technology; they have been analyzed under different working conditions. Some devices are programmed to communicate directly with a web server, others to communicate only with a special device that acts as a bridge between some devices and the web server. Communication parameters and device status have been changed dynamically according to different scenarios in order to have the most benefits in terms of energy cost and battery lifetime. So the way devices communicate with the web server or between each other and the way they try to obtain the information they need to be always up to date change dynamically in order to guarantee always the lowest energy consumption, a long lasting battery lifetime, the fastest responses and feedbacks and the best quality of service and communication for end users and inner devices of the system.

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“…In recent years high speed and improved end quality results are emerged as important aspects of many digital systems like Clinical imaging [1], wireless communication [2] and IoT Applications [3] which give rise to both bandwidth and frequency. In general, almost in all fields of signal and image processing, data acquisition using sampling theory has been primarily used to narrow down the penalty gap that exists between desired qualities over rate of signal acquisition [4].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity FFT Factorization for CS Reconstruction

Radhika¹,

Prasad²,

Rao³

2020

IJEAT

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In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compressive sensing (CS) reconstruction. Among various methodologies used for CS reconstruction algorithms, Greedy-based orthogonal matching pursuit (OMP) approach provides better solution in terms of accurate implementation with complex computations overhead. Several computationally intensive arithmetic operations like complex matrix multiplication are required to formulate correlative vectors making this algorithm highly complex and power consuming hardware implementation. Computational complexity becomes very important especially in complex FFT models to meet different operational standards and system requirements. In general, for real time applications, FFT transforms are required for high speed computations as well as with least possible complexity overhead in order to support wide range of applications. This paper presents an hardware efficient FFT computation technique with twiddle factor normalization for correlation optimization in orthogonal matching pursuit (OMP). Experimental results are provided to validate the performance metrics of the proposed normalization techniques against complexity and energy related issues. The proposed method is verified by FPGA synthesizer, and validated with appropriatecurrently available comparative analyzes.

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Adaptive compressive sensing for energy efficient smart objects in IoT applications

Cited by 25 publications

References 17 publications

Rate-Adaptive Compressive Sensing for IoT Applications

Rate-Adaptive Compressive Sensing for IoT Applications

An energy-efficient architecture for internet of things systems

Low Complexity FFT Factorization for CS Reconstruction

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