Journal ArticleDOI
Green Sensing and Communication: A Step Towards Sustainable IoT Systems
TLDR
This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications and presents a few case studies that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality.Abstract:
With the advent of Internet of Things (IoT) devices, their reconfigurability, networking, task automation, and control ability have been a boost to the evolution of traditional industries such as health-care, agriculture, power, education, and transport. However, the quantum of data produced by the IoT devices poses serious challenges on its storage, communication, computation, security, scalability, and system’s energy sustainability. To address these challenges, the concept of green sensing and communication has gained importance. This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications. Further, a few case studies are presented that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality. Challenges associated with these green techniques, various open issues, and future research directions for improving the energy efficiency of the IoT systems are also discussed.read more
Citations
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Proceedings ArticleDOI
Learning-based Smart Sensing for Energy-Sustainable WSN
TL;DR: In this article, a learning-based adaptive sampling framework was proposed to optimize the energy consumption of WSNs and increase the network lifetime by exploring the sparsity in the time series data and finding optimal sampling instants for the next measurement cycle.
Journal ArticleDOI
Local Reference-Free In-Field Calibration of Low-Cost Air Pollution Monitoring Sensors
TL;DR: In this paper , a cross correlation-based method of determining the optimum time to recalibrate the low-cost sensors in a multisensing node was proposed to eliminate the requirement of taking the MSNs offline to calibrate/recalibrate them.
Journal ArticleDOI
Local Reference-Free In-Field Calibration of Low-Cost Air Pollution Monitoring Sensors
TL;DR: A cross correlation-based method of determining the optimum time to recalibrate the low-cost sensors in a multisensing node is proposed, which eliminates the requirement of taking the MSNs offline to calibrate/recalibrate them.
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Automatic Recognition of Communication Signal Modulation Based on the Multiple-Parallel Complex Convolutional Neural Network
TL;DR: In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals, which learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output.
Journal ArticleDOI
URLLC in Beyond 5G and 6G Networks: An Interference Management Perspective
TL;DR: In this article , the authors present state-of-the-art research work, in-depth analysis of interference problems, and guidance on the futuristic 6G URLLC technologies and communication networks.
References
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TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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Subspace Pursuit for Compressive Sensing Signal Reconstruction
Wei Dai,Olgica Milenkovic +1 more
TL;DR: The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter.
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Subspace Pursuit for Compressive Sensing Signal Reconstruction
Wei Dai,Olgica Milenkovic +1 more
TL;DR: In this paper, the subspace pursuit algorithm was proposed for sparse signals with and without noisy perturbations, which has low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of LP optimization methods.
Journal ArticleDOI
Sparse Bayesian learning for basis selection
David Wipf,Bhaskar D. Rao +1 more
TL;DR: This paper adapts SBL to the signal processing problem of basis selection from overcomplete dictionaries, proving several results about the SBL cost function that elucidate its general behavior and providing solid theoretical justification for this application.