Journal ArticleDOI
Data-driven optimizations in IoT: a new frontier of challenges and opportunities
Sharda Tripathi,Swades De +1 more
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TLDR
IoT data driven unique communication approaches and optimization techniques to reduce the data handling footprint, leading to communication bandwidth, cloud storage, and energy saving, without compromising the service quality are presented.Abstract:
Internet of Things (IoT) has gained tremendous popularity with the recent fast-paced technological advances in embedded programmable electronic and electro-mechanical systems, miniaturization, and their networking ability. IoT is expected to change the way of human activities by extensively networked monitoring, automation, and control. However, widespread application of IoT is associated with numerous challenges on communication and storage requirements, energy sustainability, and security. Also, IoT data traffic as well as the service quality requirements are application-specific. Through a few practical example cases, this article presents IoT data driven unique communication approaches and optimization techniques to reduce the data handling footprint, leading to communication bandwidth, cloud storage, and energy saving, without compromising the service quality. Subsequently, it discusses newer challenges that are needed to be tackled, to make the IoT applications practically viable for their wide-ranging adoption.read more
Citations
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Adaptive Multivariate Data Compression in Smart Metering Internet of Things
TL;DR: Performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver.
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Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid
TL;DR: In this article, a Deep Learning based Locational Detection technique is proposed to continuously recognize the specific areas of FDIA, which is based on the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN).
Journal ArticleDOI
Edge Intelligence Framework for Data-Driven Dynamic Priority Sensing and Transmission
TL;DR: A novel edge intelligence-based data-driven priority-aware sensing and transmission framework that saves up to 41% energy and 32% bandwidth with 68% data accuracy compared to the existing competitive frameworks for non-real-time systems.
Journal ArticleDOI
Channel-Adaptive Transmission Protocols for Smart Grid IoT Communication
Sharda Tripathi,Swades De +1 more
TL;DR: Novel channel prediction frameworks using stochastic modeling as well as data-driven learning of channel variability are proposed, which are complemented with an adaptive channel coding scheme to increase the transmission reliability of time-critical grid monitoring data over a wireless channel.
Journal ArticleDOI
A Data-Driven WSN Security Threat Analysis Model Based on Cognitive Computing
TL;DR: A simulation platform based on OMNeT++ to make up for the shortcomings of current WSN simulation platforms, improve the simulation capability of WSN security protocols, and provide a new technical means for designing and verifying security protocols is developed.
References
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Journal ArticleDOI
Dynamic Prediction of Powerline Frequency for Wide Area Monitoring and Control
Sharda Tripathi,Swades De +1 more
TL;DR: A novel data driven framework based on Support Vector Regression to reduce the bandwidth requirement for transmission of phasor measurement unit (PMU) data by judicious elimination of redundant data at the PMU before transmission is presented.
Journal ArticleDOI
SBL-Based Adaptive Sensing Framework for WSN-Assisted IoT Applications
Vini Gupta,Swades De +1 more
TL;DR: A novel sparse Bayesian learning-based adaptive sensor selection framework that selects an active sensor set and turns off the remaining SNs by jointly optimizing two conflicting performance measures: 1) sensing quality and 2) energy efficiency, while considering prevailing energy parameters of the network.
Proceedings ArticleDOI
Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor Hub
TL;DR: It is demonstrated that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information.
Proceedings ArticleDOI
Feasibility analysis on integrated recharging and data collection in pollution sensor networks
TL;DR: An extensive study on the energy consumptions with a few chosen pollution sensor examples is done, and the required recharging periodicity is derived and forms the basis of constrained mobility and path planning of the mobile robot.
Proceedings ArticleDOI
Assessment of power system stability using reduced-rate synchrophasor data
Sharda Tripathi,Swades De +1 more
TL;DR: A data driven approach based on o-Support Vector Regression (o-SVR) to identify the dependence of present sample of power-line frequency on past few samples to reduce the sampling rate at a PMU or transmission rate of the fixed-rate samples from aPMU to the PDC such that any impending disturbance in the power system can be detected early without compromising stability of thePower system.
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