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Journal ArticleDOI

Data-driven optimizations in IoT: a new frontier of challenges and opportunities

Sharda Tripathi, +1 more
- 09 Mar 2019 - 
- Vol. 7, Iss: 1, pp 35-43
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.

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Citations
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Journal ArticleDOI

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.
Journal Article

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

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

Xinyang Huang
- 27 Jan 2022 - 
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

Application of Compressive Sampling in Synchrophasor Data Communication in WAMS

TL;DR: In this paper, areas of power system synchrophasor data communication which can be improved by compressive sampling (CS) theory are identified and it is shown that missing and bad data can be reconstructed satisfactorily using CS.
Journal ArticleDOI

Smart Metering Load Data Compression Based on Load Feature Identification

TL;DR: The generalized extreme value distribution characteristic for household load data is proposed and then utilizes it to identify load features including load states and load events and a highly efficient lossy data compression format is designed to store key information of load features.
Journal ArticleDOI

Fuzzy Transform Based Compression of Electric Signal Waveforms for Smart Grids

TL;DR: A fuzzy-based mathematical kernel is discussed which transforms the data into a new domain where their cardinality can be sensibly reduced, consequently allowing the development of more efficient data analysis algorithms.
Proceedings ArticleDOI

Efficient Ultra-Reliable and Low Latency Communications and Massive Machine-Type Communications in 5G New Radio

TL;DR: A multi-armed bandit (MAB) based reinforcement learning approach is proposed to achieve the optimum harmonization of feedback and feedbackless transmissions, and simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC.
Journal ArticleDOI

Frequency Prediction of Power Systems in FNET Based on State-Space Approach and Uncertain Basis Functions

TL;DR: In this article, the use of a state-space model and basis functions to predict power frequency is discussed, where expectation maximization (EM) and prediction error minimization (PEM) are used to dynamically estimate the model's parameters.
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