An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things

Pease, Sarogini Grace; Trueman, Russell; Davies, Callum; Grosberg, Jude; Yau, Kai Hin; Kaur, Navjot; Conway, Paul; West, Andrew A.

doi:10.1016/j.future.2017.09.026

Cited by 34 publications

(6 citation statements)

References 24 publications

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“…Other notable systems for energy monitoring include the OpenEnergy Monitor and ACme. OpenEnergy uses open source Arduino boards in a three-phase power metering system that measures apparent and real power, and Root Mean Square (RMS) voltage and current, while ACme measures active, reactive, and apparent power using the wireless technology 6LoWPAN for small devices with limited processing (Jiang et al, 2009 ; Pease et al, 2018 ). These different monitoring systems could help to push the boundaries of indirect emissions accounting in a multitude of sectors.…”

Section: Potential Digital Data Collection Solutionsmentioning

confidence: 99%

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

et al. 2020

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Climate change has been called "the defining challenge of our age" and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.

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Section: Potential Digital Data Collection Solutionsmentioning

confidence: 99%

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

et al. 2020

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“…CoAP is a lightweight IoT protocol which shares similarities Fig. 4 Overview of the proposed IoTE-Fall system with HTTP since it includes resource abstraction, URI use, RESTful interaction (i.e., methods such as GET, POST, PUT, and DELETE to access the various resources), and extensible header options [63]. However, compared with HTTP, CoAP implementation uses minimal resources on the constrained devices and constrained networks.…”

Section: Wearable Devicementioning

confidence: 99%

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

Yacchirema

Puga

Palau

et al. 2019

Pers Ubiquit Comput

141

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Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.

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“…In order to optimize the cumulative consumption of energy in the plant an adaptive cyber‐physical tool set was developed and validated 8 . Further, increasing the network throughput by energy conservation that is exclusively during the process of routing has been developed by Shah et al 9 A survey on different IoT cloud platforms under different parametric constraints has also been presented by Ray 10 .…”

Section: Introduction and Related Workmentioning

confidence: 99%

Adaptive extreme learning machine ‐ Fuzzy system framework for energy optimization of IOTs in wireless sensor networks

Nachimuthu

Jeya

Devi

2021

Internet Technology Letters

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In this paper, a new adaptive extreme learning machine (ELM) neural network-Fuzzy system framework is developed for effective and efficient network energy optimization of internet-of-things (IoTs) sensor nodes in wireless sensor networks (WSNs). Each sensor nodes in a WSN communicates with one another in varied methods to transfer the data from IoT cloud to the other virtual modes. Optimization of IoTs in sensor networks tends to handle the network energy and accuracy with the help of extremely complex clustering techniques. Henceforth, this paper has developed a novel adaptive ELM-Fuzzy system framework to handle the network energy level in an optimum way and thus achieving better data accuracy and network throughput by reducing the overhead of the network to the maximum possible. Numerical simulations carried out proves the consistency and efficacy of the modeled novel adaptive ELM-Fuzzy system framework for IoT based sensor network on comparing with traditional techniques from previous literatures.

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An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things

Cited by 34 publications

References 24 publications

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

Adaptive extreme learning machine ‐ Fuzzy system framework for energy optimization of IOTs in wireless sensor networks

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