Rethinking IoT Network Reliability in the Era of Machine Learning

Fafoutis, Xenofon; Marchegiani, Letizia

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00189

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…For the purposes of this work, we also considered the k-Nearest Neighbours (kNN), Support Vector Machine (SVM), and Deep Neural Network (DNN) classifiers. All four classifiers yield comparable results in terms of accuracy (see Reference [36] for detailed results). Ultimately, we selected Random Forest.…”

Section: Classificationmentioning

confidence: 89%

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Zalewski

Marchegiani

Elsts

et al. 2020

Sensors

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Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

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Section: Classificationmentioning

confidence: 89%

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Zalewski

Marchegiani

Elsts

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Software needs to be optimized together with the hardware, so developers need to introduce energy-aware constraints during the development of G-IoT solutions. Such optimizations are especially critical for certain digital signal processing tasks such as compression, feature extraction, or machine learning training [80].…”

Section: G-iot Carbon Footprintmentioning

confidence: 99%

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Fraga‐Lamas

Lopes

Fernández‐Caramés

2021

Sensors

200

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Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.

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“…Moreover, IoT extends beyond smart consumer devices by introducing wireless embedded systems in crucial structures, including, among others, health and care services, industrial sensor networks, and municipal infrastructures 10 . Reliable sensor networks are required because of these IoT application areas' crucial nature 11 . Therefore, this review investigates the effect of meta‐heuristic and nature‐inspired algorithms in creating a reliable network to overcome the problems of IoT networks.…”

Section: Introductionmentioning

confidence: 99%

“…10 Reliable sensor networks are required because of these IoT application areas' crucial nature. 11 Therefore, this review investigates the effect of meta-heuristic and nature-inspired algorithms in creating a reliable network to overcome the problems of IoT networks.…”

Section: Introductionmentioning

confidence: 99%

An application of meta‐heuristic and nature‐inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

Gong

2023

Int J Communication

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The widespread use of Internet of Things (IoT) in various wireless sensor networks applications has increased their importance in recent years. IoT is a smart technology that connects anything anywhere at any time. These smart objects, which connect the physical world with the world of computing infrastructure, are expected to permeate all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, and transportation. As wireless networking expands, the disadvantage of wireless communication is clearly obvious. People's apprehension over the IoT's dependability has therefore skyrocketed. IoT networks' key requirements are dependability, channel security, fault tolerance, and reliability. Monitoring the IoT networks depends on the availability and correct functioning of all the network nodes. Recent research has proposed promising solutions to address these challenges. This article systematically examines recent articles that use meta-heuristic and nature-inspired algorithms to establish reliable IoT networks. Eighteen articles were analyzed in four groups. Results showed that reliable enhancement mechanisms in IoT networks increase fault node detection, network efficiency, and lifetime and attain energy optimization results in the IoT concept. Additionally, it was discovered in the literature that the current studies focus on how to effectively use edge network capabilities for IoT application executions and support, along with the related needs.

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Rethinking IoT Network Reliability in the Era of Machine Learning

Cited by 7 publications

References 30 publications

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

An application of meta‐heuristic and nature‐inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

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