Machine Learning for Advanced Wireless Sensor Networks: A Review

Kim, Taeyoung; Vecchietti, Luiz Felipe; Choi, Kyu-Jin; Lee, Sangkeum; Har, Dongsoo

doi:10.1109/jsen.2020.3035846

Cited by 83 publications

(39 citation statements)

References 173 publications

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“…In this model, branches of the tree represent observations and leaves represent conclusions about the data target. The quality of the outcome depends on the criterion known as entropy, which has to be minimized and helps in judging the final outcome [43]. For example, someone wants to know if he should go fishing based on weather and pressure conditions.…”

Section: Decision Tree Learningmentioning

confidence: 99%

“…The largest variance of data helps in identifying the first axis, the second largest data variance along the orthogonal direction to the first axis identifies the second axis, and so on. This mechanism continues until all the axes are located [43]. That is how the unsupervised learning mechanism of PCA works and helps in processing the unlabeled data.…”

Section: Decision Tree Learningmentioning

confidence: 99%

“…The decision boundary is selected such that the gap between data points in the two classes is maximized. The decision boundary can be separated using linear as well as non-linear kernels [43]. Thus, it can be used to classify unlabeled data in healthcare for realizing interesting applications.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

2021

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Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

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Section: Decision Tree Learningmentioning

confidence: 99%

Section: Decision Tree Learningmentioning

confidence: 99%

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Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

2021

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“…2 highlights the taxonomies of existing ML approaches. Proliferated usage of ML approaches in WSN is seen in work of Kim et al [58]. Apart from the above discussed frequently used ML approaches, there are various other approaches too viz.…”

Section: B Machine Learning Approachmentioning

confidence: 99%

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

Kavya¹,

Ravi²

2021

IJACSA

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Energy Optimization in Wireless Sensor Network (WSN) deals with the techniques which targets higher degree of energy efficiency using resource-constraint sensor nodes with minimal inclusion of any different variants of resources. At present, there are various approaches and techniques towards addressing the problems of energy but not all the research implications can be considered as optimized approach. Therefore, this paper reviews all the existing energy optimization schemes, categorizes them, briefly discuss about their strength and weakness to offer a compact snapshot of existing energy optimization techniques in WSN. The paper also contributes towards exploring the updates research trends and highlights about the open-end research problems in WSN. It is anticipated that the study findings of this manuscript will offer a true picture of study effectiveness in dealing with energy challenges so that favorable direction of investigation towards evolving up optimized solution comes up with promising outcome.

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“…Recently, deep reinforcement learning (DRL) has been widely applied to deterministic games ( Silver et al, 2018 ), video games ( Mnih et al, 2015 ; Mnih et al, 2016 ; Silver et al, 2016 ), sensor networks ( Kim et al, 2020 ), and complex robotic tasks ( Andrychowicz et al, 2017 ; Hwangbo et al, 2019 ; Seo et al, 2019 ; Vecchietti et al, 2020 ; Vecchietti, Seo & Har, 2020 ). Despite the breakthrough results achieved in the field of DRL, deep learning in multi-agent environments that require both cooperation and competition is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Two-stage training algorithm for AI robot soccer

Kim

Vecchietti

Choi

et al. 2021

PeerJ Computer Science

Self Cite

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In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative behavior among different types of agents in a group is pursued. Learning a joint-action set during centralized training is an attractive way to obtain such cooperative behavior; however, this method brings limited learning performance with heterogeneous agents. To improve the learning performance of heterogeneous agents during centralized training, two-stage heterogeneous centralized training which allows the training of multiple roles of heterogeneous agents is proposed. During training, two training processes are conducted in a series. One of the two stages is to attempt training each agent according to its role, aiming at the maximization of individual role rewards. The other is for training the agents as a whole to make them learn cooperative behaviors while attempting to maximize shared collective rewards, e.g., team rewards. Because these two training processes are conducted in a series in every time step, agents can learn how to maximize role rewards and team rewards simultaneously. The proposed method is applied to 5 versus 5 AI robot soccer for validation. The experiments are performed in a robot soccer environment using Webots robot simulation software. Simulation results show that the proposed method can train the robots of the robot soccer team effectively, achieving higher role rewards and higher team rewards as compared to other three approaches that can be used to solve problems of training cooperative multi-agent. Quantitatively, a team trained by the proposed method improves the score concede rate by 5% to 30% when compared to teams trained with the other approaches in matches against evaluation teams.

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Machine Learning for Advanced Wireless Sensor Networks: A Review

Cited by 83 publications

References 173 publications

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

Two-stage training algorithm for AI robot soccer

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