Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

Osifeko, Martins O.; Hancke, Gerhard P.; Abu‐Mahfouz, Adnan M.

doi:10.3390/jsan9020021

Cited by 29 publications

(11 citation statements)

References 175 publications

(207 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the heart stroke rehabilitation system [95] in smart health care using LDA, MLP and SVM algorithms. Next-generation smart cities will have ultra-dense cellular IoT networks using high-performance machine learning algorithms [96][97][98][99][100].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

Haque

Blaabjerg

2021

Electronics

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

Haque

Blaabjerg

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…regarding the source, credibility, timeliness, context). It should be mentioned that AI algorithms have been applied to the preprocessing task itself in an attempt to (at least partly) automate the preprocessing towards self-preprocessing (Osifeko et al, 2020) as well as to detect whether changes in the data (e.g. less available data) require changes in the predictive models (so-called concept or data drift, see Gama et al, 2014).…”

Section: Digital Platforms For Aimentioning

confidence: 99%

Electronic Markets on digital platforms and AI

Alt

2021

Electron Markets

View full text Add to dashboard Cite

“…The role of digital technologies for the energy industry has been described by many authors [56][57][58][59][60][61][62][63][64][65][66].…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of Managerial Competences Transformation in the Polish Energy Industry

Pilipczuk¹

2021

Energies

View full text Add to dashboard Cite

Different technological, socio-economic, geopolitical, and demographic factors have a significant influence on labor markets. Currently, due to COVID-19, the global economy is in a challenging situation, and millions of people from different countries have lost their jobs. The employee’s mental health and well-being are in risk conditions. In the coming years, the Polish energy sector will face several transformations. Emerging technologies are intended to deal with the problems in energy management. One of the main industry forces is human capital, people who will be able to project and manage the innovative technologies. Thus, this paper examines the determinants of managerial competences transformation in the energy industry from the labor market perspective. The paper fulfills the research gap in the energy manager profession’s transformation in Poland. The aim of the paper was to present the current state of the energy manager profession in Poland. Two methodological approaches were used: the theoretical and practical approaches. Descriptive statistics are provided to present the labor market research results. The findings of the research can contribute to the literature and practice by applying them in the process of developing energy manager competency models, as well as in education programs and training courses for enterprises and universities.

show abstract

Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

Cited by 29 publications

References 175 publications

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Electronic Markets on digital platforms and AI

Determinants of Managerial Competences Transformation in the Polish Energy Industry

Contact Info

Product

Resources

About