Learning paradigms for communication and computing technologies in IoT systems

Ejaz, Waleed; Basharat, Mehak; Saadat, Salman; Khattak, Asad Masood; Naeem, Muhammad; Anpalagan, Alagan

doi:10.1016/j.comcom.2020.01.043

Cited by 18 publications

(8 citation statements)

References 85 publications

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“…The open issues and challenges of IoT are also identified for further research enhancement. Ejaz et al (2020) present IoT research, including the integration of learning techniques from an optimization viewpoint, with optimization targets classified as maximization, reduction, and associated applications. It also covers learning algorithms for key performance indicators (KPIs) used to evaluate the performance of IoT systems.…”

Section: Related Workmentioning

confidence: 99%

Factors Influencing the Adoption of IoT for E-Learning in Higher Educational Institutes in Developing Countries

et al. 2022

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The internet of things (IoT) is an emerging paradigm of educational applications and innovative technology in the current era. While capabilities are increasing day by day, there are still many limitations and challenges to utilizing these technologies within E-Learning in higher educational institutes (HEIs). The IoT is well-implemented in the United States of America (USA), United Kingdom (UK), Japan, and China but not in developing countries, including Saudi Arabia, Malaysia, Pakistan, Bangladesh, etc. Few studies have investigated the adoption of IoT in E-Learning within developing countries. Therefore, this research aims to examine the factors influencing IoT adoption for E-Learning to be utilized in HEIs. Further, an adoption model is proposed for IoT-based E-Learning in the contexts of developing countries and provides recommendations for enhancing the IoT adoption for E-Learning in HEIs. The IoT-based E-Learning model categorizes these influencing factors into four groups: individual, organizational, environmental, and technological. Influencing factors are compared along with a detailed description in order to determine which factors should be prioritized for efficient IoT-based E-Learning in HEIs. We identify the privacy (27%), infrastructure readiness (24%), financial constraints (24%), ease of use (20%), support of faculty (18%), interaction (15%), attitude (14%), and network and data security (14%), as the significant E-Learning influencing factors on IoT adoption in HEIs. These findings from the researcher's perspective will show that the national culture has a significant role in the individual, organizational, technological, and environmental behavior toward using new technology in developing countries.

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Section: Related Workmentioning

confidence: 99%

Factors Influencing the Adoption of IoT for E-Learning in Higher Educational Institutes in Developing Countries

et al. 2022

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“…The embracing of Wireless Sensor Networks (WSN), low power communication technology with small embedded devices led to the technical convergence of IoT [35]. IoT offers several refined and ubiquitous solutions in various applications, such as intelligent transportation systems, governance, environment monitoring, smart homes, smart health, and quality of life [36]. Sustainability has become a key issue nowadays for the world population, as the dynamic and progressive advancement in the area of IoT technologies are leading to totally different helpful edges, however this fast growing developments must be rigorously monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and make sure the smart utilization of limited world resources.…”

Section: B Role Of Internet Of Things (Iot) In Water Sectormentioning

confidence: 99%

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

et al. 2022

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Water distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of the water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technologies (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks -IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that to build a robust water distribution network, these components need to be designed and implemented effectively. INDEX TERMS Internet of things, IoT communication technologies, IoT services, water distribution networkThis article has been accepted for publication in IEEE Access.

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“…3 Channel sharing with other technologies is described in Section VII where, among others, we address sensor and vehicular networks. Additionally, we refer the readers to [225], in which different learning paradigms for IoT communication and computing technologies are surveyed, and to [226], in which ML-supported detection and identification of IoT devices is surveyed.…”

Section: Coexistence Scenariosmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

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Learning paradigms for communication and computing technologies in IoT systems

Cited by 18 publications

References 85 publications

Factors Influencing the Adoption of IoT for E-Learning in Higher Educational Institutes in Developing Countries

Factors Influencing the Adoption of IoT for E-Learning in Higher Educational Institutes in Developing Countries

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

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