Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Sodhro, Ali Hassan; Gurtov, Andrei; Zahid, Noman; Pirbhulal, Sandeep; Wang, Lei; Rahman, Muhammad Mahboob Ur; Imran, Muhammad Ali; Abbasi, Qammer H.

doi:10.1109/jiot.2020.3023667

Cited by 28 publications

(11 citation statements)

References 25 publications

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“…For instance, the Internet-of-Things (IoT) links objects wirelessly to a network that enables data sharing, and within this network AI is simultaneously analyzing IoT data and making decisions autonomously [10][11][12]. The smart home can be offered as an example of the popular application areas for this technology [13,14]. While highly innovative technologies-e.g., artificially intelligent internet-of-things (AI-IoT) [15]-are disrupting the industrial processes-i.e., Industry 4.0 [16]-, they are disrupting our cities and societies as well-i.e., smart city and smart community [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

Yiğitcanlar

Corchado

Mehmood

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

144

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The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

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Section: Introductionmentioning

confidence: 99%

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

Yiğitcanlar

Corchado

Mehmood

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

144

View full text Add to dashboard Cite

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“…Ref. [ 39 ] proposes two video transmission algorithms for energy-efficient video streaming through wireless micro medical devices for smart healthcare homes.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Environmental Impact of Green IoT Deployments

Baldini

Chessa

Brogi

2023

Sensors

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The Internet of Things (IoT) is demonstrating its huge innovation potential, but at the same time, its spread can induce one of highest environmental impacts caused by the IoT industry. This concern has motivated the rise of a new research area aimed at devising green IoT deployments. Our work falls in this research area by contributing to addressing the problem of assessing the environmental impact of IoT deployments. Specifically, we propose a methodology based on an analytical model to assess the environmental impact of an outdoor IoT deployment powered by solar energy harvesting. The model inputs the specification of the IoT devices that constitute the deployment in terms of the battery, solar panel and electronic components, and it outputs the energy required for the entire life-cycle of the deployment and the waste generated by its disposal. Given an existing IoT deployment, the models also determine a functionally equivalent baseline green solution, which is an ideal configuration with a lower environmental impact than the original solution. We validated the proposed methodology by means of the analysis of a case study conducted over an existing IoT deployment developed within the European project RESCATAME. In particular, by means of the model, we evaluate the impact of the RESCATAME system and assess its impact with respect to its baseline. In a scenario with a 30-year lifespan, the model estimates for the system more than 3 times the energy required by its baseline green solution and a waste for a volume 15 times greater. We also show how the impact of the baseline increases when assuming deployments in locations at increasing latitudes. Finally, the article presents an implementation of the proposed methodology as a web service that is publicly available.

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“…Studies [28][29][30][31][32] related to the application of AI in the field of Smart Home can also be cited. Sodhro et al [28] state that the convergence of IoT and AI promotes energyefficient communication in smart homes. Its main objective is to optimize the Quality of Service (QoS) of video transmission, which is carried out using wireless micro medical devices (WMMD), in smart healthcare homes.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligent system for multimedia services in smart home environments

et al. 2021

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Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.

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Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Cited by 28 publications

References 25 publications

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

Estimating the Environmental Impact of Green IoT Deployments

Artificial intelligent system for multimedia services in smart home environments

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