Emerging ICT applications and services—Big data, IoT, and cloud computing

Park, Jong Hyuk; Younas, Muhammad; Arabnia, Hamid R.; Chilamkurti, Naveen

doi:10.1002/dac.4668

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…A smart city is an urban area that employs Information and Communication Technologies (ICT) [1] , an intelligent network of connected devices and sensors that can work interdependently [2,3] and a distributive manner [4] to continuously monitor the environment, collect data, and share them among the other assets in the ecosystem. A smart city uses all the available data to make real-time decisions about the many individual components of the city to ease up the livelihood of its citizens, and make the whole system more efficient, more environmentally friendly, and more sustainable [5].…”

Section: Introductionmentioning

confidence: 99%

Embodied AI-Driven Operation of Smart Cities: A Concise Review

Shenavarmasouleh¹,

Mohammadi²,

Amini³

et al. 2021

Preprint

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<div>A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient.</div><div>Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays.</div><div>In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.</div>

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Section: Introductionmentioning

confidence: 99%

Embodied AI-Driven Operation of Smart Cities: A Concise Review

Shenavarmasouleh¹,

Mohammadi²,

Amini³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Smart cities are created to enable advanced capabilities, such as sustainable energy systems and electrified transportation networks, and interact with information and communication technologies (ICTs) to enhance the efficiency of the cities being generated [9,10]. An example of responding to new changes for smart cities may be seen in the progress being made in smart healthcare systems for emergency care cases.…”

Section: Introductionmentioning

confidence: 99%

Data Analytics for Smart cities: Challenges and Promises

Mohammadi¹,

Shenavarmasouleh²,

Amini³

et al. 2021

Preprint

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The explosion of advancements in artificial intelligence, sensor technologies, and wireless communication activates ubiquitous sensing through distributed sensors. These sensors are various domains of networks that lead us to smart systems in healthcare, transportation, environment, and other relevant branches/networks. Having collaborative interaction among the smart systems connects end-user devices to each other which enables achieving a new integrated entity called Smart Cities. The goal of this study is to provide a comprehensive survey of data analytics in smart cities. In this paper, we aim to focus on one of the smart cities important branches, namely Smart Mobility, and its positive ample impact on the smart cities decision-making process. Intelligent decision-making systems in smart mobility offer many advantages such as saving energy, relaying city traffic, and more importantly, reducing air pollution by offering real-time useful information and imperative knowledge. Making a decision in smart cities in time is challenging due to various and high dimensional factors and parameters, which are not frequently collected. In this paper, we first address current challenges in smart cities and provide an overview of potential solutions to these challenges. Then, we offer a framework of these solutions, called universal smart cities decision making, with three main sections of data capturing, data analysis, and decision making to optimize the smart mobility within smart cities. With this framework, we elaborate on fundamental concepts of big data, machine learning, and deep leaning algorithms that have been applied to smart cities and discuss the role of these algorithms in decision making for smart mobility in smart cities.

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“…Researchers try to maximize the quality of service (QoS) and communication quality in future smart cities [1]. Others [2] try to find new solutions for many problems facing this field of information. Mobile devices and autonomous systems are now available next to users while generating large volumes of data that can be used in many machine learning and artificial intelligence applications.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

On-Demand Client Deployment And Selection In Federated Learning. (c2022)

Chahoud¹

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Traditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients on the fly wherever and whenever needed. In fact, some devices are not available to serve as clients in the federated learning due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in federated learning offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed.

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Emerging ICT applications and services—Big data, IoT, and cloud computing

Cited by 9 publications

References 15 publications

Embodied AI-Driven Operation of Smart Cities: A Concise Review

Embodied AI-Driven Operation of Smart Cities: A Concise Review

Data Analytics for Smart cities: Challenges and Promises

On-Demand Client Deployment And Selection In Federated Learning. (c2022)

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