Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Hu, Zhiwen; Bai, Zixuan; Bian, Kaigui; Wang, Tao; Song, Lingyang

doi:10.1109/jiot.2019.2900751

Cited by 19 publications

(16 citation statements)

References 23 publications

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“…Hu et al, [17] present the architecture, implementation, and optimization of an air quality sensing system, which provides real time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of the system is studied in detail minimizing the average joint error of the established real time air quality map, which involves data inference for the unmeasured data values.…”

Section: B Smart City and Pollutionmentioning

confidence: 99%

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

Fadda

Anedda

Girau

et al. 2023

IEEE Internet Things J.

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In the last years, the smart city paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new smart city based on Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city.

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Section: B Smart City and Pollutionmentioning

confidence: 99%

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

Fadda

Anedda

Girau

et al. 2023

IEEE Internet Things J.

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“…Other examples of delay-tolerant applications include smart parking [14], intelligent waste management [15], infrastructure (e.g., bridges, railways, etc.) monitoring [16], air quality management [17], noise monitoring [18], smart city lighting [19], smart management of city energy consumption [20], and automation of public buildings such as schools, museums, and administration offices to automatically and remotely control lighting and air condition [21].…”

Section: B Network Slicing For Heterogeneous Iov and Smart City Demandsmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

Nassar¹,

Yılmaz²

2020

Preprint

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Intelligent vehicular systems and smart city applications are the fastest growing Internet of things (IoT) implementations at a compound annual growth rate of 30%. In view of the recent advances in IoT devices and the emerging new breed of IoT applications driven by artificial intelligence (AI), fog radio access network (F-RAN) has been recently introduced for the fifth generation (5G) wireless communications to overcome the latency limitations of cloud-RAN (C-RAN). We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments. We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) to efficiently utilize the limited resources at the network edge. For each service request in a cluster, the EC decides which FN to execute the task, i.e., locally serve the request at the edge, or to reject the task and refer it to the cloud. We formulate the problem as infinite-horizon Markov decision process (MDP) and propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy. The performance of the proposed DRL-based slicing method is evaluated by comparing it with other slicing approaches in dynamic environments and for different scenarios of design objectives. Comprehensive simulation results corroborate that the proposed DRL-based EC quickly learns the optimal policy through interaction with the environment, which enables adaptive and automated network slicing for efficient resource allocation in dynamic vehicular and smart city environments.

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“…The introduction of inexpensive small sensors allows retrieving a huge amount of data in real-time fashion (Hu et al, 2019). Effective machine learning techniques are implemented in this study to perform such a real-time air quality assessment.…”

Section: ) Visualization Layermentioning

confidence: 99%

“…Real-time monitoring stations are built by many cities to check the air quality, then inform people when it is safe to conduct outside activities and plan better movements (Zhang & Woo, 2020). Systems for collecting and assessing air quality have been installed in several areas, e.g., Peking University (with 100 thousand data from 30 devices) (Hu et al, 2019), Christchurch that is a part of IBM's smart city initiatives (Marek et al, 2017), Los Angeles (Wu et al, 2017), etc. Various information systems are implemented for supporting the data collection and air quality information transfer to the people.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction in Smart City’s Information System

Singgih¹

2020

INJIISCOM

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The introduction of new technology and computational power enables more data usages in a city. Such a city is called a smart city that records more data related to daily life activities and analyzes them to provide better services. Such data acquisition and analysis must be conducted quickly to support real-time information sharing and support other decision-making processes. Among such services, an information system is used to predict the air quality to ensure people's health in the city. The objective of this study is to compare various machine learning techniques (e.g., random forest, decision tree, neural network, naïve Bayes, etc.) when predicting the air quality in a city. For the comparison, we perform the removal of records with empty values, data division into training and testing datasets, and application of the k-fold cross-validation method. Numerical experiments are performed using a given online dataset. The results show that the three best methods are random forest, Gradient Boosting, and k-nearest neighbors with precision, recall, and f1-score values more than 0.63

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Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Cited by 19 publications

References 23 publications

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

Air Quality Prediction in Smart City’s Information System

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