A Survey of Explainable Artificial Intelligence for Smart Cities

Javed, Abdul Rehman; Ahmed, Waqas; Pandya, Sharnil; Maddikunta, Praveen Kumar Reddy; Alazab, Mamoun; Gadekallu, Thippa Reddy

doi:10.3390/electronics12041020

Cited by 91 publications

(43 citation statements)

References 191 publications

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“…The bond between Machine Learning and the issues related to smart cities' development is rather solid and on-going, as the most-recent scientific literature denotes [9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Pagano,

Barbierato

2023

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Air pollution is a paramount issue, influenced by a combination of natural and anthropogenic sources, various diffusion modes, and profound repercussions for the environment and human health. Herein, the power of time series data becomes evident, as it proves indispensable for capturing pollutant concentrations over time. These data unveil critical insights, including trends, seasonal and cyclical patterns, and the crucial property of stationarity. Brescia, a town located in Northern Italy, faces the pressing challenge of air pollution. To enhance its status as a smart city and address this concern effectively, statistical methods employed in time series analysis play a pivotal role. This article is dedicated to examining how ARIMA and LSTM models can empower Brescia as a smart city by fitting and forecasting specific pollution forms. These models have established themselves as effective tools for predicting future pollution levels. Notably, the intricate nature of the phenomena becomes apparent through the high variability of particulate matter. Even during extraordinary events like the COVID-19 lockdown, where substantial reductions in emissions were observed, the analysis revealed that this reduction did not proportionally decrease PM2.5 and PM10 concentrations. This underscores the complex nature of the issue and the need for advanced data-driven solutions to make Brescia a truly smart city.

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“…The bond between Machine Learning and the issues related to smart cities' development is rather solid and on-going, as the most-recent scientific literature denotes [9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Pagano,

Barbierato

2023

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“…The wasted time spent looking for suitable parking places, the disturbance of more traffic, and pollution in the cities directly impact driving activity. According to Javed et al, 6 motorists looking for parking account for 30% of traffic bottlenecks. In this case, knowing how many parking places are available ahead of time would solve the issue.…”

Section: Introductionmentioning

confidence: 99%

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT‐based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO‐based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

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“…XAI is the set of techniques and methods to convert the so-called black-box AI algorithms to white-box algorithms, where the results achieved by these algorithms and the variables, parameters, and steps taken by the algorithm to reach the obtained results, are transparent and explainable [1]. The most-cited benefits of XAI are improved decision-making, increased trust and transparency, improved user satisfaction, debugging and model improvement, and better compliance with regulations [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Building XAI-Based Agents for IoT Systems

2023

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The technological maturity of AI solutions has been consistently increasing over the years, expanding its application scope and domains. Smart home systems have evolved to act as proactive assistants for their residents, autonomously detecting behavioral patterns, inferring needs, and making decisions pertaining to the management and control of various home subsystems. The implementation of explainable AI (XAI) solutions in this challenging domain can improve user experience and trust by providing clear and understandable explanations of the system’s behavior. The article discusses the increasing importance of explainable artificial intelligence (XAI) in smart home systems, which are becoming progressively smarter and more accessible to end-users, and presents an agent-based approach for developing explainable Internet of things (IoT) systems and an experiment conducted at the Centre of Real Time Computer Systems at the Kaunas University of Technology. The proposed method was adapted to build an explainable, rule-based smart home system for controlling light, heating, and ventilation. The results of this study serve as a demonstration of the feasibility and effectiveness of the proposed theoretical approach in real-world scenarios.

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A Survey of Explainable Artificial Intelligence for Smart Cities

Cited by 91 publications

References 191 publications

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Building XAI-Based Agents for IoT Systems

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