**1. Introduction**

The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [1]. We are witnessing a rapid evolution, or rather, a transformation of our societies. Novel solutions are being developed and adopted in work and life, benefiting from the growing ability to monitor and analyze our environments in near real time. A range of devices and technologies are being used for monitoring purposes, including the Internet of Things (IoT), the Global Positioning System (GPS), sensors, cameras, Radio Frequency Identification (RFID) devices, smartphones, smartwatches, other smart wearables, and social media platforms. These devices produce diverse data that are analyzed using Artificial Intelligence (AI) and other computational intelligence methods and are used for decision-making purposes. While significant advances have been made in developing smart applications and technologies, a systematic effort to define and develop "smartness" is missing. An investigation into the theoretical and technological foundations of this "smartness" can help systemize and mass-produce technologies for autonomous production and for the operation of smart environments.

This Special Issue's focus is on the IoT, and it is concerned with bringing "smartness" to the IoT and other system layers using technologies such as Cloud, Fog, and Edge Computing; High-Performance Computing (HPC); Big Data; Blockchain; and/or AI. In addition to this Editorial piece, a collection of 13 articles is featured in this Special Issue, covering a range of topics, including mobility, healthcare, image analysis, permeable pavements, solid waste management, sensor node and gateway architectures, air quality monitoring, thermal anomalies and smart helmets in industrial environments, smart airports, smart districts, smart travel choices, sensor cities, artificially intelligent cities, and platform urbanism. Figure 1 provides a word cloud which represents the themes explored by this Special Issue.

Smartness is a multidisciplinary topic and can be defined from different perspectives. We see through the articles included in this Special Issue that smartness can be seen to have four dimensions (however, this is not the only way to look at it). These dimensions are: (i) Sensors, IoT, and Data Generation; (ii) Data and Information Processing; (iii) Actuation; and (iv) Digital Systems and Infrastructure. To elaborate, we can see smartness in the way sensing is embedded in a system, the way data and information are processed, how a system interacts internally and with its environment, and whether a system is ubiquitous or limited by space (cloud-based or edge-enabled). What follows is a brief review of the articles included in this Special Issue, which highlights their contributions with respect to these four dimensions. They are grouped according to their application areas: mobility and transportation, healthcare, industrial environments, and other urban infrastructures.

### **Citation:** Mehmood, R.;

Corchado, J.M.; Yigitcanlar, T. Developing Smartness in Emerging Environments and Applications with a Focus on the Internet of Things. *Sensors* **2022**, *22*, 8939. https:// doi.org/10.3390/s22228939

Received: 26 October 2022 Accepted: 11 November 2022 Published: 18 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** A Word Cloud of the Research Topics in this Special Issue.

### **2. Mobility and Transportation**

Transportation is the backbone of modern economies, albeit at massive human, environmental, and economic costs [2,3]. Lana et al. [4] assess that advances in data science have permeated into every field of transportation science and engineering, resulting in data-driven transportation developments. The authors describe how data from various intelligent transport system (ITS) sources can be used to learn and adapt data-driven models for the efficient operation of ITS assets, systems, and processes and how data-based models can become fully actionable. Furthermore, the authors define the characteristics, engineering requirements, and challenges inherent to the three compounding stages, namely, data fusion, adaptive learning, and model evaluation, based on the described data modelling pipeline for ITS. This work's theoretical framework contributes to the first three dimensions of smartness, namely, sensors, data processing, and actuation.

Chia et al. [5] use smart card data to investigate the relationship between the spatial distribution of relative transfer locations and the attractiveness of the transit service. Transfers are an important part of transit trips because they enable people to reach more destinations; however, they are also the main factor that deters people from using public transportation. The authors' findings imply that smart transit users may value travel direction in addition to travel time, which influences their mode choice. Depending on their relative location, travelers may prefer even adjacent transfer locations. The authors' findings will help improve our understanding of transit user behavior and the impact of transfer smartness, as well as smart transportation planning and the design of new transit routes and services to improve transfer performance. This work contributes to the second dimension (data processing) of smartness.

Alomari et al. [6] introduce their tool, Iktishaf+, which combines Big Data, Distributed Machine Learning, social media analytics, and an automatic data labeling method to detect road traffic-related events. It uses a range of technologies, including Apache Spark, Parquet, and MongoDB. The tool can detect and validate several real-world events in Saudi Arabia, including a fire in Jeddah, rain in Makkah, and an accident in Riyadh, without prior knowledge. The findings demonstrate the effectiveness of Twitter media in detecting important events when no prior knowledge of them is available. This work contributed to the first two dimensions of smartness, sensing, and data processing.

Motivated by the fact that over a billion people are disabled worldwide, with 253 million of them being visually impaired or blind, Busaeed et al. [7] propose an approach for collecting data and predicting objects for environment perception, mobility, and navigation

using a LiDAR with a servo motor and an ultrasonic sensor. The authors use this approach with a pair of smart glasses called LidSonic V2.0 to help the visually impaired identify obstacles. The LidSonic system consists of a smart glasses-integrated Arduino Uno edge computing device and a smartphone app that transmits data via Bluetooth. Arduino collects data, controls the smart glasses' sensors, detects obstacles with simple data processing, and provides buzzer feedback to visually impaired users. This work is related to indoor and outdoor mobility, and hence we group this with mobility and transportation research. This work contributes to the first two dimensions of smartness, namely, sensing, and data processing, and moreover, it touches upon the fourth dimension as it discusses its potential to be extended to Cloud and Edge Computing.

### **3. Industry 4.0**

The detection of anomalies in harsh industrial environments is a challenging task. To address this, Ghazal et al. [8] propose an Edge–Fog–Cloud architecture, based on mobile IoT edge nodes carried by autonomous robots, for detecting thermal anomalies in aluminum plants. The authors use companion drones as fog nodes and a cloud back-end to analyze thermal anomalies. Moreover, the authors propose a self-driving, deep learning architecture and a thermal anomaly detection and visualization algorithm. Their results show that the proposed robot surveyors are less expensive, have a shorter response time, and detect anomalies more accurately than human surveyors or fixed IoT nodes monitoring the same industrial area. This work contributes to the first, second, and fourth dimensions of smartness, i.e., sensing, data processing, and computing infrastructure.

Campero-Jurado et al. [9] discuss the role of information and communication technologies (ICTs) in advancing occupational health and safety and increasing worker security. Personal Protective Equipment (PPE) based on ICTs reduces the risk of workplace accidents due to the equipment's ability to make decisions based on environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) enable the generation of PPE models and the development of devices with more advanced capabilities such as monitoring, sensing the environment, and risk detection, among others. These models continuously monitor the working environment and notify employees and supervisors of any anomalies or threats. With this context, they propose a smart helmet prototype that monitors the working environment and performs a near real-time risk assessment. The sensor data are sent to an AI-powered platform for analysis. A comparative study of supervised learning models is carried out as part of this research. Furthermore, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of potential occupational risks. This work contributes to the first and second dimensions of smartness, namely, sensing and data processing.
