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This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device's ability to function under various adverse conditions. The results demonstrate the system's capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments. [ABSTRACT FROM AUTHOR]

Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city. [ABSTRACT FROM AUTHOR]

One of the most well-known uses of smart cities has been massive multimedia offerings. But the enormous demand for device-to-access point (D2A) communication has significantly increased the energy consumed and the greenhouse impact. As a result, energy-harvesting-based two-way access points (AP) and space shift keying (SSK) modulation are used to analyse the error performance of smart users in the smart city. Communication between smart users and a centralised AP is crucial in a smart metropolis. In this instance, energy harvesting at the two-way AP over the Saleh–Valenzuela channel has been taken into consideration using the simultaneous wireless information and power transmission (SWIPT) system. For smart users, SWIPT can remove the need for battery replacement or meet energy needs. In contrast, SSK modulation is a less complex method that does not need as many radio-frequency chains, inter-antenna synchronisation, or inter-channel interference for multiple-input-multiple-output transmission. Applying multi-user selection combining, smart sensors' average bit error probability efficiency is derived in closed form. The numerical and simulation assessment results demonstrate how energy harvesting affects the system's effectiveness. [ABSTRACT FROM AUTHOR]

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes. [ABSTRACT FROM AUTHOR]

Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility. [ABSTRACT FROM AUTHOR]

This paper explores development and deployment of a smart city prediction system, demonstrating this capability on data generated by footfall counting sensors. Presented approach integrates classical machine learning (ML) techniques with process orchestration framework Apache Airflow. The architecture is designed to handle datasets in periodic batches, ensuring updates are regularly integrated into the prediction system and new predictions are created at every increment. Our work demonstrates ease at which similar systems can be developed, given sufficient volume of data and availability of compute power. This approach highlights that increasing number of smart sensors, availability of proven ML techniques and modern processing frameworks create a critical mass for proliferation of real-time forecasting solutions. Our results indicate that the developed system is effective in predicting footfall patterns, a variable that can be instrumental in applications such as traffic control, resource allocation, public safety, and urban planning. Used methodology is not limited to footfall data, and can be applied to other timeseries datastreams, making it a versatile tool for smart city context. Showcasing practical implementation and benefits of the system, the paper contributes to the ongoing efforts in developing a class of digital urban infrastructure. [ABSTRACT FROM AUTHOR]

This study examines outlier detection in time-series sensor data from PurpleAir low-cost sensors in Athens, Greece. Focusing on key environmental parameters such as temperature, humidity, and particulate matter (PM) levels, the study utilizes the Interquartile Range (IQR) and Generalized Extreme Studentized Deviate (GESD) methods on hourly and daily basis. GESD detected more outliers than IQR, most of them in PM, while temperature and humidity data had fewer outliers; applying filters before outlier detection and adjusting alpha values based on time scales were crucial, and outliers significantly affected spatial interpolation, emphasizing the need for spatial statistics in smart city air quality management. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), sensor nodes were considered to be an integral part of IoT (Internet of Things) for sensing and monitoring the environment. The IoT-based applications need to be optimized regarding the changing requirements of users as everything is connected via the internet. In today's era, where every day new technologies were rebuilt where sensor nodes plays an important role on it. In every field, whether it is healthcare, smart agriculture, smart home appliances, smart traffic, or smart city sensors were deployed for sensing their environment, collecting data from them, and forwarding it to the servers. These sensor nodes were made up of non-rechargeable power batteries, as a fact efficient energy consumption of these batteries becomes vital. In WSN, efficient energy consumption is still an issue, and its solutions were given by many researchers among them, clustering is considered to be more effective in this domain. For efficient energy consumption, multi-attributes of cluster head selection need to be considered and proper coordination among the conflicting nature of multi-attributes needs to be done. In this paper, we have proposed PROMETHEE II and modified AHP together for cluster heads selection by considering multi-attributes. Twenty-one attributes were considered including connectivity, distance to the base station, residual energy, member nodes, and many more. Being conflicting in nature, proper coordination among these attributes has been done and optimal cluster heads were selected modified for data transmissions. In this paper, modified AHP has been compared with our proposed modified PROMETHEE II and AHP for understanding the significance of this integration. Results is evaluated in terms of energy consumption, network lifetime, and load balancing and it also validate that our proposed approach outperforms with modified AHP and other existing algorithms. Our proposed algorithm enriches network lifetime by balancing the load among sensor nodes which leads to efficient energy consumption. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad‐ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three‐tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to develop an implementation guideline for manufacturing companies integrating ISO 9001 requirements and Industry 4.0 technologies. The implementation guideline follows the Plan-Do-Check-Act model of ISO 9001 standard. We interviewed 25 experts and observed the quality management documentation of seven European manufacturing companies. Results emerged using thematic content analysis and we gained further insights from interviewees, allowing us to propose suggestions garnered from the interviews. The guideline demonstrates how requirements can be integrated with Industry 4.0 technologies such as radiofrequency identification tags, smart sensors, the internet of things, smart human interface, enterprise resource planning modules, artificial intelligence to improve a typical ISO 9001 quality management system. Some issues emerged relating to the fear of losing jobs, validation of quality control equipment based on artificial intelligence, and decision-making and problem-solving processes. The implications of this research are mainly related to the opening of a new debate concerning the integration between ISO 9001 and Industry 4.0 as well as the proposal, to our knowledge, of the first tentative integration. Manufacturing companies can use the guideline for integrating their quality management system with Industry 4.0 technologies while considering also the practical suggestions provided by the experts. [ABSTRACT FROM AUTHOR]

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Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained and validated with real data from the city of Madrid in Spain for 84 weeks, combining information from 4372 traffic measuring points and 143 COVID-19 PCR test centers. The results are compared with a baseline model designed for the extraction of spatio-temporal patterns from COVID-19-only sequences of images, showing that using traffic information enhances the results when forecasting a new wave of infections (MSE values are reduced by a 70% factor). The information that traffic data has on the spread of the COVID-19 virus is also analyzed, showing that traffic data alone is not sufficient for accurate COVID-19 forecasting. [ABSTRACT FROM AUTHOR]

The concept of smart sustainable cities—as a favorable response to different challenges faced in urban areas—is rapidly gaining momentum and worldwide attention. This trend has driven the exploration of various technologies to improve the utilization of limited resources and idling capacities (i.e., underutilized physical assets such as buildings or facilities). In fact, a new business model has been introduced recently to smart cities, known as "shared economy". The shared economy is a socioeconomic system that enables intermediary exchanges of goods and services between people and/or organizations, which boosts productivity and leverages underutilized resources to maximum potential. However, one of the inherent issues hindering the wide adoption of shared economy systems is the lack of trust between the providers and users of such systems. To this end, this study focuses on long-term shared properties/buildings and proposes an intelligent, IoT-enabled device and dynamic pricing model to address the issue of information asymmetry. First, 10 indicators were identified to assess the condition of the shared property. Next, multiple sensors were used, calibrated, and integrated into an IoT-enabled sensing device where the collected data was combined using intelligent sensor fusion technologies in a real-time manner. Third, a survey was developed and distributed to examine the significance of the 10 indicators, and an innovative reliability index was created accordingly to reflect the overall condition of the shared property. Fourth, a dynamic pricing model was developed to reward condition-conscious property users and penalize condition-unconscious ones. To ensure applicability and robustness of the proposed device and model, a pilot project was implemented in a smart long-term rental property in Newark, NJ, United States. Ultimately, this research provided insights on how to improve the operational efficiency of shared economy systems by offering (1) the providers of shared properties visibility over the condition of their properties through real-time assessment of the user reliability, and (2) the users of shared properties assured safety and monetary incentives to maintain the shared environment in a good condition. [ABSTRACT FROM AUTHOR]

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

A smart city has a complex hierarchical communication system with various components. It must meet the requirements of fast connection, reliability, and security without data compromise. Internet of Things technology is widely used to provide connectivity and control solutions for smart sensors and other devices using heterogeneous networking technologies. In this paper, we propose a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) with increasing speed, reliability, and sufficient security. Many routing protocols have been proposed for WSNs and MANETs. We combine the Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide an efficient mechanism for a Distributed dynamic heterogeneous network Transmission (DT) with new security and reliability routing protocol (DT-RRNS). We analyze the concept of data transmission based on RRNS that divides data into smaller encoded shares and transmits them in parallel, protecting them from attacks on routes by adaptive multipath secured transmission and providing self-correcting properties that improve the reliability and fault tolerance of the entire system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network's lifetime. [ABSTRACT FROM AUTHOR]

Around 20% of the population is disabled. Many people have mobility problems, including the elderly and people with young children. It is crucial to adapt cities to the needs of these people and, at the same time, to the needs of all residents. This is the subject of universal design, which should consider inhabitants' needs and habits. This information can be collected by Internet of Things (IoT) devices that observe and listen to residents. Residents do not accept constant surveillance, so the public may not accept data collection by IoT sensors. This study aimed to identify and evaluate factors influencing the acceptance of data collection by IoT devices for universal design. For this purpose, an online survey was prepared by the Warsaw University of Technology. The following statistical methods were used to analyze the data: descriptive statistics, exploratory factor analysis, confirmatory factor analysis, reliability analysis and structural equation modeling. This paper identifies key factors influencing the acceptance of IoT devices for universal design. The statistically significant factors are the perceived usefulness of data collection, trust in city authorities, the perceived security of data collected by IoT devices and empathy for people with disabilities. The original achievement of this study is its indication that empathy for the disabled moderates and increases the positive relationship between the perceived usefulness of IoT devices and their acceptance. It was also found that trust in city authorities mediates the relationship between the perceived usability and acceptance of IoT devices. City authorities can use the results of this analysis in the implementation of IoT devices in smart cities. [ABSTRACT FROM AUTHOR]

The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems. [ABSTRACT FROM AUTHOR]

Recent advances in various technologies have introduced effective new systems that can be successfully deployed in various future rail operations and applications, including smart city security functions. Rail accidents are a major problem in the transportation industry in many countries around the world. There is an urgent need to install protective elements to prevent accidents. This research describes the design and implementation of the rail safety system for smart cities using real-time mobile communication. The proposed prototype system has two basic functions to operate, namely accident detection and accident prevention. The proposed system consists of several sensors, LTE module, micro-controller, motorized gate and various displays for traffic control. The detection of the train is tracked via round trip time of the ultrasonic sensor and a micro-controller is used together with a GPS module and an LTE module to detect accidents. Hence there is two-way communication between the train and the control room. The proposed system is implemented by controlling the automated doors according to the reading of the sensors. When the sensors detect the movement of the rail, they can indicate this in real time through a buzzer and the message, which will eventually close the doors installed at the level crossing. In addition, the coordinates of the rail are transmitted via the GPS module, the GPS module continuously monitors the location, speed and time of the car. Therefore, the stated goal is achieved through real-time two-way communication between the control room and the rail via LTE and GPS modules. In this research, a prototype is designed and tested in real time for various scenarios to demonstrate the effectiveness of the proposed system. In the end, this research provides the simulation results to substantiate our claim to the reliability and importance of the proposed system for the implementation in the smart cities. [ABSTRACT FROM AUTHOR]

We know that in today's advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance. [ABSTRACT FROM AUTHOR]

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space. [ABSTRACT FROM AUTHOR]

Summary: Especially in urban contexts, the detection of damages in road infrastructures is crucial for their management and efficiency. This entails complex measurement chains. Several solutions were already developed and applied (e.g., high speed monitoring systems or sensor‐based systems), but the emerging smart city and smart road paradigms call for innovations in these fields. A possible solution could be the development of smart, non‐intrusive, and sustainable sensing systems able to perform continuous monitoring, allowing automatic and timely generation of alarms and proper maintenance strategies from Road Management Systems (RMSs). Consequently, this study aims to demonstrate the feasibility of integrating miniaturized sensing systems with high‐speed monitoring systems, to obtain innovative RMSs. In this regard, a specific web‐based platform, the core of a Smart RMS, properly defined to acquire, correlate, and exploit data from several sources was developed. To this end, high‐performance monitoring systems, an innovative sensing system (i.e., based on miniaturized devices and on feature‐ and signature‐based methods), and Micro‐electromechanical systems (i.e., smartphones accelerometers) were used. A new set of indicators has been set up and partly validated in the pursuit of setting out a new paradigm where Pavement Management Systems are supposed to become urban‐oriented and less expensive. Results show that combining traditional and innovative solutions allows providing a comprehensive overview of both the structural condition and the performance of road infrastructures. This information could be used to exponentially improve the efficiency of current RMSs in forecasting the occurrence of damages and in scheduling more effective and sustainable management interventions. [ABSTRACT FROM AUTHOR]

In the current period, there has been a prominent and gradual upswing in the application of Internet of Energy (IoE) sensors in smart cities. These sensors play a vital role across diverse aspects of the energy sector, ranging from producing energy to haggling with the complexities of the smart grid. The IoE sensors use fiber optics technology that increases the speed and bandwidth of data transfer in smart grid applications. Incorporating IoE sensors, including fiber optics, is paramount to rehabilitating extensive IoE sensor data into practical information to distribute energy based on prices, availability, and demand for smart homes and electric vehicles. However, from energy generation to consumption, many nodes are incorporated. Therefore, security is a crucial challenge to processing accurate IoE sensor data for energy generation and consumption in smart cities. This paper presents blockchain-enabled, energy-efficient IoE smart grid architecture for smart homes and electric vehicle applications. The proposed architecture suggests blockchain-energy-efficient IoE sensors data scheduling (BEDS) algorithm schemes that consist of blockchain, smart grid, and vehicle and smart home schemes. The paper considers grid data based on sensors for different applications. The proposed system integrates fiber optics, collecting and offloading sensors to the grid for execution. This study aims to process IoE sensor data based on blockchain with a minimum processing time of 29% and less power consumption of 41%. Simulation results show that BEDS has less processing time and energy consumption than existing proof-of-work and proof-of-stake blockchain methods in smart grid networks. • A smart-grid architecture for energy production in smart cities. • Blockchain energy data scheduling (BEDS) algorithm framework. • An energy-efficient blockchain-enabled IoE smart grid architecture. [ABSTRACT FROM AUTHOR]

• The definition of smart cities involves urban infrastructures that utilize intelligent sensors, IoT devices, and data integration. • The article explores different fusion methods for smart cities. • Benefits and challenges include managing large volumes of data and meeting real-time processing requirements. • It covers both technical AI aspects and practical applications in urban contexts. • Future research directions, addressing key issues and promising exploration areas in information fusion for smart cities. Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in creating interconnected and intelligent urban spaces. In this literature review, we explore the different methods of information fusion used in smart cities, along with their advantages and challenges. However, there are notable challenges in managing diverse data sources, handling large data volumes, and meeting the near-real-time demands of various smart city applications. The review aims to examine smart city applications in detail, incorporating quality evaluation and information fusion techniques and identifying critical issues while outlining promising research directions. In order to accomplish our goal, we conducted a comprehensive search of literature and applied selective criteria. We identified 59 recent studies addressing machine learning (ML) and deep learning (DL) techniques in smart city applications. These studies were obtained from various databases such as ScienceDirect (SD), Scopus, Web of Science (WoS), and IEEE Xplore. The main objective of this study is to provide more detailed insights into smart cities by supplementing existing research. The word cloud visualisation of machine learning/deep learning and information fusion in smart cities papers shows a diverse landscape, covering both technical aspects of artificial intelligence and practical applications in urban settings. Apart from technical exploration, the study also delves into the ethical and privacy implications arising in smart cities. Moreover, it thoroughly examines the challenges that must be addressed to realise this urban revolution's potential fully. [ABSTRACT FROM AUTHOR]

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) provides an infrastructure to constructing smart cities. Through the installed IoT sensors in the city, a large amount of information of the city is detected and collected in a cloud center, which provides residents to have immediate and convenient services. To achieve the goal of smart cities, the fundamental challenge has been to minimize the energy consumption of the IoT sensor network, which is involved with the deployment and sleep scheduling of IoT sensors. Most of the previous related works proposed two-stage approaches to address deployment and sleep scheduling problems of IoT sensors separately. However, the deployment at the first stage influences the optimality of the sleep scheduling at the second stage significantly. Consequently, this work jointly considers deployment and sleep scheduling of IoT sensors in a smart city. To improve the performance of sleep scheduling of IoT sensors, the deployment at the first stage concurrently considers a part of the optimality of later sleep scheduling, and it is optimized by the proposed improved geometric selective harmony search algorithm that incorporates crossover and dynamic schemes. The crossover scheme can effectively solve the problem of different decision variables in the solution representation; and the dynamic scheme can increase stability and diversity of searching for solutions. The performance of the proposed algorithm is evaluated by the simulation under various combinations of covering requirements, the number of sensors, and practical parameter settings. [ABSTRACT FROM AUTHOR]

With more than 84% of the population residing in cities, the Kingdom of Saudi Arabia is among the highly urbanized countries in the world. New strategic policies support the development of smart cities across the kingdom. Though non-availability of data is a major hurdle in the way of planning smart cities. We used a mixed method approach for this study including a qualitative review of various urban planning applications in Jeddah and worldwide in combination with a pilot survey to understand what type and mode of data reporting people prefer. Based on the initial findings a data crowdsourcing application—'SMART JEDDH' was developed and tested. Around 177 crowdsourced reports detecting 15 hotspots of car parking dearth, while 47 PM2.5 measurements at King Abdulaziz University campus was recorded. Results were visualized by heat maps in an open-source GIS environment. Furthermore, we model developed ordinal logistic regression (OLR) models with the 1581 responses on 5-point Likert scale to answer 3 key questions including importance of smartphone-based participation in resolving location specific urban challenges, importance of public participation through smartphones in the improvement of the quality of smart city plans, and the importance of the role of the authorities in mobilising people for smartphone-based participation. OLR results revealed that there is a significant difference of public opinions based on their nationality, gender, education, profession, and income levels in the city of Jeddah. Finally, we recommend that crowdsourced smartphone applications should be promoted for data collection because of inherent benefits through increased public participation. Abundant data though public participation might be helpful in realizing success of smart cities under Vision 2030 of Saudi Arabia of which maximizing 'quality of life' is a key target. [ABSTRACT FROM AUTHOR]

Existing surveys look at parking solutions from the perspective of sensors, communication protocols, and the hardware-software interface. While this is a worthwhile approach, it suffers from three obvious shortcomings, namely that present-day sensors are likely to become obsolete in a few years, communication protocols get discontinued, and present-day software will almost certainly not run on tomorrow’s platforms. Consequently, these approaches are not promising for the Smart Cities of the near future. Unlike previous surveys, we look at parking in Smart Cities through the lens of market-based allocation of goods and services. In competitive markets prices act as signals used to allocate goods to those who value them most. In the case of parking spots, some drivers are willing to pay higher prices for the use of those parking spots that offer them the highest utility. What makes our survey unique is that we are looking at the recent literature with an eye for market-oriented solutions including pricing as an instrument for shaping traffic and for incentivizing socially-desirable driver behavior. We believe that one of the important contributions of any survey paper, over and above being a compendium of known art, is to suggest new lines of research. With this in mind, we have peppered the manuscript with slightly unorthodox perspectives. These perspectives are intended to be thought-provoking and to open new avenues for possible investigations. [ABSTRACT FROM AUTHOR]

With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of patient's health. To meet the constraint of medical secrecy and confidentiality of information, we will use the Blockchain as a secure system. Our system will use the data collected by several smart sensors such as blood pressure, SPO2 concentration, and EEG signals. These encrypted data will be collected by an embedded Raspberry PI 4 platform (working as a smart data relay) before being processed (on a backend server) and then saved in an embedded Blockchain node. The preliminary results show the effectiveness of the proposed platform as a candidate of a low-cost example of secured Electronic Health Record (EHR). [ABSTRACT FROM AUTHOR]

Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested. [ABSTRACT FROM AUTHOR]

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated. [ABSTRACT FROM AUTHOR]

The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas' comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller's flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN's flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs. [ABSTRACT FROM AUTHOR]

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, non‐linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non‐linearity effect among distance and received signal strength (RSS) of target nodes is minimized by modelling edge weights using knowledge base in fuzzy logic system. Further, optimum membership functions bases' of RSS and edge weights using application of butterfly optimization algorithm are evolved to minimize the node position error. Here, a single anchor node is used to estimate target nodes 3D coordinates in anisotropic environment using range free localization methods. The anchor node has been deployed at top layer and over beneath layers target nodes are distributed equally. In This paper, simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range‐free methods. Proposed technique is useful for mapping of several instances like, fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities and so forth. [ABSTRACT FROM AUTHOR]

From smart vehicles to smart city sensors, the millions of new devices connected over the next few years will generate huge amounts of highly personal data that needs to be processed in real time. Transmitting this data to the cloud, with its centralized data centers and high latency, is both economically and technically unviable. As a result, the industry is moving towards processing data closer to the source, a major shift from the cloud to the so-called edge. This new architecture is understood as a necessary augmentation to the cloud. Indeed, in many ways, the edge is the polar opposite of the cloud: ad hoc networks, composed of resource-poor devices, that function at the hyperlocal level of the home, office, or neighborhood. However, this article argues that such technical supplementation is also about control, filling a critical void in contemporary formations of power. Coupled together, this cloud-edge formation is both centralized and decentralized, resource intense yet geographically dispersed. Drawing on Foucauldian theory, such power augments heavy, situated force with a more flexible, economic architecture – conforming to a trajectory of 'intensification' while also complicating it. While each mode of power has certain strengths and weaknesses, combining their operations forms a twinned power with new capacities for subjectivation and governance. [ABSTRACT FROM AUTHOR]

Air pollution caused by fine particulate matter 2.5 (PM 2.5) is a leading health crisis for people around the world including Thailand. This paper proposes a low-cost and real- time PM 2.5 monitoring system using wireless sensor network and the Internet of Things ( IoT). The system is designed to monitor PM 2.5 via Blynk application on smart phone and via LCD monitor by showing the detection results in the Air Quality Index (AQI) which is easy to understand. Furthermore, our system is calibrated with the standard instrument to ensure accuracy and reliability. The results show that our low- cost system is able to provide a reliable real- time monitoring of the PM 2.5 condition. [ABSTRACT FROM AUTHOR]

Adolescents' emotional changes will have a huge impact on themselves; perhaps, they do not understand themselves. However, according to research, many behaviors of adolescents are often accompanied by emotional changes, and the occurrence of these changes will also bring about their unconsciousness. This article first introduces the research background, significance, and development status of smart home sensors and young people's emotions at home and abroad. This article then gives a detailed introduction to the Python language, intelligent sensor networks, and real-time analysis of youth emotions. In the introduction, it mainly explains the design of the intelligent sensor network system and introduces the system architecture and software and hardware design of the wireless sensor network in detail. In the hardware part, it mainly gives a brief overview of information collection, data transmission, and data processing. In the software part, the embedded software design of three types of network nodes and the control center software design based on Python are given. Finally, the neural network algorithm is used to realize the real-time analysis of young people's emotions, and the recognition rate of multiple algorithms and the data situation of multiple emotional factors are tested at the same time. The results show that the highest recognition rate of 58.4% can be achieved on the validation set of the HAPPEI database after preprocessing, which is higher than the recognition result obtained by directly training the network using the training set of the HAPPEI database. [ABSTRACT FROM AUTHOR]

As a typical example of an Intelligent Transport System (ITS) in a smart city, the bicycle sharing is developing so fast and changing the citizens’ travel habits to a large extent in China. However, its disorder development brings a heavy burden for city, and it is considered as a junk of city in many citizens’ opinions. In our previous work, we proposed the concept of Internet of Shared Bicycle (IoSB) to solve some problems in technique. In this paper, we devote to how to enable bicycle sharing playing a more important role in the construction of smart city, instead of being treated only as a type of traffic mode. We summarize some of the current research status of bicycle sharing in smart cities and put forward some of our own views. Then the Bicycle Sharing System (BSS) is be further studied and explored the possibility of being deployed as a terminal in the city in terms of technology. When a serious disaster comes, it serves as an auxiliary communication system for the city and becomes a measure of the city’s emergency network. In particular, we describe a special role of bicycle sharing during public health events. In addition, the feasibility in some interesting cases with great potential in the future is also discussed. [ABSTRACT FROM AUTHOR]

Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities. [ABSTRACT FROM AUTHOR]

A network of sensors and surveillance devices creates a critical infrastructure in smart cities, enabling civic authorities to render faster and more efficient services. Notably, many deployed sensor applications form linear topologies, such as road monitoring sensors (e.g., intelligent lighting, road traffic, pedestrians, etc.), railway track sensors, and pipeline sensors (e.g., sensing leaks, pressure, etc.). These linear sensors convey data to a central control station by daisy-chaining through neighbor sensors. However, it causes critical energy inefficiency and imbalance issues and creates data transmission delay, which provokes complex application and service management hassles. In this paper, we propose an Energy-Efficient Linear Sensor Architecture (ELSA) for building sensor network infrastructure of Smart Cities. We design and develop an energy-efficient Wake-up Receiving (WuRx) method on sensor nodes by using a dual-channel, an infrared (IR) based WuRx channel over a WiFi data channel to enhance the duty cycle efficiency. In addition, we build a pipelined relay algorithm to avoid any network delay performance degradation. Furthermore, we design a centrality-based algorithm to identify linear sensor networks. The experimental results show that IR WuRx ELSA performed nearly ten times better energy efficiency per node and achieved a constant network delay regardless of the number of relaying nodes in a network. [ABSTRACT FROM AUTHOR]

The study is aimed at solving the security problems caused by malicious network attacks in human resource management in the development of smart cities. First, the Stackelberg game theory model is used to describe the interaction between intelligent sensors and intelligent jammers, and the security of the information physics system is effectively evaluated. Second, a denoise autoencoder machine model that can be used in human resource management with demographic information is proposed to ensure the security of intelligent sensors. Finally, its performance is simulated and analyzed. The results show that the more packets successfully arrive at the estimator, the more favorable the estimation effect of the estimator is. The designed defense strategy is very effective in protecting the security of intelligent sensors. When the number of nearest neighbor K increases, MAE of four datasets first decreases and then tends to be stable. With the increase of K , MAE of the algorithm proposed decreases from 0.8232 to 0.8095, 0.8086 to 0.7897, and 0.8563 to 0.8351, respectively. It decreases from 1.0169 to 1.0091 and then keeps stable. The recommendation algorithm proposed shows high accuracy in rating prediction and can quickly and effectively extract the hidden features of users and adjust the sparsity of data. Therefore, it can be applied to human resource management. This study provides a new idea for the research on the security of intelligent sensors and human resource management in the development of smart cities. [ABSTRACT FROM AUTHOR]

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Nowadays, with the development of various kinds of sensors in smartphones or wearable devices, human activity recognition (HAR) has been widely researched and has numerous applications in healthcare, smart city, etc. Many techniques based on hand-crafted feature engineering or deep neural network have been proposed for sensor based HAR. However, these existing methods usually recognize activities offline, which means the whole data should be collected before training, occupying large-capacity storage space. Moreover, once the offline model training finished, the trained model can’t recognize new activities unless retraining from the start, thus with a high cost of time and space. In this paper, we propose a multi-modality incremental learning model, called HarMI, with continuous learning ability. The proposed HarMI model can start training quickly with little storage space and easily learn new activities without storing previous training data. In detail, we first adopt attention mechanism to align heterogeneous sensor data with different frequencies. In addition, to overcome catastrophic forgetting in incremental learning, HarMI utilizes the elastic weight consolidation and canonical correlation analysis from a multi-modality perspective. Extensive experiments based on two public datasets demonstrate that HarMI can achieve a superior performance compared with several state-of-the-arts. [ABSTRACT FROM AUTHOR]

In order to realize the continuous optimization of the enterprise financial accounting information system of the smart city, from the perspective of intelligent sensor technology, firstly, this paper understands the use feelings of the enterprise financial accounting-related personnel about the information system in the smart city based on the relevant theories of intelligent sensor, smart city, and accounting information. Secondly, the Internet of things technology and intelligent sensing technology are applied to the optimization of enterprise financial accounting information system. Finally, the corresponding opinions and suggestions are put forward from the perspectives of government, employees, and enterprises. The results show that about 40%∼60% of enterprise financial accountants believe that data integration and sharing, data governance, and data operation management systems need to be continuously optimized and upgraded. Applying the Internet of things technology and intelligent sensing technology to the optimization of enterprise financial accounting information system, it is found that it can not only improve the accuracy of financial data processing, but also receive the required results efficiently and in real time. It is more important to apply intelligent sensing technology to enterprise financial information system. After comparison, it is found that the net profit of the enterprise increased from 112.69 million yuan to 3329.33 million yuan. The owner's equity of the enterprise increased from 2045.68 million yuan to 11177.34 million yuan. The cash flow of the enterprise increased from 1056.91 million yuan to 6892.56 million yuan. Therefore, this paper studies how to optimize the enterprise financial accounting information system based on the intelligent sensor of the Internet of things in the smart city, which has great reference significance for the management of enterprise financial operation. [ABSTRACT FROM AUTHOR]

In the emerging era of transportation, the Internet of Vehicles envisions a large ecosystem where vehicles, traffic infrastructure, smart city sensors, and citizens will interoperate, providing safe and fast transportation services. How close are we to realizing this concept? [ABSTRACT FROM AUTHOR]