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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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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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]

The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments. [ABSTRACT FROM AUTHOR]

According to standards, the lighting system is one of the key elements to provide safety on city roads, defined by quality parameters. LED technology and movement detection sensor interaction bring about new regulation techniques, creating an energy-efficient smart LED lighting system concept. This paper reveals extensive comparative data analysis of Dialux simulation results before the project implementation phase and in-situ quality parameter measurements for various street profiles and LED luminary power types. After the project implementation phase, more than 1000 measurement points are reached. Further, energy efficiency increase issues in smart lighting systems are described in terms of LED luminary dimming profile analysis and future dynamic control application modes. The first findings clearly show that in most cases light output in simulation results is lower than in real situations; therefore, LED luminary power can be decreased, allowing for higher energy savings in first luminary maintenance years, keeping the same defined ME class or safety level. Let us suppose that the traffic intensity data are obtained from smart system sensors. In that case, the ME class can be dynamically selected during different night times, thus increasing safety and providing extra energy savings using the same system elements, as well as leading to better ROI values. [ABSTRACT FROM AUTHOR]

In this paper, we focus on building and researching the self-power sensor system based on the tiny energy harvesting technology which can be used in the scenario of ubiquitous power Internet of Things (UPIoT) and prove the possibility and efficiency for the life extension of transmission line intelligent sensing system. Large scale of sensor in the smart grid technology application, especially in high-voltage transmission lines, is not convenient; it is worth mentioning that most of all kinds of sensor node equipment using battery power make the life of the equipment or components limited by greatly. Therefore, low power consumption, long life, no battery dependence, free maintenance, and other requirements are increasingly important to solve the difficulties of deployment and maintenance. At the same time, the problem of the battery case electricity use with low efficiency and short life of sensor node has become the bottleneck of further widely deployed wireless sensor node equipment. The power collection technology based on environmental energy can effectively handle with the problems of energy collection efficiency and management that need to be urgently solved in new application scenarios such as zero-standby power consumption devices, remote active tags, battery-free telemetry and remote control, and ultralong life sensing system. By studying various kinds of environmental energy collection technologies and utilizing the conversion and management technologies of available weak environmental energy, such as solar energy and magnetic field energy, into electric energy, this paper establishes an energy conversion test system and configuration model and verifies the feasibility of the assumption of maintenance-free for the intelligent sensing system of transmission lines. [ABSTRACT FROM AUTHOR]

Experts confirm that 85% of the world's population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities. [ABSTRACT FROM AUTHOR]

Based on an in-depth survey of the literature, the purpose of the paper is to explore environmentally sustainable urban development and Internet of Things connected sensors in cognitive smart cities. Using and replicating data from Capgemini, EIU, ESI ThoughtLab, JLL, KPMG, ICMA, SCC, and UKTI, I performed analyses and made estimates regarding how networked and integrated sustainable urban technologies optimize interconnected sensor networks through digital urban governance in smart and environmentally sustainable cities. Data-driven planning technologies can upgrade the standard of living by gathering massive volumes of data from Internet of Things sensing infrastructures. Data-driven Internet of Things systems integrate computationally networked urbanism and sustainable governance networks across Internet of Things sensing infrastructures by harnessing machine learning-based analytics. Smart and environmentally sustainable cities leverage datadriven planning technologies and machine learning-based analytics. Networked and integrated sustainable urban technologies develop on data-driven Internet of Things systems and machine learning-based analytics. Smart sustainable city governance and management can be enhanced by harnessing Internet of Things sensors, urban big data analytics, data-driven Internet of Things systems, and sensor-based big data applications. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

Smart city can improve the efficiency of managing assets and resources, optimize urban services and improve the quality of citizens' life. Wireless sensor networks (WSNs) can solve many problems in smart city, such as smart transportation, smart healthcare and smart energy. However, security and privacy are the biggest challenges for WSN. Recently, Banerjee et al. proposed a security-enhanced authentication and key agreement scheme for WSN, but their scheme cannot resist offline password guessing attack, impersonation attack, and does not achieve session key secrecy, identity unlinkability, and perfect forward secrecy. In order to fix these flaws, a secure and privacy-preserving authentication protocol for WSN in smart city is proposed. We prove the security of the proposed protocol by using applied pi calculus-based formal verification tool ProVerif and show that it has high computational efficiency by comparison with some related schemes. [ABSTRACT FROM AUTHOR]

In this work, our objective is to design, develop, and evaluate the effectiveness of a stress detection model for older adults using a system of wrist-worn sensors. Our system uses four signals, EDA, BVP, IBI, and ST from EDA, PPG, and ST sensors, embedded in a smart wristband, to classify between stressed and not-stressed state. The stress reference is obtained from salivary cortisol measurement, which is a well established clinical biomarker for measuring physiological stress. This work is the result of year-long data collection and analysis of 40 older adults (28 females and 12 males) and age 73.625 ± 5.39. EDA, BVP, IBI, and ST signals were collected during TSST (Trier Social Stress Test), which is a well known experimental protocol to reliably induce stress in humans in a social setting. 47 features were extracted from EDA, BVP, IBI, and ST signals, out of which 27 features were selected using a supervised feature selection method. Results and analysis show that combining the features from all the four signal streams increases the model’s ability to accurately distinguish between the stressed and not-stressed states. The proposed model achieved a macro-average F1-score of 0.92 and an accuracy of 94% in distinguishing between the two states when features from all the four signals were used. Further, we prototype the proposed stress detection model in a consumer end device with voice capabilities, so that users can receive feedback on their vitals and stress levels by querying on voice-enabled consumer devices such as smartphones and smart speakers. [ABSTRACT FROM AUTHOR]

Smart city is a city that increases the interactivity of its components and put emphasis on their functionality. Internet of Things technology (IoT) is an innovative solution in environmental protection. Usually, information on air quality is very scattered. This paper describes the test stages of pre-implementation works, focusing on the presentation of the technical design of the measurement nodes and the assumptions of the IT project. The goal of the project Intelligent Wireless Sensor Network Infrastructure (IIBSC) is, among others, to create a dense network of air quality measurement nodes at city, district or even street level. The concept is based on Internet of Things (IoT) technology using a matrix construction tool connected to multiple identical measurement nodes located in the test area. The project developed a hardware platform supporting sensors and resistant to external factors, and an ISIMPIO information platform based on edge processing technology for processing data from air quality sensors. Due to the use of Internet of Things (IoT) technology, an edge server using edge processing was designed. Edge server provides a complete ecosystem for building edge applications that are fully optimized for seamless field work. In addition, it allows the implementation of integrated Python software, the MQ Telemetry Transport support protocol (MQTT), time-series database, firmware update over a wireless network, and built-in security system. Measuring the concentration of particulate matter and other substances in the air will be useful for specialists assessing their dynamics. The technology and test installation selected corresponds to the leading solutions in this field in Europe and, in the future, should also be extended to less urbanised areas. [ABSTRACT FROM AUTHOR]

Empirical evidence on sensor-based big data applications and environmentally sustainable urban development in Internet of Things-enabled smart cities has been scarcely documented in the literature. Using and replicating data from Capgemini, CBRE Research, Deloitte, EIU, ESI Thought Lab, and RICS, we performed analyses and made estimates regarding Internet of Things connected sensors in smart city governance. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

To reduce the uneven energy consumption for the data transmission and extend network life of intelligent community sensor network, an adaptive routing optimized algorithm for intelligent community sensor networks with cluster head election is proposed. In this algorithm, a three‐dimensional clustering method adapted to the structure of intelligent community sensor network is proposed. The three‐dimensional clustering method uses the cluster head election mechanism based on minimizing the total transmission loss to optimize the energy of the intelligent community sensor network. Second, an adaptive ant colony propagation method is proposed to solve the problem of intercluster data propagation after clustering. With the best path finding algorithm of ant colony algorithm, energy balance routing with lower energy loss and lower packet error rate is proposed. Finally, the simulation results show that the algorithm has better performance in reducing energy consumption and delay, improving transmission efficiency and node survival time. [ABSTRACT FROM AUTHOR]

IoT and Smart-City, broadly defined, encompasses current sensors, networking infrastructure, computing and storage elements as well as 5th generation cellular and fiber architectures, new services and cognitive networking paradigms for heterogeneous networks and data analytics. The vast amount of potential data from sensors and mobile devices, the increased traffic demand from end-users, and the ever-increasing number of end-users (up to 50 billion worldwide) will require smarter approaches to sensor, network and computing resources deployment, interconnection, usage and management. [ABSTRACT FROM AUTHOR]

Various kinds of smart sensors are being developed and released with the growth of sensor technology and Internet of Things (IoT) technology. IoT smart service can provide convenience in our daily lives with these smart sensors. However, the increasing number of mobile sensors and amount of data may increase latency. It may cause network congestion on a particular network. Therefore, we propose a Smart Edge Broker (SEB) to intelligently transmit data traffic generated by a smart city in this paper. SEB can prevent the traffic congestion from being transmitted or bypassed to a location where traffic is not necessary. In addition, SEB can prevent overload in a specific area between services through a location-based transfer. Plus, SEB is suitable to operate it as a fog computing model by placing it at the edge of a smart city network. We conducted a latency measurement experiment and load measurement experiment to evaluate the effectiveness of the proposed Smart Edge Broker. As a result, we found that the latency was reduced by 72%, and the CPU usage was reduced by 63% compared to when the Smart Edge Broker was not used. [ABSTRACT FROM AUTHOR]

V2X communication in the mmWave band is one way to achieve high data rates for applications like infotainment, cooperative perception, augmented reality assisted driving, and so on. MmWave communication relies on large antenna arrays, and configuring these arrays poses high training overhead. In this article, we motivate the use of infrastructure mounted sensors (which will be part of future smart cities) to aid establishing and maintaining mmWave vehicular communication links. We provide numerical and measurement results to demonstrate that information from these infrastructure sensors reduces the mmWave array configuration overhead. Finally, we outline future research directions to help materialize the use of infrastructure sensors for mmWave communication. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of scheduling the optimal paths of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize the travel distance and balance the energy consumption and the data gathering latency in wireless sensor networks for smart cities. To partition the network for the multiple mobile elements and compute the trajectories of the multiple mobile elements, we utilize the sensor's communication range and construct a multiple mobile elements scheduling problem. A heuristic mobile data gathering approach is proposed to solve this problem, which includes the following three steps. The sensor nodes are preliminarily partitioned into four levels, and then the clusterheads are further partitioned, and the traveling tour is scheduled for each cluster. After the first two steps, all the sensor nodes are partitioned reasonably for the multiple mobile elements. In the last step, the traveling tour is scheduled for each cluster, and the meeting point of each clusterhead is determined. We compare the proposed heuristic mobile data gathering with the existing approaches. The results indicate that the travel distance and the data gathering latency are reduced significantly, which further validates that the communication range is beneficial to minimize the travel distance. [ABSTRACT FROM AUTHOR]

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IoT technology is a fundamental infrastructure for information transmission and integration in smart city implementations to achieve effective fine-grained city management, efficient operations, and improved life quality of citizens. Smartcity IoT systems usually involve high volume and a variety of information circulation. The information life-cycle also involves many parties, stakeholders, and entities such as individuals, businesses, and government agencies with their own objectives which needed to be incentivized properly. As such, recent studies have modeled smart-city IoT systems as a market, where information is treated as a commodity by market participants. In this work, we first present a general information-centric system architecture to analyze smart-city IoT systems. We then discuss features of market-oriented approaches in IoT, including market incentive, IoT service pattern, information freshness, and social impacts. System design challenges and related works are also reviewed. Finally, we optimize information trading in smart-city IoT systems, considering direct and indirect network externalities in a social domain. [ABSTRACT FROM AUTHOR]

Featured Application: Data sensors for Smart Cities are an important component in the extraction of patterns—thus, they must be placed in strategic locations where they are able to provide information as accurate as possible. A significant and very extended approach for Smart Cities is the use of sensors and the analysis of the data generated for the interpretation of phenomena. The proper sensor location represents a key factor for suitable data collection, especially for big data. There are different methodologies to select the places to install sensors. Such methodologies range from a simple grid of the area to the use of complex statistical models to provide their optimal number and distribution, or even the use of a random function within a set of defined positions. We propose the use of the same data generated by the sensor to locate or relocate them in real-time, through what we denominate as a 'hot-zone', a perimeter with significant data related to the observed phenomenon. In this paper, we present a process with four phases to calculate the best georeferenced locations for sensors and their visualization on a map. The process was applied to the Guadalajara Metropolitan Zone in Mexico where, during the last twenty years, air quality has been monitored through sensors in ten different locations. As a result, two algorithms were developed. The first one classifies data inputs in order to generate a matrix with frequencies that works along with a matrix of territorial adjacencies. The second algorithm uses training data with machine learning techniques, both running in parallel modes, in order to diagnose the installation of new sensors within the detected hot-zones. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of providing cheap and on-demand electricity to low-power devices and sensors of smart cities and infrastructures. A new design for an electromagnetic road energy harvester is proposed, and its performance is evaluated through numerical simulations and experiments. The new method can generate sufficient electricity to run the low-power facilities of the smart cities in real-time and provide enough power for further applications using energy storage devices. Unlike the current road energy harvesters, the new harvester has a compact design, negligible mismatch risk between the components, and is more compatible with high-speed roadways. It can be used as road rumbles, transverse rumbles, or smooth speed bumps with a minimal vertical displacement of a maximum of 25 mm. It is shown that the harvester can illuminate LED light bulbs of 2 W at a displacement of 18 mm, which is compatible with the required depth of road rumbles. This power generation level satisfies sensors and IoT requirements and can also be stored for large-scale applications. • The crank-shaft road energy harvester's performance is simulated and successfully verified experimentally. • The optimum external resistor of the harvesting circuit is independent of the mechanical conditions, equal to 35.5 Ω. • A maximum power pulse of 4.36 W was obtained from the harvester, enough to light up 2 LED lightbulbs. • The harvester provides surplus power for smart city devices, serving as their on-demand power source. • The harvester has the potential of reducing 34 Kg of CO2 annually. [ABSTRACT FROM AUTHOR]

Robotic sensor systems play a vital role in the intelligent working of service robots, and service robots are important components of smart cities. Robotic sensors sense information from themselves and their environment, and fuse pieces of information for use in perception, decision, planning, and control. In this paper, we investigate the application of robotic sensor systems in 3D biped walking. By using sensory feedback control, offline gait planning and online gait modification schemes have been proposed. The gait planner plans the frontal and lateral reference trajectory of a biped robot. Moreover, the kinetic constraint, zero moment point (ZMP) stability criterion, and unilateral constraint are taken into account, simultaneously. The gait modification consists of the torso posture controller, the ZMP compensator, and the joint angle controller. It is utilized to reduce the robot joint error through posture control and ZMP compensation. Additionally, the online sensor parameter optimization based on reinforcement learning (RL) is proposed to improve the stable margin of walking. Finally, simulations are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents a method of obtaining short-termpositioning accuracy based on micro electro-mechanical system (MEMS) sensors and analysis of the results. A high-accuracy and fast-positioning algorithm must be included due to the high risk of accidents in cities in the future, especially when autonomous objects are taken into account. High-level positioning systems should consider a number of sub-systems such as global positioning system (GPS), CCTV - video analysis, a system based on analysis of signal strength of access points (AP), etc. Short-term positioning means that there are other locating systems with a sufficiently high degree of accuracy based on, e.g. a video camera, but the located object can disappear when it is hidden by other objects, e.g. people, things, shelves etc. In such a case, MEMS sensors can be employed as a positioning system. The paper examines typical movement profiles of a radio-controlled (RC) model and fundamental filtering methods in respect of position accuracy. The authors evaluate the complexity and delay of the filter and the accuracy of the positioning in respect of the current speed and phase of movement (positive acceleration, constant) of the object. It is necessary to know whether and how the length of the filter changes the position accuracy. It has been shown that the use of fundamental filters, which provide solutions in a short time, enables to locate objects with a small error in a limited time. [ABSTRACT FROM AUTHOR]