A recent review of thermography studies in rheumatoid arthritis shows limited data about disease activity and mostly focuses on differences between the thermography of rheumatoid arthritis patients and typical subjects. A retrospective study compared patients with high disease activity (n = 50), moderate disease activity (n = 16), and healthy participants (n = 42), taking into account demographic, clinical, laboratory, and thermography parameters. We applied an infrared thermography sensor and a fingers examination protocol. Outcomes included the mean temperature of five fingers of a hand: In static, post-cooling, post-rewarming, the total change in mean temperature of fingers due to cold provocation, the total change in mean temperature of fingers due to rewarming, the area under the cooling curve, the area under the heating curve, the difference between the area under the rewarming and the cooling curve, and temperature intensity distribution maps. For patients with high disease activity, a lower area under the heating curve and a lower difference between the area under the rewarming curve and the cooling curve were observed, as well as a smaller total change in mean temperature due to rewarming, compared to patients with moderate disease activity (p < 0.05). Our study findings could be helpful in patients with an equivocal clinical examination.

Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

Leveraging big data in smart cities: A systematic review

The growing concerns of families about the safety and security of their children prompted a major interest in developing robust systems that offer efficient tracking and monitoring of children commuting between homes and schools. With the emergence of the Internet of Things (IoT) technology, in addition to Radio Frequency Identification (RFID), developing such systems became feasible and cost-effective. In this paper, we present the design and implementation of a comprehensive low-cost system based on IoT that allows schools, parents, and authorities to track the movement of children while in school buses or being transported in private vehicles in real time. The system is based on off-the-shelf passive RFID readers that are installed within buses, next to bus stations, and pick-up points at school entrances. We have implemented the system as an application connected to a MySQL database deployed over the Heroku’s versatile cloud platform. The different stakeholders (parents and school administrators) connect to the system through the Internet using a secure Java GUI. The parents can track their children along the bus route in real time. The system is built to integrate children monitoring within the buses, the tracking of the buses, and the tracking of private vehicles transporting children to/from school. The developed system provides additional security features such as access to safety records of bus/vehicle drivers and infringements. A variety of reports for different activities of buses and vehicles can be generated in real time at the school level. The system was tested successfully over the King Fahd University of Petroleum and Minerals large campus which includes several schools (kindergarten, primary, and secondary).

An IoT-Based School Bus and Vehicle Tracking System Using RFID Technology and Mobile Data Networks

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of smart city. Application domains include the essential aspect of the smart cities including the energy, healthcare, transportation, security, and pollution. The methodology presents the state-of-the-art, taxonomy, evaluation and model performance. The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity. On the other hand, the deep learning (DL) techniques had higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference is negligible, it can be concluded that using either of them is appropriate. The study&#x2019;s findings identify the pros and cons of the methods in each application for future researchers, practitioners, and policy-makers for the right problem within the context of smart cities. 

When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review

Intrusion detection is a very sensitive and major concern in wireless sensor networks. In border areas, sensors are installed to discover the presence of enemies or any trace passing in a prohibited area or any vicious moving object in the region of interest. In order to gain access to a region of interest, an intruder can take various paths, i.e., it can move along a straight line, follow a zigzag path or a curved path and move at a particular angle to cross the region without being detected and to improve its attacking ability. This paper formulates and analyzes 
$$\kappa$$

-barrier coverage probability which acts as the intrusion detection probability for an intruder when the intruder follows different paths at different path angles with respect to the shortest path to cross the region of interest in a mobile sensor network. Furthermore, the effect of different network variables such as node density, sensing range, intrusion path angle and the ratio of sensor to intruder velocity on intrusion detection probability are also investigated. It is believed that the proposed model renders an effective tool to incorporate the intruder’s movement pattern in the design of an advanced finite wireless sensor network.

Intrusion Detection in Mobile Sensor Networks: A Case Study for Different Intrusion Paths

Background Due to the problem of aging societies, there is a need for smart buildings to monitor and support people with various disabilities, including rheumatoid arthritis. Objective The aim of this paper is to elaborate on novel techniques for wireless motion capture systems for the monitoring and rehabilitation of disabled people for application in smart buildings. Methods The proposed techniques are based on cross-verification of distance measurements between markers and transponders in an environment with highly variable parameters. To their verification, algorithms that enable comprehensive investigation of a system with different numbers of transponders and varying ambient parameters (temperature and noise) were developed. In the estimation of the real positions of markers, various linear and nonlinear filters were used. Several thousand tests were carried out for various system parameters and different marker locations. Results The results show that localization error may be reduced by as much as 90%. It was observed that repetition of measurements reduces localization error by as much as one order of magnitude. Conclusions The proposed system, based on wireless techniques, offers a high commercial potential. However, it requires extensive cooperation between teams, including hardware and software design, system modelling, and architectural design.

Novel techniques for a wireless motion capture system for the monitoring and rehabilitation of disabled persons for application in smart buildings

This paper presents a contribution in the development of a wireless sensor network, which can be used for building, in real time, dense air pollution maps, for compact urban areas. Such system may be useful for cyclists and pedestrians moving through the city. Based on such data, they can select the route in such a way, as to avoid the most polluted areas. An important step here may be development of miniaturized and cheap intelligent sensors, capable not only of data recording and transmitting, but also of some on‐site data processing and prediction. Such sensors require a development of small and power efficient circuit, including data processing unit integrated with an artificial neural network (ANN) in a single chip. We present a prototype chip that contains main components of such sensors, which include a programmable 10‐bit analog‐to‐digital converter, a programmable clock generator, and selected blocks of the ANN. The chip is a reconfigurable device, with many testing abilities. For this reason, one of the main challenges was a fast and efficient programming and testing tool. Such tool has been developed by us and is described in this work in detail along with selected measurement results. The presented work is an extended version of our conference paper (“Novel solutions for smart cities—creating air pollution maps based on intelligent sensors”).

New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors

In this paper, we focus on practical issues related to the implementation of real time locating systems (RTLS) in the context of vehicle-to-infrastructure (V2I) communication. These emerging concepts are an important part of novel areas such as Smart Cities and Intelligent Transportation Systems (ITS). Some problems encountered during the development of these systems result from the limitations of technologies that are supposed to become the basis for these new applications. In our opinion, there is additionally a direct link between the development of the new technologies, the architectural planning, as well as regulations. An appropriate planning of surrounding of the road will facilitate the realization of V2I systems. On the other hand, technology limitations will provide guidance how to design the road environment. In this work, we focus, in particular, on the assessment of the localization precision of RTLS used in ITS. The precision to some extent will depend on the variation of physical parameters of the used circuits, e.g. PVT (process, voltage, temperature) variation → delay in answer → localization error → safety issues.

Real-time locating systems for smart city and intelligent transportation applications

This paper proposes novel solutions for the application of air pollution monitoring systems in so called ‘smart cities’. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%–80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.

Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

Passive thermographic measurement is susceptible to multiple factors. However, there are not many evidences of how these factors impact the measurement of human hand skin temperature evolution after cold stress inducement, especially in patients with inflammatory lesions. The study aimed at the analysis of the variability factors in: heat source, environment conditions including material designed for placing hand, blood circulation, blood pressure and their effect on the hand infrared image of RA patients. Thermal image sequences were collected for 10 RA patients. The procedure consisted of hand immersion in ice-water for 5 seconds and 3-minute recording of temperature evolution for left and right hand, separately. Impact of the following factors was included in the analysis: heat source, material designed for placing hand, blood circulation. The plane, which is heated by the heat of the hands, emits radiation with similar intensity like the hand itself. During the process of hand warming, we observe gradual changes in the image of superficial veins. Blood vessels should be treated as an element of hand texture. The glow around the hand is a serious difficulty in the analysis. Development of dedicated algorithms is necessary.

Marzena Banach

Papers

Novel techniques for a wireless motion capture system for the monitoring and rehabilitation of disabled persons for application in smart buildings

New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors

Real-time locating systems for smart city and intelligent transportation applications

Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

Selected Factors Affecting Active Thermographic Measurement of Human Response to Cold Stress in RA Patient