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Wearable human interaction devices are technologies with various applications for improving human comfort, convenience and security and for monitoring health conditions. Healthcare monitoring includes caring for the welfare of every person, which includes early diagnosis of diseases, real-time monitoring of the effects of treatment, therapy, and the general monitoring of the conditions of people’s health. As a result, wearable electronic devices are receiving greater attention because of their facile interaction with the human body, such as monitoring heart rate, wrist pulse, motion, blood pressure, intraocular pressure, and other health-related conditions. In this paper, various smart sensors and wireless systems are reviewed, the current state of research related to such systems is reported, and their detection mechanisms are compared. Our focus was limited to wearable and attachable sensors. Section 1 presents the various smart sensors. In Section 2, we describe multiplexed sensors that can monitor several physiological signals simultaneously. Section 3 provides a discussion about short-range wireless systems including bluetooth, near field communication (NFC), and resonance antenna systems for wearable electronic devices.

The rapid advancement of smart sensor technologies has transformed the landscape of precision agriculture, ushering in a new era of data‐driven and sustainable farming practices. This study presents a comprehensive review of recent innovations and future perspectives in the domain of smart sensors for precision agriculture. By harnessing the power of the Internet of Things (IoT) and artificial intelligence, these sensors have revolutionized how farmers collect, analyze, and utilize data to optimize crop yield, conserve resources, and enhance overall agricultural efficiency. The review delves into various types of smart sensors used in precision agriculture, including soil sensors for monitoring moisture, nutrient levels, and soil health; crop health sensors for detecting diseases, pests, and stress factors; weather and environmental sensors for real‐time climate monitoring; and automated irrigation systems for precise water management. Cutting‐edge technologies such as drone‐based sensors, hyperspectral imaging, and sensor fusion are explored, highlighting their potential to revolutionize data acquisition and decision‐making in agriculture. Furthermore, the manuscript addresses the incorporation of smart sensors with data analysis systems. This enables farmers to acquire practical observations and render knowledgeable choices regarding watering schedules, fertilizing regimens, and safeguarding approaches for crops. The role of data‐driven agriculture in mitigating environmental impact, optimizing resource utilization, and promoting sustainable practices is emphasized. Despite the significant strides made in smart sensor technology, challenges such as sensor calibration, data privacy, interoperability, and adoption barriers remain pertinent. To address these hurdles, the manuscript outlines potential research directions and emerging trends in sensor development, including miniaturization, energy efficiency, and wireless communication advancements. In conclusion, this manuscript provides a comprehensive assessment of the recent advancements and future prospects of smart sensors in precision agriculture. With the potential to revolutionize traditional farming practices and address global food security challenges, smart sensors offer an unprecedented opportunity for farmers to enhance productivity, conserve resources, and foster sustainable agricultural practices regardless of an ever‐changing climate and growing demand for food.

Highlights Recent developments of advanced electronic and optoelectronic gas sensors are introduced. Sensor array with artificial intelligence algorithms and smart gas sensors in “Internet of Things” paradigm are highlighted. Applications of smart gas sensors in environmental monitoring, medical and healthcare applications, food quality control, and public safety are described. Gas sensor is an indispensable part of modern society with wide applications in environmental monitoring, healthcare, food industry, public safety, etc. With the development of sensor technology, wireless communication, smart monitoring terminal, cloud storage/computing technology, and artificial intelligence, smart gas sensors represent the future of gas sensing due to their merits of real-time multifunctional monitoring, early warning function, and intelligent and automated feature. Various electronic and optoelectronic gas sensors have been developed for high-performance smart gas analysis. With the development of smart terminals and the maturity of integrated technology, flexible and wearable gas sensors play an increasing role in gas analysis. This review highlights recent advances of smart gas sensors in diverse applications. The structural components and fundamental principles of electronic and optoelectronic gas sensors are described, and flexible and wearable gas sensor devices are highlighted. Moreover, sensor array with artificial intelligence algorithms and smart gas sensors in “Internet of Things” paradigm are introduced. Finally, the challenges and perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

Presently, various real time applications has been developed using smart systems such as smart cities, smart homes, smart transportation, etc. The use of smart sensors in those systems leads to the generation of different kinds of multimedia data like images, videos, audios, and so on. To acquire multimedia data from smart sensor environments, Wireless Sensor Networks (WSN) has been employed, which is an integral part of smart system which helps to maintain connectivity and coverage. In WSN, the major challenging issue is to process the massive amount of multimedia data which leads to maximum energy utilization. Clustering is an energy efficient way of organizing the network in a systematic way for proper load distribution and maximize network lifetime. To facilitate the optimal selection of Cluster Heads (CHs), in this paper, we propose an Improved Artificial Bee colony optimization based ClusTering(IABCOCT) algorithm by utilizing the merits of Grenade Explosion Method (GEM) and Cauchy Operator. This incorporation of GEM and Cauchy operator prevents the Artificial Bee Colony(ABC) algorithm from stuck into local optima and improves the convergence rate. The benefits of GEM and Cauchy operator are embedded into the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration during the process of CH selection. The simulation results reported that the IABCOCT algorithm outperforms the state of art methods like Hierarchical Clustering-based CH Election (HCCHE), Enhanced Particle Swarm Optimization Technique (EPSOCT) and Competitive Clustering Technique (CCT) interms of different measures such as throughput, packet loss, delay, energy consumption and network lifetime.

Summary Railroads carry more than 40% of the freight, in terms of tons per mile transported in North America. A critical portion of the railroad infrastructure is the more than 100,000 bridges, which occur, on the average, every 1.4 miles of track. Railroads have a limited budget for capital investment. Therefore, decisions on which bridges to repair/replace become critical for both safety and economy. North American railroads regularly inspected bridges to ensure safe operation that can meet transport demands, using inspection reports to decide which bridges may need maintenance, replacement, or further investigation. Current bridge inspection practices recommend observing bridge responses under live load to help assess bridge condition. However, measuring bridge responses under train loads in the field is a challenging, expensive, and complex task. This research explores the potential of using wireless smart sensors (WSS) to measure bridge responses under revenue service traffic that can be used to inform bridge management decisions. Wireless strain gages installed on the rail measure real‐time train loads. Wireless accelerometers and magnetic strain gages installed in the bridge measure associated bridge responses. The system is deployed and validated on a double‐track steel truss bridge on the south side of Chicago, Illinois, owned by the Canadian National Railway. A calibrated finite element model of the bridge with known train input load estimated the responses of the bridge at arbitrary, unmeasured locations, showing the possibility of applying the system for decision making process. These results demonstrate the potential of WSS technology to assist with railroad bridge inspection and management practice. Copyright © 2016 John Wiley & Sons, Ltd.

A Wireless Smart Sensor (WSS) has an embedded processor, which is employed for signal processing, communication, and integration capabilities. A state-of-the-art review of recent articles on the WSS technologies employed in Structural Health Monitoring (SHM) is presented in this paper. Different types of WSS and communication technologies are reviewed, and their advantages and disadvantages are pointed out. WSS networks provide a number of advantages for SHM such as robust data management, higher flexibility, low cost, and high potential for providing data for a better understanding of structural response and behavior. Hybrid platforms, fusing different technological platforms, appear to be promising schemes as the strengths of each technology are exploited. Next-generation WSS must consume less power, integrate more with new sensors, have improved noise immunity, and be capable of working with a huge quantity of data without losses produced by wireless communication. Power harvesting based on wind, solar, and structural vibration energy needs to be explored further for a long-term period. Truly smart sensors should have an inherent pattern recognition and machine learning capabilities. Authors advance the research ideology of integrating the sensor technology with recent advances in machine learning technologies.

In the era of smart sensor networks for Internet of Things (SSN-IoT), interconnected sensors and the Internet of Things (IoT) enable what was previously unattainable. These networks are comprised of strategically placed sensor nodes that are carefully planned to gather, process, and transmit data seamlessly in a variety of settings. In this research, the Comprehensive Adaptive Sensing and clustering system (CASC-Sys) is carefully and quantitatively probed in the context of SSN-IoT, with a focus on how it fits in and what effects it might have on smart cities. When we look at key performance measures, CASC-Sys is much jester than other clustering algorithms including proficient bee colony-clustering protocol (PBC-CP), Enhanced PSO-based clustering (EPSO-C), backup cluster head (BCH) clustering. Moreover, its most adept quality is that it clusters efficiently, with a time of 17.5, which is faster than PBC-CP (18.5), EPSO-C (21.25), and BCH (20.5). This expresses that CASC-Sys can quickly organize groups, which is a very crucial feature in dynamic sensor networks. Concerning network stability, CASC-Sys has a higher reaffiliation rate (RR) of 1.25 compared to PBC-CP (2.53), EPSO-C (1.58), and BCH (0.8), indicating that it ameliorates at keeping consistent connections that are necessary for data flow to continue. With only 8.88% of dead nodes, CASC-Sys has the most reliable network, beating out PBC-CP (22.28%), EPSO-C (17.57%), and BCH (26.12%). This evinces how strong it is at ceasing node breakdowns, which is a fundamental part of maintaining network functionality. Furthermore, CASC-Sys can handle the control messages overhead, earning 4.33 points compared to PBC-CP's 1.75, EPSO-C's 0.90, and BCH's 0.54 points. Another strength is the Packet Delivery Ratio (PDR). CASC-Sys guarantees safe data sharing with a PDR of 85.87%, higher than PBC-CP (80.98%), EPSO-C (77.42%), and BCH (75.08%). CASC-Sys demonstrates efficient data delivery, which is crucial for real-time applications. This gives us a clearer understanding of its performance and highlights its potential usefulness in various IoT applications, especially in smart cities.

Miniaturized electrochemical sensing systems are employed in day-to-day uses in the several area from public health to scientific applications. A variety of electrochemical sensor and biosensor systems may not be effectively employed in real-world diagnostic laboratories and biomedical industries due to their limitation of portability, cost, analytical period, and need of skilled trainer for operating devices. The design of smart and portable sensors with high sensitivity, good selectivity, rapid measurement, and reusable platforms is the driving strength for sensing glucose, lactate, hydrogen peroxide, nitric oxide, mRNA, etc. The enhancement of sensing abilities of such sensor devices through the incorporation of both novel sensitive nanomaterials and design of sensor strategies are evidenced. Miniaturization, cost and energy efficient, online and quantitative detection and multiple sensing ability are the beneficial of the nanostructured-material-based electrochemical sensor and biosensor systems. Owing to the discriminating catalytic action, solidity and biocompatibility for designing sensing system, nanoscale materials empowered electrochemical detection systems are accomplished of being entrenched into/combined with portable or miniaturized devices for specific applications. In this review, the advance development of portable and smart sensing/biosensing systems derived from nanoscale materials for clinical and biomedical applications is described.

The importance of technical, functional and smart textiles with high added value is gradually increasing. One class of smart textiles is chromic materials. Chromism is a reversible change in color caused by certain factors (temperature, pH, etc.). In this study, it is aimed to develop a process that will ensure the dyeability of cotton fabrics with halo-, thermo- and photochromic dyes by using padding method. After determining the optimum conditions for each chromic dyes, their binary and triple combinations were also performed. A cabinet design in which the color change of chromic fabrics can be observed with the effect of pH (acid and base vapors), temperature and/or light is also realized. In this cabinet, the color change of the fabrics is detected by camera depending on pH, temperature and time. It is determined that the chromic fabrics produced in this study can react well to changes in pH, temperature and light, and maintain color changing ability as long as 20 cycles. Using the triple combination of thermochromic, halochromic and photochromic dyes, a method for producing cotton smart sensor clothes (or a wristband etc.) that can provide one or both of the following functions; informing whether the ambient temperature is uncomfortable or risky for the worker (thermochromic), notifying if there is a dangerous chemical vapor (acid–base vapors) in the work environment (halochromic), and creating a visual effect (photochromic) by changing color with UV rays on one hand, while providing protection against UV rays on the other hand, was developed. Graphic abstract

This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening – It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change