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Wireless Sensor Network (WSN) is a network with a good organization that provides efficient communication services. However, high energy usage and attack data sometimes become the key challenges that degrade the entire network's Performance. Therefore, in the present research, a novel Chimp-based Curriculum Learning (CbCL) is created for the WSN mobile cloud environment to reduce energy usage and detect the attack node with the CH selection. Initially, the required nodes are deployed in the network. The attack nodes are monitored and removed based on Chimp's optimal functions; optimal CH is selected for the data transfer. Finally, data is shared between the attack node and normal nodes. The created framework was tested in the Matlab environment, and the network efficiency parameters were calculated. To validate the improvement, the results are related to the existing models. The higher Performance of the CbCL satisfied the network's efficient energy management and attack detection process. [ABSTRACT FROM AUTHOR]

The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this self−powered wireless sensor can operate stably under the data transmission cycle of 39.38 s when the heat source temperature is 70 °C. Only 19.57% of electricity generated by a thermoelectric power generation system (TPGS) is available for use. Before this, the power consumption of this wireless sensor had been accurately measured, which is 326 mW in 0.08 s active mode and 5.45 μW in dormant mode. Then, the verified simulation model was established and used to investigate the generation performance of the TPGS under the Dirichlet, Neumann, and Robin boundary conditions. The minimum demand for a heat source is cleared for various data transmission cycles of wireless sensors. Low−temperature industrial waste heat is enough to drive the wireless sensor with a data transmission cycle of 30 s. Subsequently, the economic benefit of the thermoelectric self−powered system was also analyzed. The cost of one thermoelectric self−powered system is EUR 9.1, only 42% of the high−performance battery cost. Finally, the SEPIC converter model was established to conduct MPPT optimization for the TEG module and the output power can increase by up to approximately 47%. This thermoelectric self−powered wireless sensor can accelerate the process of achieving energy independence for wireless sensors and promote the Fourth Industrial Revolution. [ABSTRACT FROM AUTHOR]

The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG. [ABSTRACT FROM AUTHOR]

Converting otherwise wasted kinetic energy present in the environment into usable electrical energy to power wireless sensor nodes, is a green strategy to avoid the use of batteries and wires. Most of the energy harvesters presented in the literature are based on the exploitation of a one-degree-of-freedom arrangement, consisting of a tuned spring-mass system oscillating in the main direction of the exciting vibration source. However, if the direction of excitation changes, the efficiency of the harvester decreases. This paper thus proposes the idea of a curved cantilever beam with a two-degree-of-freedom arrangement, where the two bending natural frequencies of the mechanical resonator are designed to be equal. This is thought to lead to a configuration design that can be used in practical circumstances where excitation varies its direction in the plane. This, in turn, may possibly lead to a more effective energy-harvesting solution to power nodes in a wireless sensor network. [ABSTRACT FROM AUTHOR]

Recently, addressing various wounds, ranging from skin injuries to pressure and chronic ulcers, has necessitated a more detailed approach to wound condition monitoring aimed at enhancing the patient's quality of life. Although most available wound dressings offer protection and promote the regeneration of wound lesions, they fail to monitor wound progression. To meet this need, this study introduces a pioneering solution: a wireless, battery‐free, optoelectronic diagnostic sensor seamlessly integrated into a colorimetric, pH‐sensitive wound dressing, designed to significantly enhance patient quality of life through improved wound care. Curcumin‐polycaprolactone (C‐PCL) dressing protect the wounds, promote cell regeneration, and possess antimicrobial properties. In addition, the dressing exhibits colorimetric pH‐monitoring capabilities across different wound conditions, enabling individuals without specialized knowledge to assess the status of their wounds. The core innovation lies in the integration of a green light‐emitting diode (LED) and photodiode, which meticulously evaluates color changes from yellow to red of colorimetric dressing, enhance the precision of this user‐friendly colorimetric diagnosis strategy. The integration of wireless battery‐free systems facilitates quantitative and real‐time monitoring of wound data in patients without inconvenience. These innovations will pave the way for rapid wound status evaluation, ensuring timely interventions and optimized care for deteriorating wound conditions. [ABSTRACT FROM AUTHOR]

Advanced wireless sensors, incorporating metal‐organic frameworks (MOFs), enzymatic systems, and nanocomposites, offer unparalleled solutions for monitoring analytes and human physiological signals. These cutting‐edge sensors, when used with external devices, enable real‐time monitoring of analytes and physicochemical processes within the human body, thereby enhancing the understanding of complex biological systems. This study presents advancements in sensor development, fabrication techniques, and user‐friendly protocols. The performance of these sensors is evaluated based on their selectivity, sensitivity, and detection limits. Moreover, this article explores limitations, challenges, and key strategies to enhance analyte recognition from onsite environmental and biological species, ensuring human point‐of‐care safety. [ABSTRACT FROM AUTHOR]

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water. [ABSTRACT FROM AUTHOR]

To enhance the precision and efficiency of the tractor-failure-rate and equipment-quality inspection, the present study introduces a wireless rapid detection method for assessing tractor quality. For this study, which was based on the symmetrical structural characteristics of tractors, we designed a magnetic suction accelerometer. The test system was composed of a wireless router, a magnetic suction accelerometer sensor, a data-acquisition terminal, and other components. This test system aimed to test the equipment quality of the tractor at idle speed before leaving the factory. The experiment found that the vibration characteristics of the tractor had a symmetrical pattern on the left and right sides of the front and rear axle at idle. When the idle speed of the tractor was 800 r/min and 1000 r/min, the predominant vibration direction of both sides of the front axle of the tractor was the Y direction, while the predominant vibration direction of the rear axle was the Z direction. The experimental results showed that the proposed wireless rapid detection method of tractor quality and the designed acceleration sensor had good testing accuracy. The present study could provide a novel rapid detection method for the failure detection of power machinery in the agricultural field and for inspection before leaving the factory. The implementation of the method could improve the detection efficiency, and reduce the detection cost and the incidence of failure during actual use. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have become a crucial technology because they are used in several contexts, such as public safety, environmental monitoring, healthcare, military operations, and surveillance systems. The applications of WSNs have increased because they can be deployed in areas where cable and power supply are difficult to use. However, a variety of security assaults can easily penetrate these networks. To solve this problem, a Firebug Optimized Modified Bee Colony algorithm (FOMBC) has been proposed, which achieves the maximum network security goal with lower energy consumption in WSN. The proposed method is carried out into two phases namely Secure node selection and Orthogonal Routing (OR). In the first phase, secure node among the WSN's initial nodes is selected based on characteristics such as trust, delay, and Quality of Services. In the second phase, Firebug Optimized Modified Bee Colony (FOMBC) Optimization technique is used to find optimal route based on factors like trust, distance, latency, and quality of service. The effectiveness of the proposed FOMBC approach is evaluated against existing techniques like Trust-Aware Routing Framework (TARF), SOAR (Simple Opportunistic Adaptive Routing Protocol), Particle Swarm Optimization, and Whale Optimization Algorithm for Secure Routing (PSWOSR). According to the experimental findings, the proposed FOMBC technique achieves high detection, maximum throughput, least latency, and minimum range. The proposed method achieves network lifetime of 4.7%, 5.8%, and 9.87% than the existing TARF, SOAR, and PSWOSR, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces the implementation of a new device for measuring deformations at the surface layers of bituminous pavement. Using wireless technology, rechargeable remotely, low cost, and easily positioned in a layer by coring after pavement construction, this sensor makes it possible to obtain measurements of the deformation when a vehicle passes by. The development of the wireless sensor is presented as well as its advantages and limitations. It was then tested in the laboratory under a hydraulic press and in situ using a full-scale test of the mobile load simulator (MLS10 type). This system allows simple measurement, gives reliable results, and could be a useful device for the structural monitoring of pavement structures. [ABSTRACT FROM AUTHOR]

Aseptic loosening is the dominant failure mechanism in contemporary knee replacement surgery, but diagnostic techniques are poorly sensitive to the early stages of loosening and poorly specific in delineating aseptic cases from infections. Smart implants have been proposed as a solution, but incorporating components for sensing, powering, processing, and communication increases device cost, size, and risk; hence, minimising onboard instrumentation is desirable. In this study, two wireless, battery-free smart implants were developed that used passive biotelemetry to measure fixation at the implant–cement interface of the tibial components. The sensing system comprised of a piezoelectric transducer and coil, with the transducer affixed to the superior surface of the tibial trays of both partial (PKR) and total knee replacement (TKR) systems. Fixation was measured via pulse-echo responses elicited via a three-coil inductive link. The instrumented systems could detect loss of fixation when the implants were partially debonded (+7.1% PKA, +32.6% TKA, both p < 0.001) and fully debonded in situ (+6.3% PKA, +32.5% TKA, both p < 0.001). Measurements were robust to variations in positioning of the external reader, soft tissue, and the femoral component. With low cost and small form factor, the smart implant concept could be adopted for clinical use, particularly for generating an understanding of uncertain aseptic loosening mechanisms. [ABSTRACT FROM AUTHOR]

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13 % and 86.76 % for 1 and 3 data samples. [ABSTRACT FROM AUTHOR]

A compact wireless near-field hydrogen gas sensor is proposed, which detects leaking hydrogen near its source to achieve fast responses and high reliability. A semiconductor-type sensing element is implemented in the sensor, which can provide a significant response in 100 ms when stimulated by pure hydrogen. The overall response time is shortened by orders of magnitude compared to conventional sensors according to simulation results, which will be within 200 ms, compared with over 25 s for spatial concentration sensors under the worst conditions. Over 1 year maintenance intervals are enabled by wireless design based on the Bluetooth low energy protocol. The average energy consumption during a single alarm process is 153 μJ/s. The whole sensor is integrated on a 20 × 26 mm circuit board for compact use. [ABSTRACT FROM AUTHOR]

Resonators are passive time-invariant components that do not produce a frequency shift. However, they respond to an excitation signal close to resonance with an oscillation at their natural frequencies with exponentially decreasing amplitudes. If resonators are connected to antennas, they form purely passive sensors that can be read remotely. In this work, we model the external excitation of a resonator with different excitation signals and its subsequent decay characteristics analytically as well as numerically. The analytical modeling explains the properties of the resonator during transient response and decay behavior. The analytical modeling clarifies how natural oscillations are generated in a linear time-invariant system, even if their spectrum was not included in the stimulation spectrum. In addition, it enables the readout signals to be optimized in terms of duration and bandwidth. [ABSTRACT FROM AUTHOR]

In many buildings with wooden structures, the service life and comfort of the floor will be affected by the walking load of the human body. The vibration effect generated by the human walking on the floor will affect the comfort that the floor brings to people. This article primarily focuses on the study of the correlation between floor vibration and human walking load in wooden buildings. It provides an analysis and summary of research findings from both domestic and international sources. Furthermore, the data collected is utilized to establish a model for estimating human walking load. The advent of information technology has witnessed significant transformative changes spurred by shifts in societal needs. Notably, the establishment of wireless sensor networks stands as a significant milestone within the field of information technology, highlighting advancements in science and technology. The evolution of wireless sensor networks has proven instrumental in various sectors within China, including but not limited to the military, environmental monitoring, biological research and medical systems, disaster monitoring, and energy allocation. This paper uses deep learning algorithms to analyze the node deployment problems of wireless sensor networks, summarizes a reasonable node deployment plan through specific research and practice, and uses collaborative deep learning methods to improve the operating efficiency of the network. In addition, this article also tested the vibration effect of wood structure buildings, analyzed different influencing factors, and provided a good reference point for the formulation of wood structure comfort standards. [ABSTRACT FROM AUTHOR]

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With the continuous development of technology, music performances are gradually moving from the traditional stage format to the virtual reality (VR) environment to provide a more immersive experience for the audience. But traditional music performance lacks interaction and adaptability with the audience. This research aims to propose an adaptive matching optimization and VR system for indoor music performance based on wireless sensors to realize the interaction and self-adaptation between music performance and audience. The research uses wireless sensor networks to obtain audience behavior and feedback information, and matches it with music performance. By collecting physiological indicators such as the audience's movements, emotions and heartbeat, we can analyze and identify the audience's emotion and experience state in real time, so as to realize the adaptive music performance. The experimental results show that the adaptive music performance system based on wireless sensor successfully improves the audience's participation and emotional experience. The audience can interact with the musical performance through their movements and emotions, and feel a personalized musical experience that enables the audience to be more immersed and engaged.

Closed-loop deep brain stimulation (cl-DSB) is recognized as one of the most prominent schemes of neurostimulation therapy which aims to reduce the level of hand tremor in Parkinson’s disease (PD). Recently, modern neurostimulation has integrated wireless sensors and programming devices with communication infrastructures to monitor and regulate hand speed remotely. However, deploying wireless communication technologies introduces degradation procedures (i.e., latency, time delay, and so on) which adversely affect the neurostimulation treatment. Thus, in this paper, an adaptive fractional-order model predictive control (FOMPC) is designed to address the challenges of wireless neurostimulation in the cl-DSB system. For this purpose, a deep on-policy learning (DOL) algorithm is adopted to design parameters of the FOMPC in such a way that minimizes the fluctuations of hand speed and eliminates side effects of stimulation. Compared with the integer version of the model predictive controller, further flexibility is achieved by the non-integer orders. Deep neural networks (DNNs) of on-policy learning are trained to regulate tremor speed by dynamically adjusting the parameters of FOMPC in an episodic manner. The main novelty of this work is to consider the challenges of unreliable wireless communications, as it allows us to investigate the effects of latency and time delays in cl-DSB systems. This makes the suggested controller a prominent option for real-time applications where the presence of time delay can threaten the system’s stability. Extensive simulation scenarios under various levels of random communication delays are considered to assess the feasibility of suggested stimulators for wireless cl-DSB systems. Comparative analysis of cl-DBS’s responses reveals the superiority of the suggested FOMPC controller (designed by deep-on-policy learning) to regulate hand tremors over other state-of-the-art strategies.

The use of wireless sensors has expanded across various industries, highlighting the need for accurate positioning research. In irregular network environments, conventional distance vector jump algorithms result in poor localization performance. To solve this problem, an improved distance vector jump algorithm is proposed in this study. The algorithm introduces geometric constraint mechanism and optimized weighted DV-HOP localization algorithm. The proposed method applies an optimal weight function to determine the hop count from unknown nodes to anchor nodes and calculates the unknown node coordinates using the least squares method. Additionally, geometric constraint mechanisms are used to eliminate positioning errors caused by outliers. The experimental results demonstrate that the positioning error is 0.37% in C-type network environment, 0.38% in F-type network environment, 0.29% in L-type network environment and 0.30% in H-type network environment. These results verify the effectiveness of the proposed algorithm in reducing positioning errors, which can meet the accuracy requirements of practical applications in complex network environments.

Abstract Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training.

The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this self−powered wireless sensor can operate stably under the data transmission cycle of 39.38 s when the heat source temperature is 70 °C. Only 19.57% of electricity generated by a thermoelectric power generation system (TPGS) is available for use. Before this, the power consumption of this wireless sensor had been accurately measured, which is 326 mW in 0.08 s active mode and 5.45 μW in dormant mode. Then, the verified simulation model was established and used to investigate the generation performance of the TPGS under the Dirichlet, Neumann, and Robin boundary conditions. The minimum demand for a heat source is cleared for various data transmission cycles of wireless sensors. Low−temperature industrial waste heat is enough to drive the wireless sensor with a data transmission cycle of 30 s. Subsequently, the economic benefit of the thermoelectric self−powered system was also analyzed. The cost of one thermoelectric self−powered system is EUR 9.1, only 42% of the high−performance battery cost. Finally, the SEPIC converter model was established to conduct MPPT optimization for the TEG module and the output power can increase by up to approximately 47%. This thermoelectric self−powered wireless sensor can accelerate the process of achieving energy independence for wireless sensors and promote the Fourth Industrial Revolution.

The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG.

Power supply is playing an increasingly important role in the rapidly developing era of the Internet of Things. Achieving a sustainable and clean power supply for electronic devices is an urgent and challenging task. In this study, we present a heat-triggered triboelectric nanogenerator (TENG) and develop a self-powered fire alarm system to achieve an early warning without an external power supply. A TENG comprises a gear system that can utilize the elastic potential energy of a spring. A wax block was used as a heat trigger. When melted at high temperatures, the TENG will be triggered to work and generate considerable electric energy. Within a single operation cycle of approximately 6 s, a 22 μ F capacitor can be charged up to 3.7 V. Such electrical energy is sufficient to drive a wireless transmission module through an automatic switching circuit. Overall, this study provides a feasible approach for a self-powered wireless warning system in power-shortage areas. [ABSTRACT FROM AUTHOR]

The accelerated process of urbanization has led to an increase in the demand for housing, particularly in urban centers. While the housing industry brings economic benefits, it also has many problems, among which the most serious problem is the energy saving of construction. In order to better understand the energy consumption of various types of buildings, this paper uses the principles and technologies of Internet of things and wireless sensor network to analyze different villas and residential buildings. In the process of analysis, the collected data is transmitted to the computer for simulation, and the influence of the door and window structure and other building factors on the overall energy consumption of buildings is analyzed The consumption is analyzed in a unified way. Through the specific analysis, this article thinks that the use of solar radiation for building envelope construction can reduce energy consumption. This article also uses the Internet technology to develop a new indoor heating control system. This new intelligent system alleviates the shortcomings of the previous heating system to a certain extent, and provides people with a more convenient indoor temperature control structure, which improves the use value of the heating system. [ABSTRACT FROM AUTHOR]

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training. [ABSTRACT FROM AUTHOR]

Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks. [ABSTRACT FROM AUTHOR]

The Industry 4.0 has focus on connected devices and machines. It needs a number of sensors connected with each other and transfer of the information. Most of the sensors and sensor nodes require low power. In remote areas, where the power is limited, self-powered devices are more useful. Wind is available everywhere but the wind speed varies from place to place. Windmills are being used to generate electric power from the wind, however, is restricted due to large size and high cost. In this paper, it is proposed to develop a magnetic excited rotary harvester to harvest power at low wind speed. This can solve one of the major problems of frequent replacement of the battery in remote devices required for sensor and sensor nodes. To convert the rotation of the windmill to electric power, the rotation energy is converted to vibrating motion of a piezoelectric cantilever beam. The vibrations in the beam are generated with the help of interaction of magnetic field on the stator and blade mounted on the rotating shaft. The vibrations are then converted to electric charge due to the property of the piezoelectric material. An analytical model is developed and the results are compared with experiments. It is observed that at minimum wind speed of 2 m/s the estimated power is 1.06 mW while at a normal wind speed of 5 m/s power is calculated as 2.21 mW from the device. [ABSTRACT FROM AUTHOR]

The status monitoring system of train bogie is key to the overall health monitoring of a train. Typically dozens of sensors, like axle temperature monitoring, instability detection, vibration impact monitoring, etc., are deployed on each bogie, making the overall bogie wiring complex and maintenance challenging. In this paper, the ZigBee wireless communication technology and a cluster-based networking approach are employed to form a wireless sensor network for each bogie system. This significantly reduces the deployment time and cost and makes operation and maintenance more convenient. By utilizing network simulation tool NS2, a train bogie wireless sensor network model based on the IEEE 802.15.4 standard and the Ad Hoc on-demand distance vector (AODV) routing protocol was built. To mitigate packet loss rate and decrease end-to-end communication delay, modifications to the AODV protocol were undertaken based on the route timeout mechanism, the lifecycle of route request (RREQ), and the direction of RREQ propagation. Simulation results illustrate that, following the improvements, the AODV protocol reduces by 32% the time to reestablish communication after interruption. Simultaneously, packet loss rate and end-to-end communication delay are both reduced by over 50%. These results demonstrate that the wireless sensor network design proposed in this paper can satisfy the data transmission requirements of train bogie sensors.

Converting otherwise wasted kinetic energy present in the environment into usable electrical energy to power wireless sensor nodes, is a green strategy to avoid the use of batteries and wires. Most of the energy harvesters presented in the literature are based on the exploitation of a one-degree-of-freedom arrangement, consisting of a tuned spring-mass system oscillating in the main direction of the exciting vibration source. However, if the direction of excitation changes, the efficiency of the harvester decreases. This paper thus proposes the idea of a curved cantilever beam with a two-degree-of-freedom arrangement, where the two bending natural frequencies of the mechanical resonator are designed to be equal. This is thought to lead to a configuration design that can be used in practical circumstances where excitation varies its direction in the plane. This, in turn, may possibly lead to a more effective energy-harvesting solution to power nodes in a wireless sensor network.

When operating solar–wind power plants (SWPPs) located in populated areas, cases of premature failure of expensive batteries and other power equipment often occur. The purpose of this study is to develop a wireless data acquisition system (DAS) for the operation of an SWPP with a feedback function to prevent material damage from the failure of power equipment and to increase the efficiency of natural energy use. The principles of constructing a DAS, free from some of the disadvantages of analogues, are described in this paper. Intelligent wireless current and voltage sensors and a device for receiving and recording data with an additional feedback function have been developed, providing real-time feedback when the measured parameters go outside the norm. Measurement data are displayed on the laptop screen and alphanumeric display and stored on the hard drive along with timestamps and current event messages. An example of using a reverse communication channel to implement the functions of backup battery protection and to switch SWPP loads is described. The principles and methods proposed in this article are suitable for constructing systems for remote measurements of any physical quantities; therefore, the scope of application of the described system can be significantly expanded.

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

To enhance the precision and efficiency of the tractor-failure-rate and equipment-quality inspection, the present study introduces a wireless rapid detection method for assessing tractor quality. For this study, which was based on the symmetrical structural characteristics of tractors, we designed a magnetic suction accelerometer. The test system was composed of a wireless router, a magnetic suction accelerometer sensor, a data-acquisition terminal, and other components. This test system aimed to test the equipment quality of the tractor at idle speed before leaving the factory. The experiment found that the vibration characteristics of the tractor had a symmetrical pattern on the left and right sides of the front and rear axle at idle. When the idle speed of the tractor was 800 r/min and 1000 r/min, the predominant vibration direction of both sides of the front axle of the tractor was the Y direction, while the predominant vibration direction of the rear axle was the Z direction. The experimental results showed that the proposed wireless rapid detection method of tractor quality and the designed acceleration sensor had good testing accuracy. The present study could provide a novel rapid detection method for the failure detection of power machinery in the agricultural field and for inspection before leaving the factory. The implementation of the method could improve the detection efficiency, and reduce the detection cost and the incidence of failure during actual use.

In order to further study the application of energy acquisition model, based on the relevant theory of adaptive dynamic programming, the wireless sensor is tracked by modifying the energy acquisition model, and the relevant data and parameters are quantitatively analyzed according to the test results. Finally, the parameters of different samples are tracked and identified. The results show that the evaluation analysis curve with four different parameters can be divided into two stages according to the different variation trends: fluctuation stage and stable stage. It indicates that the increase of time can effectively improve the stability corresponding to the convergence of neural network. The trajectory under the three different parameter states shows a general trend of rapid decline at first and then tends to be stable. There is a certain disturbance in the rapid decline of the curve, but when the time is between 1 and 2, it corresponds to the drop of the curve, and the curve gradually flattens after rapid decline. Parameters in the adaptive dynamic programming model are shown as follows: parameter W decreases slowly to the lowest point and then rises slowly with the increase of iteration steps; The proportion of parameter W1 in the model shows a trend of slow increase. Relevant research can provide theoretical basis for the application of energy acquisition model based on adaptive dynamic programming and analysis in other fields.

Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of collected data and avoid table lookup, the adaptive weighted fusion algorithm is improved. According to the characteristics of the median and the mean value in the normal distribution, a new method of preprocessing to remove outliers is proposed to improve the accuracy of the final fusion result. The algorithm is used to calculate the temperature data to be processed in a greenhouse. The results showed that the fusion result after average processing was Xˆ\hat{X} = 15.77°C. The standard deviation is σ\sigma = 0.1194°C. After the treatment of the Grabbs criterion, the fusion result is Xˆ\hat{X} = 15.73°C and the standard deviation is σ\sigma = 0.1110°C. The fusion result of the improved algorithm is Xˆ\hat{X} = 15.74°C. The standard deviation is σ\sigma = 0.0959°C. Advantages of various preprocessing algorithms: improved algorithm > Grubbs method > no preprocessing. From the processing results of group A1 data, it can be seen that the improved algorithm can effectively suppress the ipsilateral shielding effect. Compared with the traditional Grubbs method to eliminate outliers and other algorithms, the improved algorithm can make the standard deviation of the fusion result smaller, and the fusion result can better represent the overall distribution, and there is no need to look up the table.

Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity-capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (Ω) and volumetric soil water content (θ). The Ω-θ relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the Ω-θ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (ECs) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (ECw). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (ECb) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks.