Embedded Sensor Networks Research Articles

Enhancing the performance of a resonance-based sensor network for soft robots using binary excitation.

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.

Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring

Abstract The crystAIr project is about sensitising burners for flame monitoring based on a discrete number of dynamic pressure sensors and a machine learning approach. The context is the development of better aircraft engines and the prospect of technical solutions for hydrogen-fuelled gas turbines. The combination of sensors and appropriate signal processing is designed to mimic a ‘feel’ for the state of the flame. The idea is to monitor the gas turbine's correct operation in real-time and anticipate impending problems such as flame blowout, combustion instability or flashback. Compared to conventional flame monitoring based on signal sampling, thresholding, band-pass analysis and time-lag measurement, this method should be less computationally intensive, more sensitive and more robust to artefacts. Not only should it be more responsive, but it should also anticipate problems based on a lessons-learned approach and outperform the conventional precursors. An unassisted machine learning method is used to achieve this. A custom-built AM burner designed for this experiment provides multiple instrumentation options. A powerful data acquisition system is set up to collect data on multiple channels, over a long time duration and at high speed to collect the learning signal chunks. The focus is on the detection of flames and, more specifically, the moment of their ignition and extinction, the operating conditions that are prone to flashback and the stability of the combustion. Additional measurement techniques are used to refine the learning method. The paper covers all these points and presents the first results.

Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.

An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.).

Retracted: Interbank Offered Interest Rate Risk Measurement Based on Embedded Sensor Network

Retracted: Interbank Offered Interest Rate Risk Measurement Based on Embedded Sensor Network

Retracted: Internet Football Training Teaching Data Analysis Based on an Embedded Sensor Network

Retracted: Internet Football Training Teaching Data Analysis Based on an Embedded Sensor Network

Retracted: The Creative Route of University Ideological and Political Teaching on the Essential of Embedded Sensor Network

Retracted: The Creative Route of University Ideological and Political Teaching on the Essential of Embedded Sensor Network

EMBEDDED SENSOR NETWORK

Sensors are quite important in the current world. Sensors advance society in a number of areas, including the monitoring of environment as well as human health, safety, and security. Advanced military, agricultural, medical, and disaster management areas frequently employ sensor nodes to streamline monitoring by humans. Due to the fact that sensors are frequently utilised in non-human environments and for monitoring purposes of terrestrial areas, a computer hardware and software combination known as an embedded system is created for this purpose. An embedded sensor network is a network that is positioned in the real world and communicates with it. The significance of embedded sensors in a network and how they operate are covered in this research.

Retracted: The Optimal Path of College Art Teaching Based on Embedded Sensor Network

Retracted: The Optimal Path of College Art Teaching Based on Embedded Sensor Network

Data-driven method for damage localization on soft robotic grippers based on motion dynamics.

Damage detection is one of the critical challenges in operating soft robots in an industrial setting. In repetitive tasks, even a small cut or fatigue can propagate to large damage ceasing the complete operation process. Although research has shown that damage detection can be performed through an embedded sensor network, this approach leads to complicated sensorized systems with additional wiring and equipment, made using complex fabrication processes and often compromising the flexibility of the soft robotic body. Alternatively, in this paper, we proposed a non-invasive approach for damage detection and localization on soft grippers. The essential idea is to track changes in non-linear dynamics of a gripper due to possible damage, where minor changes in material and morphology lead to large differences in the force and torque feedback over time. To test this concept, we developed a classification model based on a bidirectional long short-time memory (biLSTM) network that discovers patterns of dynamics changes in force and torque signals measured at the mounting point. To evaluate this model, we employed a two-fingered Fin Ray gripper and collected data for 43 damage configurations. The experimental results show nearly perfect damage detection accuracy and 97% of its localization. We have also tested the effect of the gripper orientation and the length of time-series data. By shaking the gripper with an optimal roll angle, the localization accuracy can exceed 95% and increase further with additional gripper orientations. The results also show that two periods of the gripper oscillation, i.e., roughly 50 data points, are enough to achieve a reasonable level of damage localization.

Interbank Offered Interest Rate Risk Measurement Based on Embedded Sensor Network

Interbank lending rate is the main benchmark interest rate in the domestic currency market, looking for an appropriate financial time series model to describe its stochastic fluctuation process, and choosing an appropriate risk measurement method to measure the domestic interbank lending interest rate risk. The risk management of the interest rate market has great theoretical and practical significance. This article is aimed at studying the interbank interest rate risk measurement based on the embedded sensor network and proposes the key technology of sensor network, sensor network MAC layer protocol, LEACH energy model, and other methods in the article. Related experiments have been carried out on the interbank interest rate risk measurement. The experimental results show that the interbank interest rate risk measurement model based on the embedded sensor network is much more efficient than the traditional interest rate risk measurement model. This has reduced the bank’s loss of interest rate by at least 7%.

The Optimal Path of College Art Teaching Based on Embedded Sensor Network

As a new type of intelligent network technology, sensor network has been widely used in recent years. This paper is aimed at studying the optimization path of art teaching in colleges and universities based on embedded sensor network, proposing methods such as ZigBee network, HRPTC, wireless routing protocol, and sensor technology, and carrying out experimental research on the application of embedded sensor network in the optimization path of college art teaching. The experimental results show that the embedded sensor network can promote well the development of the optimization path of art teaching in colleges and universities and promote the reform of art education teaching methods. At the same time, the education and teaching methods optimized based on sensor network technology can better play the role of education and teaching so that students’ learning enthusiasm has been improved by at least 10%.

The Creative Route of University Ideological and Political Teaching on the Essential of Embedded Sensor Network

Contemporary society has opened the door to the Internet era due to the advancement of knowledge and skills. The utilization of Internet technology has brought unprecedented challenges to the teaching of academic and politics systems in college. This research is on the basis of the effect of embedded sensor networks on the innovation of intellectual and mechanical teaching paths in university. This article is aimed at analyzing the application of embedded sensor technology in image processing and video processing to provide a basis for the development of teaching paths in college. The article searches related literature to investigate the current status and progress. This article mainly selects 200 students from a certain university to participate in the experimental investigation under the mode of new web teaching. This article combines the embedded system, audio network node function, sensing network system, etc., to improve the teaching tools and methods on the basis of the previous one. Finally, this article conducts the evaluation and statistical analysis of students before and after the embedded sensor network application teaching path from the phrases of course content, education mode, education function, etc. The experimental results show that the content of education is more scientific and coordinated, and the students’ evaluations have changed from 4.4-6.3 to 6.8-8.1. After the utility of embedded sensing net, the influence played a role to a large altitude. Exploring the multiple paths not only is conducive to more effective and targeted ideological education of students but also makes difference in guiding practical work at the theoretical level.

5G Embedded Sensor Network System for Sports Information Service Hotspot Recommendation

How to rationally allocate and integrate existing resources has become the top priority of the development of sports public information services in the transition period, but it is also a weak link in the construction of current social information resources. This article is aimed at studying how to develop sports information service hotspots based on 5G embedded sensor network systems. Methods such as the network structure based on 5G embedded sensors, the Kriging algorithm based on global optimization, and sensor distance measurement are proposed and also conducted experiments on the application of 5G embedded sensor network system in sports information service hotspot recommendation. The results show that people are satisfied with the sports information service of the wireless sensor network, with a maximum score of 9.8, which is suitable for the information provision of sports information services. The development of the times has met the needs of most people.

Internet Football Training Teaching Data Analysis Based on an Embedded Sensor Network

Football is recognized as the “first sport” with the largest number of players and the highest concentration in the world, and it also plays a pivotal role in the development of Chinese sports. Whether football can be fully developed is the key to reflecting the strength of the national sports industry. Based on the embedded sensor network, this article analyzes the internet football training and teaching data. This article is aimed at analyzing the effects of the combination of embedded sensor networks and various indexes of football sports and at making full use of this technology to provide guidance for the update of football players’ training concepts. The article first analyzes the parameters of the embedded sensor and analyzes the specific details of the football players’ actions, such as skill, strength, and continuity of the action, based on the structure of the captured image. Then, this article selects 40 football players from a certain place and analyzes their concepts of football sports competition education and training. With the help of sensor network technology to carry out experimental comparison, this article focuses on the analysis of the experimental group and the control group, respectively, around movement stretching and dynamic integration, athletes’ training willingness, etc. The experimental training data shows that the experimental group has more advantages in training results whether it is muscle activation or nerve activation. In terms of nerve activation, the maximum parameter of the experimental group members was 8.74, and that of the control group was only 7.03. The gap between the two shows that through the application of embedded sensor network technology, Internet football training and teaching can provide a significant auxiliary role for the development of football players in all aspects.

Smart Farming enabled by IoT and Spectral Imaging

Smart Farming System is an emerging concept which utilizes sensors in the field enabled through IoT to get live data from the farm. This paper aims at developing such a Smart Farming system using the highly advanced technology of Texas instruments microcontrollers, MSP430 and TIVA C Series TM4C1294. Along with IoT the system uses Multispectral Imaging in conjunction with Wireless Soil Embedded Sensor Networks. The goal of the system is to provide reliable live data which is obtained from the multiple sensor nodes placed throughout the farm, that use the sink nodes to transfer the data to the cloud. The farmer can access this data using the Blynk Mobile app and can thus take further calculated actions towards maintaining the farm and further monitor the soil/crop health to increase the ultimate yield from his farm.

Application of compressed sensing in the guided wave structural health monitoring of switch rails

Switch rails are weak but essential components of high-speed rail (HSR) systems. In the condition-based maintenance of HSR, ultrasonic guided wave (UGW) on-line monitoring technology is widely used in judging real-time operating conditions; however, it always generates large amounts of data. Too much data bring significant challenges, such as too many unnecessary costs of energy, storage, and network bandwidth in the structural health monitoring of switch rails, making it challenging to realize embedded sensor networks with high durability and low power consumption. Furthermore, the structural damage occurs relatively less over the long-term, indicating that these measurements are inherently sparse. The sparseness of structural damage makes them attractive for the compressed sensing technique. This study proposes a novel data compression and reconstruction method to meet the challenges and reduce the amount of data transmitted by sensor networks and maintain their accuracies simultaneously. First, a lightweight data dictionary is constructed to perform a sparse decomposition of UGW signals according to the characteristics of UGW propagation. Second, an effective sampling method based on a sparse random matrix is designed for sub-Nyquist sampling and compressing UGW signals. Third, a novel block adaptive matching pursuing algorithm is proposed to reconstruct UGW signals from compressed data. Finally, numerical signals, finite element simulation, and several actual monitoring experiments on the foot of a switch rail are conducted to verify the effectiveness and accuracy of the proposed method. The influence of different compression ratios and block sizes on the reconstruction performance of guided wave signals is investigated. The results indicate that the proposed method can sample UGW signals with much lower requirements than the Nyquist sampling theorem and is superior to other novel algorithms.

Optimised embedded sensor network using multicore architecture for low power application

Embedded infrastructure takes on a significant role in energy and efficiency since the Rise of the Internet of Things (IoT). The integrated circuit wants more positive strategies to adapt to an IoT system to improve design and resource consumption. Numerous algorithms were offered in embedded frameworks for productive use of switching frequency, including dynamically scaled rates, thread representation and scarcity methods. These approaches have, however, some bottlenecks that enable use in the embedded frameworks. In light of these, this study proposes a novel MCA-LPA technique that includes a robust learning device optimisation for switching frequency by entry and core allotment based on the working load. The methodology was validated and analysed on the ARM/Cortex configurations. The application of the suggested system in simulation resulted in a 45% reduction in energy usage.

Optimised embedded sensor network using multicore architecture for low power application

Optimised embedded sensor network using multicore architecture for low power application

A Machine Learning Approach to Hand-Arm Motion Prediction for Active Upper Extremity Occupational Exoskeleton Devices

Exoskeleton devices are currently being utilized in a variety of occupational settings to reduce musculoskeletal efforts to lower fatigue, improve performance, and minimize work-related injuries associated with musculoskeletal disorders (MSDs). The intrinsic challenges that accompany the development of fully supporting and active upper extremity multi-degrees of freedom (DOF) devices include not only the mechanical design, but also lack of an intuitive way to control and operate these devices. A proof-of-concept instrumented handle consisting an embedded sensor network was developed with the intention to utilize artificial neural networks (ANN) to properly identify the intended motion of the user and to estimate the motion intensity. Results show this method is feasible and delivers useful insight into developing the next stages of the “smart handle” technology that will include the remaining hand motions, correctly classifying combination of intended motions and using the handle output to control complex multi-DOF upper extremity exoskeletons devices.

Mountain Temperature Changes From Embedded Sensors Spanning 2000 m in Great Basin National Park, 2006–2018

Mountains of the arid Great Basin region of Nevada are home to critical water resources and numerous species of plants and animals. Understanding the nature of climatic variability in these environments, especially in the face of unfolding climate change, is a challenge for resource planning and adaptation. Here, we utilize an Embedded Sensor Network (ESN) to investigate landscape-scale temperature variability in Great Basin National Park (GBNP). The ESN was installed in 2006 and has been maintained during uninterrupted annual student research training expeditions. The ESN is comprised of 29 Lascar sensors that record hourly near-surface air temperature and relative humidity at locations spanning 2000 m and multiple ecoregions within the park. From a maximum elevation near 4000 m a.s.l. atop Wheeler Peak, the sensor locations are distributed: (1) along a multi-mountain ridgeline to the valley floor, located ~2000 m lower; (2) along two streams in adjoining eastern-draining watersheds; and (3) within multiple ecological zones including sub-alpine forests, alpine lakes, sagebrush meadows, and a rock glacier. After quality checking all available hourly observations, we analyze a twelve-year distributed temperature record for GBNP and report on key patterns of variability. From 2006 to 2018, there were significantly increasing trends in daily maximum, minimum and mean temperatures for all elevations. The average daily minimum temperatures increased by 2.1°C. The trend in daily maximum temperatures above 3500 m was significantly greater than the increasing trends at lower elevations, suggesting that daytime forcings may be driving enhanced warming at GBNP’s highest elevations. These results indicate that existing weather stations, such as the Wheeler Peak SNOTEL site, alone cannot account for small-scale variability found in GBNP. This study offers an alternative, low-cost methodology for sustaining long-term, distributed observations of conditions in heterogeneous mountainous environments at finer spatial resolutions. In arid mountainous regions with vulnerable water resources and fragile ecosystems, it is imperative to maintain and extend existing networks and observations as climate change continues to alter conditions.