Reliable Sensor Data Research Articles

EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study

Background Current health recommender systems lack interactivity that relates to the current situation. Methods We designed and implemented an intervention study for obese patients that incorporates context information obtained from smartphone and smartwatch sensors, gamification, as well as joint goal management of patients and health coaches. We developed a health behaviour recommendation system comprising of a smartphone application, cloud platform for data management, and a data dashboard for coaches. Results We conducted a three months long study and analysed data from eight patients, focusing on system function, patient adherence, satisfaction and overall impact of the proposed system on changing health-related habits. Along with data analysis, we also provide patient feedback collected during interview round after the end of the study. Conclusion Patients could successfully implement the goals using the EghiFit app. Challenges regarding the data collection, recommendation synthesis, and patient engagement persist. Furthermore, reliable sensor data processing on current smartphone platforms is difficult due to system restrictions. Future research should further integrate sensor data, gaming, and health behaviour intervention design using smart devices.

Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems

In today’s technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.

Deep Learning‐Driven Robust Glucose Sensing and Fruit Brix Estimation Using a Single Microwave Split Ring Resonator

AbstractExtracting the desired information from sensor data with various internal and external effects is a significant challenge in sensor applications. Difficult‐to‐control factors such as temperature, humidity, and sample position can significantly affect the stability and reliability of sensor data. In this paper, a deep learning‐based glucose sensing method that is robust to variations in sample position is proposed. It is shown that the variations in sample position affect the sensor data measured by the designed split ring resonator‐based microwave sensor. Then, artificial neural network and 1D convolutional neural network (CNN) models are evaluated for extracting glucose concentration information from the sensor data measured at random sample positions. The concentration of the glucose solution ranged from 1% to 23% (2% increments). The 1D CNN with all frequencies (0.5–18 GHz) of the and datasets outperformed the other model, with a mean absolute error (MAE) of 0.695% and a mean squared error (MSE) of 0.876 evaluated via cross‐validation. The study demonstrated that the sensor system can be applied in real life by performing fruit Brix estimation based on transfer learning of the previous 1D CNN network, and the MAE and MSE are 0.450% and 0.305, respectively.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

Ab. No. 101 A Conceptual Model of a Unified Wrist Sensor (UWS) for Non-Invasive Real-Time Preterm Infant Monitoring at NICU

Introduction: This study introduces a groundbreaking conceptual model for a Unified Wrist Sensor (UWS) designed to revolutionize non-invasive real-time monitoring of preterm infants in the Neonatal Intensive Care Unit (NICU). Current pain, sleep, and stress assessments in NICU settings are fraught with subjectivity and time constraints. The proposed UWS addresses these challenges by integrating multiple sensors into a single, non-restrictive wristband. Methods: The conceptual model employs a systematic approach, encompassing pre-processing, feature extraction, optimization, and learning models. Deep learning and machine learning techniques are harnessed to enhance accuracy in processing diverse physiological data. Standardization methods such as linear scaling, log scaling, or z-score are strategically applied to reduce variability, ensuring reliable outcomes across different individuals. Result: The discussion underscores the critical need for intelligent fusion structures that incorporate multiple sensor data and rich contextual information. The UWS aims to strike a balance between clinical utility and practicality, offering complementary, robust, and reliable multimodal sensor data. This approach provides a comprehensive understanding of pain, sleep, and stress patterns in preterm infants, paving the way for personalized treatment protocols. Conclusion: In conclusion, the proposed UWS emerges as a potential game-changer, appropriately reweighting data and enabling real-time monitoring of pain, stress, and sleep in NICU infants. The implications of this research are far-reaching, as the UWS can monitor physiological changes using multiple modalities, assess real-time patient outcomes, and dynamically adjust treatment protocols. Implication: This study contributes significantly to the field of neonatal care and introduces the prospect of seamlessly integrating advanced sensor technology into routine NICU practices.

Data Prediction-Based Energy-Efficient Architecture for Industrial IoT

This paper presents an energy-efficient industrial Internet of Things (IIoT) architecture that minimizes the data transmission process based on sensor data prediction. While current IIoT network implementations aim to improve lifetime and reduce maintenance costs, existing data prediction studies have primarily focused on prediction performance, disregarding computing time and energy efficiency. In this paper, we propose a data prediction approach on the base station side to maximize the energy efficiency of sensor nodes. A fast deep learning (DL) model is required to achieve low-latency network communications. Therefore, we exploited a DL-based multi-layer perceptron (MLP) with a deep concatenation method called DC-MLP to ensure data prediction reliability and fast computing time. To demonstrate its robustness, we evaluate the proposed DC-MLP model using six performance metrics with k-fold cross-validation. We varied the sampling rate for data prediction to demonstrate the effectiveness of prediction accuracy and energy efficiency. The performance evaluation results revealed that the proposed architecture successfully reduced energy consumption by up to 33% compared with traditional data transmission while maintaining reliable sensor data and achieving an 81% faster prediction time than existing DL models. Based on these findings, the application of the proposed DC-MLP has the potential to increase the sensor lifetime while satisfying the rigorous requirements of the industrial sector, such as fast prediction times, energy efficiency, and reliable prediction results.

Sensor Data Recovery of Faulty Rocket Engine Based on Multistage Graph Convolutional Network

Engine, the indispensable core of a rocket, has a significant impact on space exploration, especially the high-thrust liquid-propellant rocket engine. Most new-generation manned rockets for space stations or lunar exploration prefer the engine for its high performance and environmental friendliness. However, the engine is susceptible to failure under extreme conditions, which could cause catastrophic consequences without timely warning. Real-time state detection and fault location can prevent some catastrophic outcomes, but they require reliable sensor data. Nevertheless, some sensor data could be lost due to signal interruptions or equipment shutdown caused by system faults. Therefore, recovering the lost data based on the remaining measurements is a critical challenge that involves dealing with the distribution gap between normal and faulty data. To tackle the data drift and achieve real-time and high-precision sensor data recovery of the faulty engine, a multistage model based on graph convolutional networks is proposed in this paper. Trained by a multiloss function, the model primarily recognizes the status of the engine and passes the status to the next stage. Then the second stage recovers the lost data by two graph convolutional networks specific to the normal or faulty state. Evaluated on the practical experimental data from Xi’an Aerospace Propulsion Institute, our method successfully identifies the state of the system with accuracy above 99.99% and recovers the incomplete sensor data with a mean absolute error under 0.0065. Moreover, some ablation studies demonstrate that the blocks of two-stage and graph convolution could achieve a 26% improvement over the vanilla neural network.

Trends and Challenges in AIoT/IIoT/IoT Implementation.

For the next coming years, metaverse, digital twin and autonomous vehicle applications are the leading technologies for many complex applications hitherto inaccessible such as health and life sciences, smart home, smart agriculture, smart city, smart car and logistics, Industry 4.0, entertainment (video game) and social media applications, due to recent tremendous developments in process modeling, supercomputing, cloud data analytics (deep learning, etc.), communication network and AIoT/IIoT/IoT technologies. AIoT/IIoT/IoT is a crucial research field because it provides the essential data to fuel metaverse, digital twin, real-time Industry 4.0 and autonomous vehicle applications. However, the science of AIoT is inherently multidisciplinary, and therefore, it is difficult for readers to understand its evolution and impacts. Our main contribution in this article is to analyze and highlight the trends and challenges of the AIoT technology ecosystem including core hardware (MCU, MEMS/NEMS sensors and wireless access medium), core software (operating system and protocol communication stack) and middleware (deep learning on a microcontroller: TinyML). Two low-powered AI technologies emerge: TinyML and neuromorphic computing, but only one AIoT/IIoT/IoT device implementation using TinyML dedicated to strawberry disease detection as a case study. So far, despite the very rapid progress of AIoT/IIoT/IoT technologies, several challenges remain to be overcome such as safety, security, latency, interoperability and reliability of sensor data, which are essential characteristics to meet the requirements of metaverse, digital twin, autonomous vehicle and Industry 4.0. applications.

An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs.

Determining and Investigating the Variability of Bridges’ Natural Frequencies with Ground-Based Radar

Assessing the condition of bridge infrastructure requires estimating damage-sensitive features from reliable sensor data. This study proposes to estimate natural frequencies from displacement measurements of a ground-based interferometric radar (GBR). These frequencies are determined from the damped vibration after each vehicle crossing with least squares and compared to a Frequency Domain Decomposition result. We successfully applied the approach in an exemplary measurement campaign at a bridge near Coburg (Germany) with an additional comparison to commonly used strain sensors. Since temperature greatly influences natural frequencies, linear regression is used to correct this influence. A simulation shows that GBR, combined with the least squares approach, achieves the lowest uncertainty and variation in the linear regression, indicating better damage detection results. However, the success of the damage detection highly depends on correctly determining the temperature influence, which might vary throughout the structure. Future work should further investigate the biases and variability of this influence.

CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

Behavior Detection Mechanism for Trust Sensor Data Using Deep Learning in the Internet of Things

In this paper, we propose BDM-TSD(Behavior Detection Mechanism for Trust Sensing Data) to classify risk group and non-risk group for reliable sensor data identification in IoT environment with sensing function. BDM-TSD collects trust data such as sensing time, operation cycle, and type of sensing data of sensor devices connected to the IoT environment and artificial malicious data. The collected data performs network packet analysis and sensing data behavior analysis through the behavior of the sensor device that is subsequently operated through deep learning. Previously, research was conducted to detect unauthorized system calls of each device through security agents or abnormal behaviors through monitoring servers, and research to detect new and variant malicious behaviors with advanced attack techniques in IoT environments is insufficient A trusted IoT configuration is possible through malicious packet filtering and multi-sensor behavior detection. In this paper, we show how deep learning can be used to detect anomalies and malicious behaviors in the IoT environment based on the sensing function of multiple sensors.

PORTABLE AVIATION NAVIGATION CALIBRATION SYSTEM BASED ON RASPBERRY PI, HACK RF ONE, AND SOFTWARE DEFINED RADIO COMMUNICATION SYSTEM

Calibration procedures for the aviation navigation system which involve full size aircraft and its crew may be considered risky and high cost . This paper introduces the state of the art communication system to provide lower cost and flexible solutions based on Raspberry Pi, and SDR device Hack RF One, equipped with proper and aviation-related sensors, which later may be treated as unmanned aerial vehicle payload to replace flying aircraft aviation instruments. Design principles which mainly consist of both flexible communication and operating feasibility are explained in early of this paper. As a preliminary method, simple FSK modulation coupled with data integrity mechanisms are implemented to realize reliable but fast sensor data transmission. Data communication rate under a single board computer environment may achieve 40 kbps. Proposed system achieved a data transmission success rate as high as 88% in reasonable condition. In addition, the discussion of further system development is presented.

Inverse model-based virtual sensors for detection of hard faults in air handling units

Faults in air handling units (AHUs) in commercial buildings often waste energy and/or cause discomfort. Intermediate AHU sensors are dedicated merely for diagnostics and are essential to isolate faults rather than for controls; However, in many AHUs, such sensors are either missing, misplaced, or uncalibrated. This paper investigates three model-based methods to make up for the lack of reliable intermediate sensor data. To this end, trend data from five AHUs in five different buildings in Ottawa, Canada, are extracted for 2019. The accuracy of three different model forms is compared - artificial neural network (ANN), genetic algorithm (GA), and multiple linear regression (MLR) - are applied to model the supply air temperature of the AHUs. The behaviour of AHU heating and cooling coil valves and outside air dampers with and without the intermediate sensors is studied in this paper. Although installing temperature sensors before and after the heating and cooling coils facilitates detection of the faults occurring in AHUs, the authors showed the generated inverse models can act as virtual temperature sensors to estimate the intermediate measurements and isolate hard faults in AHU outside air dampers in addition to heating/cooling coil valves.

Blockage Avoidance Based Sensor Data Dissemination in Multi-Hop MmWave Vehicular Networks

MmWave communication has emerged as a promising technology to fulfill requirements of gigabit-level data rates, so as to support the exchange of large amounts of sensor data in vehicle-to-vehicle (V2V) communications. However, inter-vehicle line-of-sight (LOS) blockages may significantly deteriorate the link quality and lead to data packet loss. Addationally, it is difficult to coordinate directional transmissions to broadcast massive sensor data to nearby vehicles timely for enhanced environmental awareness. To overcome these challenges, this article proposes a blockage avoidance based multi-hop sensor data dissemination (BAMD) scheme in a centralized manner. First, we propose a mmWave LOS link availability identification method based on the geometric relation among vehicles. Second, the problem of sensor data dissemination among all vehicles is formulated aiming at minimizing the overall transmission delay. To find an approximate solution, a novel transmission utility function based multi-hop data dissemination algorithm is designed jointly considering the distance between vehicles, packet queue, the number of LOS neighbors and concurrent transmissions. By theoretical analysis and extensive simulation, it is indicated that the proposed scheme can achieve a reliable and fast sensor data dissemination. In high-density case, BAMD can improve reliability by 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and reduce transmission delay by 82 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared with baselines.

Design, development of MQ TELEMETRY TRANSPORT-sensor network interface protocol for the sensor device and IoT edge gateway

Abstract The Internet of things is gaining a lot of interestfrom day to day forvarious applications like Smart cities, Industrial health monitoring, smart homes, etc. IoT systems consist of sensor devices with communication capabilities, actuators, edgegateways, Back end Servers, and IoT applications. The Ultimate goal of an IoT network is to collect sensor data, reliable Transfer sensor data to the backend serverthrough the edgegateways. Back end server delivers the sensor data to the IoT Applications. IoT application process the Sensor data and apply the appropriate decision. Reliable Transfer of sensor data tothe Backend server isessential considering wireless sensor network characteristics, low computation power, and sensor devices' bandwidth. The IoT network deployment signaling protocol plays a crucial role in communicating sensor data to the IoT program. Various application messaging protocols are currently available, like MQ TELEMETRY TRANSPORT, COAP, HTTPetc. These protocols aredesigned, developed considering richcomputation powerand bandwidth. These protocolsare notenergy efficientfor sensor devices. MQ TELEMETRY TRANSPORT-SN protocol specified for sensor devices considering wireless sensor networks. In this article, Message Telemetry Transfer Protocol-sensor networks were planned, built, and validated according to the MQ TELEMETRY TRANSPORT-SN specification.

Graphene Oxide/Silver Nanocomposites as Antifouling Coating on Sensor Housing Materials

These days, sensors are widely used in a variety of underwater sites like marine monitoring, fish-farming and water quality monitoring. However, to achieve reliable sensor data from long-term monitoring in aqueous solution, several challenges still need to be solved. Biofilm formation both on sensor housings and membranes is among one of the most serious challenges, which strongly influences the sensor responds and the validity of the results. To prevent biofilm growth, a series of graphene oxide (GO)/silver nanoparticles (Ag NPs) nanocomposites (GOA) have been developed and coated on sensor housing materials, e.g. polypropylene. The antifouling property of the GOA nanocomposite has been demonstrated by antifouling tests using Halomonas. Pacifica (Baumann et al.) Dobson and Franzmann (ATCC® 27122) (H. Pacifica) and a mixture of marine algae. The antifouling property of GOA composites has been proved to be closely related to the dispersibility of Ag NP. The overall work might provide valuable insight into developing antifouling materials for sensors in general.

Scalable and Reliable Multi-dimensional Sensor Data Aggregation in Data Streaming Architectures

Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems designed according to the concept of stream processing. A common area of application is processing continuous data streams from sensors, for example, IoT devices or performance monitoring tools. In addition to analyzing pure sensor data, analyses of data for entire groups of sensors often need to be performed. Therefore, data streams of the individual sensors have to be continuously aggregated to a data stream for a group. Motivated by a real-world application scenario of analyzing power consumption in Industry 4.0 environments, we propose that such a stream aggregation approach has to allow for aggregating sensors in hierarchical groups, support multiple such hierarchies in parallel, provide reconfiguration at runtime, and preserve the scalability and reliability qualities of stream processing techniques. We propose a stream processing architecture fulfilling these requirements, which can be integrated into existing big data architectures. As all state-of-the-art stream processing frameworks have to handle a trade-off between latency, resource-efficiency, and correctness, our proposed architecture can be configured for low latency and resource-efficient computation or for always ensuring correct results. To assist adopters in choosing appropriate configuration options, we provide an experimental comparison. We present a pilot implementation of our proposed architecture and show how it is used in industry. Furthermore, in experimental evaluations we show that our solution scales linearly with the amount of sensors and provides adequate reliability in the presence of faults.

Scalable and Physical Radar Sensor Simulation for Interacting Digital Twins

Automation, artificial intelligence and sensor-enabled applications are part of everyday life comprising technical systems that utilize environment-perceiving sensors. Here, rough environments often require robust sensor technologies such as radar, coupled with application-specific software evaluating the measured data. The rising complexity of underlying sensor systems (hard- and software) and complex physical interactions with dynamic environments require development-related validations that use virtual methods primarily focusing on close-to-reality sensor simulation. To allow for a wide range of applications, we propose the use of reusable building blocks that generically model and mimic the physical structure from the real world. This includes the whole path between sensor and 3D environment with respect to sensor-intrinsic characteristics as well as environmental influences such as rain. At the same time, computational scalability of each building block allows physical simulations generating reliable sensor data even in real-time applications. As a result, Digital Twins of relevant components, systems and environments are no longer acting on their own but interacting within Virtual Testbeds allowing for virtual validation of radar-based applications with respect to the operational environment.