Efficient Sensor Data Research Articles

CONSTRUCTING SENSOR SIGNAL PROCESSING CHANNEL FOR AUTONOMOUS ROBOTIC PLATFORMS

Context. The development of autonomous mobile robotic platforms has advanced rapidly, especially in cyber-physical systems where integrating physical components and computational resources is vital. A key challenge in such platforms is the efficient realtime processing of sensor signals under limited computational resources, enabling robots to operate independently of human intervention. Traditional signal processing methods demand significant power, which may limit mobile platforms constrained by energy and resources. This research focuses on restructuring sensor signal processing channels using digital bandpass filters while overcoming technical challenges posed by limited resources. Objective. The goal is to create an efficient method for processing sensor signals in autonomous mobile platforms with constrained resources. This involves using low-order bandpass filters, capable of adjusting their characteristics and improving quality through sequential connection of identical filters. Reducing the computational load allows for enhanced overall performance of cyber-physical systems, improving efficiency under changing conditions and enabling autonomous task completion. New computational formulas are also proposed to simplify the design and better utilize onboard resources. Method. The improved method for constructing sensor signal processing channels uses identical low-order frequency-dependent components, sequentially connected to solve challenges faced by higher-order components. This approach simplifies coefficient calculations for cutoff frequencies and improves filter performance by increasing the order and quality. The method achieves a quasilinear phase-frequency characteristic, ensuring minimal distortion in the processed signals, while significantly reducing computational requirements. Results. The proposed method effectively reduces computational costs while maintaining high performance in sensor signal processing. The new formulas allow for calculating filter coefficients with fewer resources, suitable for autonomous systems. Modelling and experimental verification confirm that this method lowers the computational load and enhances filter performance, enabling more efficient sensor data processing, extended battery life, and improved system reliability. Conclusions. This research presents an efficient approach to sensor signal processing for resource-constrained autonomous robotic platforms. Sequentially connecting identical frequency-dependent components reduces computational costs while maintaining high signal processing quality. These findings are recommended for real-time applications requiring efficient resource utilization, contributing to improved autonomy and adaptability in mobile robotic systems.

Energy efficient acoustic sensor data integration in hybrid mode operated pervasive wireless sensor network

Energy efficient acoustic sensor data integration in hybrid mode operated pervasive wireless sensor network

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

Architecting an Open-Source IIoT Framework for Real-Time Control and Monitoring in the Bioleaching Industry

Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery methods are environmentally unsound, prompting the exploration of greener alternatives like bioleaching, which utilizes the activity of microorganisms for a more sustainable recovery. However, the mechanisms involved in the process and the conditions to optimize the metabolic paths are still not completely known. Monitorization and automatization of the different stages composing the global process are crucial for advancing in the implementation of this novel technology at an industrial scale. For the first time, an open-source industrial IoT system is designed to enhance and regulate bioleaching by implementing real-time monitoring and control within the plant’s infrastructure. This system includes an Android app that displays real-time plant data from sensors and a robust server featuring a flexible application programming interface (API) for future applications. The app caters to specific needs such as remote sensor reading, actuator control, and real-time bioleaching alerts, ensuring secure access and proactive event management. By utilizing collected data, it minimizes downtime, equipment failures, and supply chain disruptions. The server maintains seamless communication with the plant controller, enabling efficient pump activation and sensor data transmission. A telegram bot demonstrates the API’s flexibility by forwarding plant alerts to users. During validation with concurrent remote user access, the application demonstrated its ability to prevent irreversible plant failures through an advanced alarm system. Ultimately, this IIoT system amplifies plant performance, safety, and efficiency by optimizing processes and decision-making capabilities. It emerges as a pivotal open-source tool, securing remote oversight and management of large-scale bioleaching plants, promising adaptability for future enhancements.

Indolizine-based fluorescent compounds array for noninvasive monitoring of glucose in bio-fluids using on-device machine learning

Monitoring blood sugar level is highly important for managing diabetes and preventing complications. Herein, we describe a paper-based portable assay system for highly accurate monitoring of glucose levels in tears. The system combines enzyme reactions, the perturbation of fluorescence properties, automated image analysis, and pattern recognition powered by a machine-learning algorithms in a single assay without extra manual steps. A small size (20 × 20 mm) assay system was prepared simply by printing black wax crosswise on cellulose paper, melting the wax with heat, and depositing four different fluorescent compounds and glucose oxidase at the test zones. Applying a small drop of sample in the center zone of the system evenly distributes the samples to the test zone via the paper capillary process and eventually induces a generation of unique fluorescence pattern that is specific to glucose. Two models are used to analyze image data collected from the fluorescent compounds array to make accurate estimations on glucose concentration: a random forest model that discretely classifies the concentration with respect to the training data granularity and an SVM regression model that estimates glucose concentration at interpolated finer granularity. These models are combined with feature engineering techniques for efficient sensor data processing on resource limited mobile platforms. As a result, using the random forest approach, glucose concentration in artificial tears can be accurately classified with 96.7% accuracy (0.2 mM intervals) (MSE of 0.060 mM) and the SVM regression model results in an MSE of 0.026 mM.

Novel Predictive Control and Monitoring System based on IoT for Evaluating Industrial Safety Measures

In this paper, the Accident Reduction Model (ARM) technique has been used to analyze different critical criteria in various industries. This ARM technique is used to determine the conclusions of the decision-making process. Valid data is obtained in the structure of the IoT with proper and consistent and useful information. The network address utility allows efficient sensor data. The necessary configuration procedure effectively monitors relevant sensor boundary values. Finally, we have ensured that the system will be able to provide dynamic performance in an IoT-based use of low-cost estimates and lower execution time.

A Review of Efficient Real-Time Decision Making in the Internet of Things

Emerging applications of IoT (the Internet of Things), such as smart transportation, health, and energy, are envisioned to greatly enhance the societal infrastructure and quality of life of individuals. In such innovative IoT applications, cost-efficient real-time decision-making is critical to facilitate, for example, effective transportation management and healthcare. In this paper, we formally define real-time decision tasks in IoT, review cutting-edge approaches that aim to efficiently schedule real-time decision tasks to meet their timing and data freshness constraints, review state-of-the-art approaches for efficient sensor data analytics in IoT, and discuss future research directions.

Digital Microscopic Image Sensing and Processing for Leather Species Identification

Leather is a durable material well-known for its fashion, style, and versatility. Identifying the animal species from which leather originated is necessary in leather quality-check, fraud detection, exotic animal protection, etc. The species identification techniques currently in practice involve subjective and supervised analysis with laboratory-specific devices. This paper discusses optimized and automated species identification by employing a portable and cost-effective (economically efficient) digital microscope. The goal is to acquire the leather images of the four most predominantly used permissible species, with the definite hair-pore regions. Preliminary experiments investigate the adequate image sensing parameters for efficient sensor data processing. Otsu’s thresholding followed by circular Hough transform (CHT) segments and estimates the morphological features of the informative hair-pore regions. The k-nearest neighbor (KNN) based machine learning algorithm models a pattern recognition technique for automated species prediction. Evaluation measures objectively validate the performance of the proposed pre-processing and hair-pore segmentation. The experimental analysis presents the uniqueness and significance of estimated morphological features. The study also compares KNN and Multi-Layer Perceptron (MLP) based species prediction. The comparative analysis ascertains the significance of KNN-based leather species identification with 92.5% accuracy. Thus, the present research assists in building the digital signatures of permissible leather species. It also contributes to design a cost-effective and automated leather species prediction technique with objective analysis.

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

Cloud-based Activity-aaService cyber–physical framework for human activity monitoring in mobility

This paper proposes Activity as a Service (Activity-aaService), a full-fledged cyber–physical framework to support community, on-line and off-line human activity recognition and monitoring in mobility. Activity-aaService is able to address the current lack of Cloud-Assisted Body Area Networks platforms and applications supporting monitoring and analysis of human activity for single individuals and communities. Activity-aaService is built atop the BodyCloud platform so enabling efficient BSN-based sensor data collection and local processing (Body-side), high performance computing of collected sensor data and data storing on the Cloud (Cloud-side), workflow-based programming of data analysis (Analyst-side), and advanced visualization of results (Viewer-side). Specifically, it provides specific, powerful and flexible programming abstractions for the rapid prototyping of efficient human activity-oriented applications. The effectiveness of the proposed framework has been demonstrated through the development of several prototypes related to physical activity monitoring, step counting, physical energy estimation, automatic fall detection, and smart wheelchair support. Finally, performance evaluation of the proposed framework at the Body-side of the activity classification has been carried out by analyzing processing load, data transmission time, CPU usage, memory footprint, and battery consumption using four heterogeneous mobile devices representing low, medium and high performance mobile platforms.

Timed Communication Buoy System: A Subsurface Mooring System for Efficient Sensor Data Recovery

Abstract An ocean subsurface mooring system is an effective platform for acquiring information that would advance our understanding of essential ocean processes and impacts of climate changes. Unlike a surface mooring system, data acquired from a subsurface mooring system cannot be downloaded until the whole system is retrieved. To achieve efficient sensor data recovery, a new, low-cost subsurface mooring system named the Timed Communication Buoy System (TCBS) was designed and implemented. The TCBS is usually integrated in the main float, and designated sensor data can be downloaded by the control system using a noncontact communication method termed Inductively Coupled Link. After data acquisition, an autonomous communication float in the TCBS can be released from the main float. Upon release, the communication float rises to the sea surface and transmits sensor data by satellite communication. To evaluate the performance of the TCBS, a short-term sea trial in the South China Sea (SCS) was conducted. Seawater temperature data over 30 days were collected and analyzed. The results demonstrated valid measurements of vertical wavenumber spectrum and frequency spectrum of internal waves in the SCS. TCBS will lead to important information needed to support fundamental research to better understand the characteristics of internal wave spectrum in the SCS.

A Generic System-Level Framework for Self-Serve Health Monitoring System through Internet of Things (IoT).

Sensor data are traveling from sensors to a remote server, data is analyzed remotely in a distributed manner, and health status of a user is presented in real-time. This paper presents a generic system-level framework for a self-served health monitoring system through the Internet of Things (IoT) to facilities an efficient sensor data management.

Performance Analysis of Modulation and Coding to Maximize the Lifetime of Wireless Sensor Network

A wireless sensor network (WSN) consists of several sensor nodes to monitor physical or environmental conditions. The development of WSNs is motivated by several applications such as military surveillance, industrial and consumer applications. In this paper transmission mechanism of a sensor node is analyzed with modulation schemes and error control codes. It furthermore compared based on channel condition in a sensor network. The modulation schemes considered for this work including 16-PSK, 16-PAM, 16-QAM and 16-FSK along with convolutional, Golay and RS codes under both AWGN (Additive White Gaussian Noise) and Rayleigh fading channels. To maximize the lifetime of WSN, appropriate combination of modulation scheme with error control codes is chosen for sensor data transmission. The result shows that 16-FSK with Golay codes in AWGN and 16-QAM with Golay codes in Rayleigh channel is more energy efficient than other combination of modulation and coding techniques for energy efficient sensor data transmission

Optical properties of pig skin epidermis and dermis estimated with double integrating spheres measurements

Non-invasive and non-destructive Near Infrared Reflectance Spectroscopy (NIRS) analysis of pork quality parameters is challenging due to the presence of a structured skin layer. The optical characterization (absorption coefficient, scattering coefficient and anisotropy factor) of porcine skin tissue can help in a better understanding of the light-tissue interaction, more efficient instrumental sensor designs and data modeling. For this purpose, double integrating spheres combined with an unscattered transmittance measurement system working in the range 1150–2250 nm (interval step of 10 nm) were used for optical characterization of pig epidermis and dermis. The estimated pig skin absorption coefficient spectra were dominated by water, while scattering coefficient spectra showed a steady decreasing tendency with increasing wavelength, except at strong absorption bands. The anisotropy factor spectra showed a stable value for each layer, although also some signatures were observed at strong absorption bands. A novel double integrating sphere system combined with an unscattered transmittance detector based both on a supercontinuum laser is used for optical characterization (absorption, scattering and angular scattering distribution) of pig skin tissues (epidermis and dermis) in the 1150–2250 nm wavelength range. The estimated data reported are relevant to understand individual effects of absorption, scattering and angular scattering distribution of NIR light through the tissue. Moreover, it can be used for simulating non-destructive NIRS measurement what can have an impact in the advance of NIRS technology toward in vivo/intact analysis. • A novel DIS and unscattered transmittance measuring system is used. • Absorption, scattering and anisotropy factor of pig skin tissues are estimated. • Sample thickness is a critical point in the measurement and estimation procedure. • The results would promote efficient NIRS sensor designs for non-destructive analysis.

Double integrating sphere measurements for estimating optical properties of pig subcutaneous adipose tissue

Abstract Optical properties of pig subcutaneous adipose tissue were estimated using double integrating sphere (DIS) measurements in combination with an inverse adding doubling (IAD) light propagation model. Total reflectance, total transmittance and unscattered transmittance were measured for the wavelength range 1150–2250 nm with 10 nm resolution. These measurements were used to estimate the bulk optical properties (absorption coefficient, scattering coefficient and anisotropy factor) of 10 Iberian pig adipose tissue samples. The estimated absorption coefficient spectra were dominated by water and lipid absorption bands, the main chemical components of the adipose tissue. These values showed a suitable match with the theoretical values calculated based on the average composition of the tissue. The obtained scattering coefficient spectra showed a smooth decrease with increasing wavelength, which is typical for biological tissues. The anisotropy factor spectra of adipose tissue were obtained, which describe the angular scattering pattern, instead of using a fixed value for the estimation procedure. The optical characterization described in this paper is crucial for a better understanding of the impact of pure absorption and scattering on the NIR spectra collected by a single measurement in the laboratory, process line at the slaughterhouse or on the farm. Moreover, the estimated data reported in this study would promote a more efficient sensor design and data processing optimization. Industrial relevance This paper provides an optical characterization (absorption, scattering and angular scattering distribution) of pig adipose tissues in a wide near-infrared wavelength range (1150–2250 nm) based on a novel spectroscopic setup. The estimated data reported are relevant to promote the design of innovative NIRS sensors and the development of new modeling procedures in order to optimize the non-destructive analysis of intact adipose tissues. This can have an important impact on the implementation of NIRS quality control systems in the agro-industry.

Augmented Reality-Based Embedded Sensor Location Recognition and Data Visualization Technique for Concrete Structural Health Monitoring

In recent years, numerous mega-sized and complex civil infrastructures are being constructed all over the world. Therefore, more precise construction and maintenance technologies are required for these complicated construction projects. So a variety of sensors-based structural health monitoring (SHM) techniques have been studied, but these techniques could not manage the sensors efficiently access the database obtained from the sensors. Recently, Quick response (QR) code and AR-based data access technologies have been developed. In this paper, an AR-based concrete curing strength monitoring technique for sensor management and efficient access of the measured data is introduced. It is confirmed that the AR-based concrete curing strength monitoring technique is useful for construction process. In addition, it is concluded that both efficient sensor location recognition and data visualization at anytime, anywhere, and any smart PC devices are promising.

Platform and State Estimation Design of a Small-Scale UAV Helicopter System

This paper presents the development of a small-scale unmanned aerial vehicle (UAV) helicopter system based on a Raptor 90 hobby helicopter. Firstly, onboard avionics system and ground station are designed carefully. The onboard avionics system mainly consists of sensors, flight control board, RF modem, and power supply system. The ground station comprises a computer and a RF modem. The main function of ground station is to monitor the status of onboard avionics. To avoid the effect of noises, some efficient sensor data processing and integrated navigation algorithms are designed and implemented on the onboard avionics. As a result, the constructed system exhibits low weight, small size, antivibration, and low power consumption; the essential information for system identification and automatic control can be easily acquired. Several ground and flight tests have been performed to verify the feasibility and reliability of the total system. The results show that it is sufficient for system identification and automatic control.

Efficient Sensor Stream Data Processing System to use Cache Technique for Ubiquitous Sensor Network Application Service

Problem statement: For the purpose of supporting application services in the Ubiquitous Sensor Network (USN) environment, efficient processing of stream data that occur in the sensor nodes and relevant utilization technologies are being stu died. Approach: The conversion of various sensor stream data that occur in the sensor nodes into sen sor data suitable for use in the upper-level application system is required through sensing data monitoring by applying meta-database from the middleware on the server side. Results: In the earlier developed sensor data processing sy stem, unnecessary search and operation were performed from the middleware on the server side as for sensor stream data that occurred in the same sensor node o r in the similar area. Conclusion/Recommendations: In this study, sensing cache technique is used and efficient sensor data stream processing system is designed and built to reduce the redundant sensor d ata stream processing.

RDDS: A Real-Time Data Distribution Service for Cyber-Physical Systems

One of the primary requirements in many cyber-physical systems (CPS) is that the sensor data derived from the physical world should be disseminated in a timely and reliable manner to all interested collaborative entities. However, providing reliable and timely data dissemination services is especially challenging for CPS since they often operate in highly unpredictable environments. Existing network middleware has limitations in providing such services. In this paper, we present a novel publish/subscribe-based middleware architecture called Real-time Data Distribution Service (RDDS). In particular, we focus on two mechanisms of RDDS that enable timely and reliable sensor data dissemination under highly unpredictable CPS environments. First, we discuss the semantics-aware communication mechanism of RDDS that not only reduces the computation and communication overhead, but also enables the subscribers to access data in a timely and reliable manner when the network is slow or unstable. Further, we extend the semantics-aware communication mechanism to achieve robustness against unpredictable workloads by integrating a control-theoretic feedback controller at the publishers and a queueing-theoretic predictor at the subscribers. This integrated control loop provides Quality-of-Service (QoS) guarantees by dynamically adjusting the accuracy of the sensor models. We demonstrate the viability of the proposed approach by implementing a prototype of RDDS. The evaluation results show that, compared to baseline approaches, RDDS achieves highly efficient and reliable sensor data dissemination as well as robustness against unpredictable workloads.