With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

Electrochemical sensors are powerful analytical tools that, in the last few years, have attracted tremendous attention in coupling with wearable devices due to their incomparable properties, such as instrumental simplicity, low cost, flexibility, and miniaturization. These outstanding characteristics fit with the desired features for continuous on-body analyses. Wearable electrochemical sensors enable obtaining insights into individuals' health status through the noninvasive monitoring of clinically relevant biomarkers in different biofluids (saliva, tears, sweat, and interstitial fluids) without complex manipulation, sampling, and treatment steps. The electrochemical system can be fabricated in different substrates and transferred to the human body or coupled to common utensils to monitor (bio)chemical species or potentially hazardous compounds surrounding the users without disturbing their usual activities. In this work, we critically review the recent advances, the main technological and chemical challenges identified in wearable electrochemical sensors for forensic and clinical applications, highlighting the remarkable trends, needs, and challenges for future studies.

Wearable electrochemical sensors for forensic and clinical applications

The next generation of electronics technology is purely going to be based on wearable sensing systems. Wearable electronic sensors that can operate in a continuous and sustainable manner without the need of an external power sources, are essential for portable and mobile electronic applications. In this review article, the recent progress and advantages of wearable self-powered smart chemical sensors systems for wearable electronics are presented. An overview of various modes of energy conversion and storage technologies for self-powered devices is provided. Self-powered chemical sensors (SPCS) systems with integrated energy units are then discussed, separated as solar cell-based SPCS, triboelectric nano-generators based SPCS, piezoelectric nano-generators based SPCS, energy storage device based SPCS, and thermal energy-based SPCS. Finally, the outlook on future prospects of wearable chemical sensors in self-powered sensing systems is addressed.

Recent developments in self-powered smart chemical sensors for wearable electronics

IoT enabled HELMET to safeguard the health of mine workers

In Internet of Things (IoT) based systems, the multi-level user requirements are satisfied by the integration of communication technology with distributed homogeneous networks termed as the ubiquitous computing systems (UCS). The PCS demands openness in heterogeneity support, management levels and communication for distributed users. However, providing these features is still a major challenge. In wearable IoT (WIoT) based medical sensors based applications, the end users reliability of communication is enhanced using a scalable distributed computational framework introduced in this paper. The demand and sharing parameters forms the basis of analysis of resource allocation by means of recurrent learning in this framework. The rate of communication may be improved while reducing the time delay for the end users of WIoT based medical sensors with the help of UCS and estimated resource requirements. Other than data transfer, sharing and resource allocation, end-user mobility management may also be performed on the WIoT medical sensors using the proposed framework. Certain metrics are used for proving the consistency of the framework that are assessed with the help of experimental analysis and performance estimation. Parameters inclusive of storage utilization, bandwidth, request backlogs, requests handled, request failure and response time are estimated. Reduced response time, backlogs and request failure with improved storage utilization, bandwidth and requests handled are evident using the proposed framework when compared to the existing models.

https://irojournals.com/jucct/V3/I1/03.pdf

Wearable IoT based Distributed Framework for Ubiquitous Computing

Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ( p < < 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care.

Knowledge graph (KG) entity typing focuses on inferring possible entity type instances, which is a significant subtask of knowledge graph completion (KGC). Existing entity typing methods usually exploit the entity representation to model the transmission between entities and their types, which cannot fully explore the fine-grained entity typing on identifying the semantic type of an entity. To address these issues, we propose Neighborhood-Attention Neural Fine-Grained Entity Typing (AttEt), which considers the neighborhood information of the entities from KGs to bridge entities and their types together. In this paper, AttEt first develops a type-specific attention mechanism to aggregate the neighborhood knowledge of the given entity with type-specific weights. These weights are beneficial to capture various characteristics for different types of the entity, and further imply the complex correlation among these fine-grained types. Then, AttEt adaptively integrates the aggregated neighbor-level representation with entity inherent embedding to calculate the matching score between the entity and its candidate type. Besides, many entities are sparse in their relations with other entities in KGs, which makes the entity typing task more challenging. To solve this problem, we present a smooth strategy on relation-sparsity entities to improve the robustness of the model. Extensive experiments on two real-world datasets (Freebase and YAGO) show that AttEt significantly outperforms state-of-the-art baselines in the HITS@1 by 2.11% on Freebase and by 8.42% on YAGO, respectively.

/pdf/a-neighborhood-attention-fine-grained-entity-typing-for-3nw72c3d.pdf

Weisi Han

Papers

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care.

A Neighborhood-Attention Fine-grained Entity Typing for Knowledge Graph Completion