A Prototype of Wireless Networked IoT Based Lighting Control in Open Platform

2020 ◽  
Vol 13 (3) ◽  
pp. 405-416 ◽  
Author(s):  
Sanjeev Kumar T.M ◽  
Ciji P. Kurian ◽  
Susan Varghese ◽  
Anil Upadhyaya ◽  
Anupriya John ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Environmental Benefits ◽  
Mesh Network ◽  
Sensor Data ◽  
The Internet ◽  
Self Healing ◽  
Sensors And Actuators ◽  
Lighting Control ◽  
Open Platform ◽  
The Internet Of Things

Background: The lighting researchers are keenly looking for the huge benefits of the internet of things on an open platform which provides the cost gains in addition to other environmental benefits. Connected systems interact with the software and analyse real-time building conditions, and feed information into the building controls network. Methods: This paper presents a wireless networked system for lighting control in buildings which connect the power of the Internet of Things. After analysing the ZigBee network on QualNet v7.4, a Digi Mesh network was set up using XBee modules using the XBee Configuration and Test Utility [XCTU] Software v6.3.11. The ThingSpeak cloud platform along with MATLAB 2017b provides the necessary cloud support to enable this network to communicate over the internet. The results indicate that the XBee S2C module functioning in the API mode when flashed with the DigiMesh firmware offers the best option for forming a self-healing mesh network. An aggregator node acts as an information sink and collects the sensor data from all the sensor nodes and passes it on to the cloud via the Raspberry gateway. Results: The algorithm on the cloud can read this sensor data and compute the necessary Pulse Width Modulation [PWM] signals required to control the brightness of a dimmable LED luminaire. The system also takes into consideration the zone-wise occupancy in the room while computing the PWM values to be sent to the luminaires. Conclusion: The use of the concept of open platform sensors and actuators is the significance of the work.

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals μPnP-Mesh: The plug-and-play mesh network for the Internet of Things

2015 ◽  
Author(s):  
Nelson Matthys ◽  
Fan Yang ◽  
Wilfried Daniels ◽  
Sam Michiels ◽  
Wouter Joosen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Mesh Network ◽  
The Internet ◽  
Plug And Play ◽  
The Internet Of Things

scholarly journals Sensor Data

2021 ◽  
pp. 41-48
Author(s):  
Savvas Rogotis ◽  
Fabiana Fournier ◽  
Karel Charvát ◽  
Michal Kepka
Keyword(s):  
Internet Of Things ◽  
Sensor Data ◽  
The Internet ◽  
The Internet Of Things

AbstractThe chapter describes the key role that sensor data play in the DataBio project. It introduces the concept of sensing devices and their contribution in the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.

Public street lighting control and monitoring system using the internet of things

2020 ◽  
Author(s):  
Andi Adriansyah ◽  
Setiyo Budiyanto ◽  
Julpri Andika ◽  
Arif Romadlan ◽  
Nurdin Nurdin
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
The Internet ◽  
Street Lighting ◽  
Lighting Control ◽  
Control And Monitoring System ◽  
The Internet Of Things

The Internet of Things and Opportunities for Pervasive Safety Monitored Health Environments

2017 ◽  
pp. 202-240
Author(s):  
Vaughan Michell
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Medical Errors ◽  
Information Transfer ◽  
Improve Patient Safety ◽  
The Internet ◽  
Sensors And Actuators ◽  
Surgical Interventions ◽  
Knowledge Based ◽  
The Internet Of Things

This chapter discusses the opportunities for new ubiquitous computing technologies, with concentration on the Internet of Things (IoT), to improve patient safety and quality. The authors focus on elective or planned surgical interventions, although the technology is applicable to primary and trauma care. The chapter is divided into three main sections with section 1 covering medical error issues and mechanisms, section 2 introducing Internet of Things, and section 3 discussing how IoT capabilities may address and reduce medical errors. The authors explore the existing theory of errors expounded by Reason (Reason, 2000, 1998; Leape, 1994) to identify perception-, decision-, and knowledge-based medical errors and related processes, environments, and cultural drivers causing error. The authors then introduce the technology of the Internet of Things and identify a range of capabilities from sensing, tracking, control, cooperative, and semantic reasoning. They then show how these new capabilities might be applied to reduce the errors expounded by the discussed error theories. They identify that: IoT enables augmentation of objects, which provides a massive increase in information transfer, thus improving clinician perception and support for decision-making and problem solving; IoT provides a host of additional observers and opportunities, which can shift the focus of overworked clinicians from constant monitoring to undertaking complex actions, such as decision making and care; IoT networks of sensors and actuators, through the addition of semantic and contextual rules, support decision making and facilitate automated monitoring and control of pervasive safety-monitored health environments, thus reducing clinician workload.

A Semantic Sensor Web Architecture in the Internet of Things

2019 ◽  
pp. 75-97
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Web Architecture ◽  
Hidden Knowledge ◽  
Semantic Sensor Web ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

The Internet of Things and Opportunities for Pervasive Safety Monitored Health Environments

2014 ◽  
pp. 382-420 ◽  
Author(s):  
Vaughan Michell
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Medical Errors ◽  
Information Transfer ◽  
Improve Patient Safety ◽  
The Internet ◽  
Sensors And Actuators ◽  
Surgical Interventions ◽  
Knowledge Based ◽  
The Internet Of Things

This chapter discusses the opportunities for new ubiquitous computing technologies, with concentration on the Internet of Things (IoT), to improve patient safety and quality. The authors focus on elective or planned surgical interventions, although the technology is applicable to primary and trauma care. The chapter is divided into three main sections with section 1 covering medical error issues and mechanisms, section 2 introducing Internet of Things, and section 3 discussing how IoT capabilities may address and reduce medical errors. The authors explore the existing theory of errors expounded by Reason (Reason, 2000, 1998; Leape, 1994) to identify perception-, decision-, and knowledge-based medical errors and related processes, environments, and cultural drivers causing error. The authors then introduce the technology of the Internet of Things and identify a range of capabilities from sensing, tracking, control, cooperative, and semantic reasoning. They then show how these new capabilities might be applied to reduce the errors expounded by the discussed error theories. They identify that: IoT enables augmentation of objects, which provides a massive increase in information transfer, thus improving clinician perception and support for decision-making and problem solving; IoT provides a host of additional observers and opportunities, which can shift the focus of overworked clinicians from constant monitoring to undertaking complex actions, such as decision making and care; IoT networks of sensors and actuators, through the addition of semantic and contextual rules, support decision making and facilitate automated monitoring and control of pervasive safety-monitored health environments, thus reducing clinician workload.

scholarly journals An Ontology-Based Cybersecurity Framework for the Internet of Things

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3053 ◽  
Author(s):  
Bruno Mozzaquatro ◽  
Carlos Agostinho ◽  
Diogo Goncalves ◽  
João Martins ◽  
Ricardo Jardim-Goncalves
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Sensitive Information ◽  
Sensors And Actuators ◽  
Model Driven ◽  
Security Services ◽  
Knowledge Reasoning ◽  
Resource Networks ◽  
The Internet Of Things

The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.

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