Self-Synchronization in Duty-cycled Internet of Things (IoT) Applications

In recent years, the networks of low-power devices have gained popularity. Typically these devices are wireless and interact to form large networks such as the Machine to Machine (M2M) networks, Internet of Things (IoT), Wearable Computing, and Wireless Sensor Networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.Comment: 12 Pages, 11 Figures, Journa

The Readiness of Automotive Manufacturing Company on Industrial 4.0 Towards Quality Performance

This research is focusing on analyzing readiness of automotive manufacturing firm on Industry 4.0 towards quality performance. In the era of globalization, most manufacturing firms all over the world are constantly looking for ways to increase productivity. The manufacturing industry is mainly faced with the problem of Industry 4.0’s awareness and implementation. Top and middle management of DRB-HICOM Automotive manufacturing firm from all departments have been selected as a sample study. The questionnaire has been used to get the feedback from top and middle management about readiness of Industry 4.0. There are three main objectives that were assessing the level of firm’s characteristics, determining the level of Industry 4.0 readiness for DRB-HICOM Automotive manufacturing company, namely via variables of Applied Technology, Enterprise Resource Planning (ERP), Internet of Things (IoT), Cyber Physical System (CPS) and determining the relationship between the Industry 4.0 and quality performance. Quantitative method was used in this research which incoperates the distribution of 96 questionnaires to the respondents involving DRB-HICOM Automotive manufacturing firm. In terms of conducting the required data analysis, Statistical Package for Social Science (SPSS) version 22 was used. Result shows that all related constructs have a significant yet strong relationship between Industry 4.0 and quality performance. It is hoped that this initial Industry 4.0 work will help to spur future research about implementation of Industry 4.0, across the boards among many industrial sectors

Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments

[EN] Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.This paper was supported in part by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional within the project Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software under Grant TIN2014-57991-C3-1-P, in part by the Ministerio de Educacion, Cultura y Deporte, through the Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015) under Grant FPU15/06837, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the Project TIN2017-84802-C2-1-P. (Corresponding author: Jaime Lloret.)Egea, S.; Rego Mañez, A.; Carro, B.; Sánchez-Esguevillas, A.; Lloret, J. (2018). Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments. IEEE Internet of Things. 5(3):1616-1624. https://doi.org/10.1109/JIOT.2017.2787959S161616245

Photovoltaic Energy Harvesting System Adapted for Different Environmental Operation Conditions: Analysis, Modeling, Simulation and Selection of Devices

The present research work proposes a photovoltaic energy harvester and an appropriate direct current (DC)/DC converter for a harvesting system after the study of the devices and taking the operation conditions. Parameters such as power, efficiency and voltage are taken into account under different environment conditions of illumination and temperature in order to obtain the best possible response. For this reason, suitable metal-oxide semiconductor field-effect transistor (MOSFET), diode, coil, frequency, duty-cycle and load are selected and analyzed for a DC/DC converter with boost architecture.This work has been supported by IK4-TEKNIKER research institute own funds and the joint work of Electronics and Communications and Intelligent Information System units and by the Department of Education of the Basque Government within the fund for research groups of the Basque university system [IT978-16]

Neuro-Dominating Set Scheme for a Fast and Efficient Robot Deployment in Internet of Robotic Things

International audienceInternet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT. In this context, we propose a Neuro-Dominating Set algorithm (NDS) to efficiently deploy a team of mobile wireless robots in an IoRT scenario, in order to reach a desired inter-robot distance, while maintaining global connectivity in the whole network. We use the term Neuro-Dominating Set to describe our approach, since it is inspired by both neural network and dominating set principles. With NDS algorithm, a robot adopts different behaviors according whether it is a dominating or a dominated robot. Our main goal is to show and demonstrate the beneficial effect of using different behaviors in the IoRT concept. The obtained results show that the proposed method outperforms an existing related technique (i.e., the Virtual Angular Force approach) and the neural network based approach presented in our previous work. As an objective, we aim to decrease the overall traveled distance and keep a low energy consumption level, while maintaining network connectivity and an acceptable convergence time

Mini Wind Harvester and a Low Power Three-Phase AC/DC Converter to Power IoT Devices: Analysis, Simulation, Test and Design

Wind energy harvesting is a widespread mature technology employed to collect energy, but it is also suitable, and not yet fully exploited at small scale, for powering low power electronic systems such as Internet of Things (IoT) systems like structural health monitoring, on-line sensors, predictive maintenance, manufacturing processes and surveillance. The present work introduces a three-phase mini wind energy harvester and an Alternate Current/Direct Current (AC/DC) converter. The research analyzes in depth a wind harvester’s operation principles in order to extract its characteristic parameters. It also proposes an equivalent electromechanical model of the harvester, and its accuracy has been verified with prototype performance results. Moreover, unlike most of the converters which use two steps for AC/DC signal conditioning—a rectifier stage and a DC/DC regulator—this work proposes a single stage converter to increase the system efficiency and, consequently, improve the energy transfer. Moreover, the most suitable AC/DC converter architecture was chosen and optimized for the best performance taking into account: the target power, efficiency, voltage levels, operation frequency, duty cycle and load required to implement the aforementioned converter

The Penetration of Internet of Things in Robotics: Towards a Web of Robotic Things

As the Internet of Things (IoT) penetrates different domains and application areas, it has recently entered also the world of robotics. Robotics constitutes a modern and fast-evolving technology, increasingly being used in industrial, commercial and domestic settings. IoT, together with the Web of Things (WoT) could provide many benefits to robotic systems. Some of the benefits of IoT in robotics have been discussed in related work. This paper moves one step further, studying the actual current use of IoT in robotics, through various real-world examples encountered through a bibliographic research. The paper also examines the potential ofWoT, together with robotic systems, investigating which concepts, characteristics, architectures, hardware, software and communication methods of IoT are used in existing robotic systems, which sensors and actions are incorporated in IoT-based robots, as well as in which application areas. Finally, the current application of WoT in robotics is examined and discussed

Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies