Design of Software and Data Analytics for Self-Powered Wireless IoT Devices

2018 ◽  
Author(s):  
Linga Reddy Cenkeramaddi ◽  
Ashish Goyal ◽  
Asheesh Bhuria ◽  
Srinivas M.B. ◽  
Soumya J
Keyword(s):  
Data Analytics ◽  
Self Powered ◽  
Iot Devices

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

scholarly journals All-Printed Human Activity Monitoring and Energy Harvesting Device for Internet of Thing Applications

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1197 ◽  
Author(s):  
Shawkat Ali ◽  
Saleem Khan ◽  
Amine Bermak
Keyword(s):  
Energy Harvesting ◽  
Human Activity ◽  
Indium Tin Oxide ◽  
Activity Monitoring ◽  
The Self ◽  
Smart Device ◽  
Bridge Rectifier ◽  
Self Powered ◽  
Iot Devices ◽  
Human Activity Detection

A self-powered device for human activity monitoring and energy harvesting for Internet of Things (IoT) devices is proposed. The self-powered device utilizes flexible Nano-generators (NGs), flexible diodes and off-the-shelf capacitors. During footsteps the NGs generate an AC voltage then it is converted into DC using rectifiers and the DC power is stored in a capacitor for powering the IoT devices. Polydimethylsiloxane (PDMS) and zinc stannate (ZnSnO3) composite is utilized for the NG active layer, indium tin oxide (ITO) and aluminum (Al) are used as the bottom and top electrodes, respectively. Four diodes are fabricated on the bottom electrode of the NG and connected in bridge rectifier configuration. A generated voltage of 18 Vpeak was achieved with a human footstep. The self-powered smart device also showed excellent robustness and stable energy scavenger from human footsteps. As an application we demonstrate human activity detection and energy harvesting for IoT devices.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Emulator for data analytics and IoT development projects

2020 ◽  
Vol 14 (1) ◽  
pp. 57-63
Author(s):  
Andrés Armando Sánchez Martin ◽  
Luis Eduardo Barreto Santamaría ◽  
Juan José Ochoa Ortiz ◽  
Sebastián Enrique Villanueva Navarro
Keyword(s):  
Data Analysis ◽  
Software Design ◽  
Design Patterns ◽  
Data Analytics ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Academic Environments ◽  
Software Design Patterns ◽  
Iot Devices

One of the difficulties for the development and testing of data analysis applications used by IoT devices is the economic and temporary cost of building the IoT network, to mitigate these costs and expedite the development of IoT and analytical applications, it is proposed NIOTE, an IoT network emulator that generates sensor and actuator data from different devices that are easy to configure and deploy over TCP/IP and MQTT protocols, this tool serves as support in academic environments and conceptual validation in the design of IoT networks. The emulator facilitates the development of this type of application, optimizing the development time and improving the final quality of the product. Object-oriented programming concepts, architecture, and software design patterns are used to develop this emulator, which allows us to emulate the behavior of IoT devices that are inside a specific network, where you can add the number of necessary devices, model and design any network. Each network sends data that is stored locally to emulate the process of transporting the data to a platform, through a specific format and will be sent to perform Data Analysis.

scholarly journals Cyber-Storms Come from Clouds: Security of Cloud Computing in the IoT Era

2019 ◽  
Vol 11 (6) ◽  
pp. 127 ◽  
Author(s):  
Michele De Donno ◽  
Alberto Giaretta ◽  
Nicola Dragoni ◽  
Antonio Bucchiarone ◽  
Manuel Mazzara
Keyword(s):  
Cloud Computing ◽  
Data Analytics ◽  
The Internet ◽  
Utility Computing ◽  
Security Issues ◽  
Potential Impact ◽  
Iot Devices ◽  
Computational Resources ◽  
The Internet Of Things ◽  
Security Of Iot

The Internet of Things (IoT) is rapidly changing our society to a world where every “thing” is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds.

scholarly journals Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 764 ◽  
Author(s):  
Jaehyun Park ◽  
Ganapati Bhat ◽  
Anish NK ◽  
Cemil S. Geyik ◽  
Umit Y. Ogras ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Gesture Recognition ◽  
Energy Budget ◽  
Optimization Technique ◽  
Optimization Approach ◽  
Activity Tracking ◽  
Battery Capacity ◽  
Self Powered ◽  
Iot Devices ◽  
Energy Harvesting Devices

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

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