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Internet of Things (IoT) is the connection of any object to the internet, to generate useful information about its own state or surrounding environment. IoT allows new products and services to be applied in different areas, such as smart cities, industry, smart homes, environment monitoring, smart cars, heath monitoring and others. Fog computing emerges to meet the Quality of Service requirements, of low latency real time IoT systems, that Cloud Computing cannot guarantee. This paper presents a Fire Alarm fog System, for a Smart Home, with the development of an IoT device hardware. A fog system is also developed with a website, that displays the sensor values, and the estimated battery life of the IoT device. Calculations were done with a variation of sleep-time of the IoT device, the results shows an increase of 2.5 times of battery lifespan.
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6/20/19, 11)09 AMAutonomic IoT Battery Management with Fog Computing | SpringerLink
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Autonomic IoT Battery Management
with Fog Computing
International Conference on Green, Pervasive, and Cloud Computing
GPC 2019: Green, Pervasive, and Cloud Computing pp 89-103 | Cite as
Hugo Vaz Sampaio (1) Email author (hvazsampaio@gmail.com)
Ana Luiza Cordova de Jesus (1)
Ricardo do Nascimento Boing (1)
Carlos Becker Westphall (1)
1. Universidade Federal de Santa Catarina, , Florianopolis, Brazil
Conference paper
First Online: 27 April 2019
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11484)
Abstract
Internet of Things (IoT) is the connection of any object to the internet, to generate
useful information about its own state or surrounding environment. IoT allows new
products and services to be applied in different areas, such as smart cities, industry,
smart homes, environment monitoring, smart cars, heath monitoring and others.
Fog computing emerges to meet the Quality of Service requirements, of low latency
real time IoT systems, that Cloud Computing cannot guarantee. This paper presents
a Fire Alarm fog System, for a Smart Home, with the development of an IoT device
hardware. A fog system is also developed with a website, that displays the sensor
values, and the estimated battery life of the IoT device. Calculations were done with
a variation of sleep-time of the IoT device, the results shows an increase of 2.5 times
of battery lifespan.
Keywords
Fog computing IoT Zigbee Battery management Smart homes
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Acknowledgements
This work was partially supported by the Research and Innovation Support
Foundation of the State of Santa Catarina (FAPESC) under grant
23038.013359/2017-71.
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About this paper
Cite this paper as:
Sampaio H.V., de Jesus A.L.C., do Nascimento Boing R., Westphall C.B. (2019) Autonomic IoT Battery
Management with Fog Computing. In: Miani R., Camargos L., Zarpelão B., Rosas E., Pasquini R. (eds)
Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science, vol 11484.
Springer, Cham
First Online 27 April 2019
DOI https://doi.org/10.1007/978-3-030-19223-5_7
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Print ISBN 978-3-030-19222-8
Online ISBN 978-3-030-19223-5
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Internet of Things is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. While the Internet of Things is still seeking its own shape, its effects have already stared in making incredible strides as a universal solution media for the connected scenario. Architecture specific study does always pave the conformation of related field. The lack of overall architectural knowledge is presently resisting the researchers to get through the scope of Internet of Things centric approaches. This literature surveys Internet of Things oriented architectures that are capable enough to improve the understanding of related tool, technology, and methodology to facilitate developer’s requirements. Directly or indirectly, the presented architectures propose to solve real-life problems by building and deployment of powerful Internet of Things notions. Further, research challenges have been investigated to incorporate the lacuna inside the current trends of architectures to motivate the academics and industries get involved into seeking the possible way outs to apt the exact power of Internet of Things. A main contribution of this survey paper is that it summarizes the current state-of-the-art of Internet of Things architectures in various domains systematically. Keywords: Internet of Things (IoT), Architecture, Cyber physical system
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Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.