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Autonomic IoT Battery Management with Fog Computing

  • April 2019
DOI:10.1007/978-3-030-19223-5_7
  • Conference: 14th International Conference on Green, Pervasive and Cloud Computing
  • At: Uberlandia, Brazil
Authors:
Hugo Vaz Sampaio at Federal University of Santa Catarina
Hugo Vaz Sampaio
Ana Luiza Cordova de Jesus
Ana Luiza Cordova de Jesus
Ricardo do Nascimento Boing at Federal University of Santa Catarina
Ricardo do Nascimento Boing
Carlos Becker Westphall at Federal University of Santa Catarina
Carlos Becker Westphall
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Abstract and Figures

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
Page 1 of 5https://link.springer.com/chapter/10.1007/978-3-030-19223-5_7
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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6/20/19, 11)09 AMAutonomic IoT Battery Management with Fog Computing | SpringerLink
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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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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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Full-text available
  • Mar 2017
Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.
Fog Computing: A Taxonomy, Survey and Future Directions
Chapter
Full-text available
  • Nov 2016
In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
A Survey on Internet of Things Architectures
Article
  • Dec 2016
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
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Article
  • Jun 2016
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.