Autonomic IoT Battery Management with Fog Computing

Sampaio, Hugo Vaz; Jesus, Ana Luiza Córdova de; Boing, Ricardo do Nascimento; Westphall, Carlos Becker

doi:10.1007/978-3-030-19223-5_7

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Data visualization from the cell-battery vehicle transportation system at various scales can be conducted. A hierarchical functional display is created using the Cyber Hierarchy and Interactional Network (CHAIN) architecture [46].…”

Section: Cloud Computing In Battery-management Systemsmentioning

confidence: 99%

Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations

et al. 2022

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Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed and battery capacity usage. A battery-management system overcomes these traditional challenges and enhances the performance of managing battery modules. The integration of advancements and new technologies enables the provision of real-time monitoring with an inclination towards Industry 4.0. In the previous literature, it has been identified that limited studies have presented their reviews by combining the literature on different digital technologies for battery-management systems. With motivation from the above aspects, the study discussed here aims to provide a review of the significance of digital technologies like wireless sensor networks (WSN), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, blockchain, and digital twin and machine learning (ML) in the enhancement of battery-management systems. Finally, this article suggests significant recommendations such as edge computing with AI model-based devices, customized IoT-based devices, hybrid AI models and ML-based computing, digital twins for battery modeling, and blockchain for real-time data sharing.

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Section: Cloud Computing In Battery-management Systemsmentioning

confidence: 99%

Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations

et al. 2022

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“…Unlike the works already mentioned (Sampaio et al, 2019) tested its system with three different transmission media, Zigbee, Xbee antennas with Wi-Fi, and BLE with lowcost sensors such as MQ-2 and DHT11, it performed an analysis of the entire system from the work cycle of the sensors, the current per hour in mA of each sensor, of the Arduino in active and idle mode and the modules in their respective states. While (Babatunde et al, 2020) presents an air quality monitoring system whose central node unit is an MSP430FR5969 development card that interprets the CCS811 sensor values in carbon dioxide equivalent (eCO 2 ) and volatile organic compounds.…”

Section: Related Workmentioning

confidence: 99%

“…This system is capable of monitoring temperature, relative humidity, atmospheric pressure and concentration of PM 2.5 and PM 10 in 0.01cubic feet (ft 3 ) through an ESP8266 NodeMCU. Article Journal-Urban-Rural and Regional Economy (Abraham & Li, 2016;Sampaio et al, 2019;Hossein et al, 2020;Glass et al, 2020;Babatunde et al, 2020;Grijalva et al, 2022)…”

Section: Related Workmentioning

confidence: 99%

Development of a low-cost and low-power air quality and weather monitoring system

Quiñonez-Rodríguez

Sosa-Tinoco

Ruiz-Ibarra

et al. 2021

JURRE

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Due to the recent pandemic, air quality monitoring has seen a resurgence of interest, however, commercial equipment for environmental monitoring needs a considerable economic effort. In this project, a prototype of a meteorological and air quality monitoring system is developed and implemented. The validation of the system was conducted by comparing the energy consumption with the Gila station, a version used previously. The system is composed of a sensor node, a gateway, and an application server. Communication between the sensor node and the gateway is through the LoRaWAN low-power protocol and The Things Network (TTN) service is used for IoT integration. The system monitored meteorological and air quality variables using low-cost components and with low energy consumption and prolong its autonomous life thanks to an energy harvesting system.

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“…Sampaio, Jesus, Boing and Westphall [4] developed a HEMS and a real time fire alarm system, with Fog and battery powered IoT device. The IoT device had temperature, gas/smoke, and flame sensors, to monitor the environment conditions, and a Zig-Bee antenna to send data to the Fog server.…”

Section: Related Workmentioning

confidence: 99%

“…We are taking into consideration the challenges and demands for meeting real-time system requirements, guaranteeing large scale system network stability, and a lightweight management protocol. These solutions will be useful in scenarios such as traffic management in smart cities [1], smart greenhouses in agriculture [2,3], intelligent fire alarms in smart homes [4], for instance. The key contributions of this paper in-clude:…”

Section: Introductionmentioning

confidence: 99%

Autonomic Management of Power Consumption with IoT and Fog Computing

Sampaio,

Koch,

Westphall

et al. 2021

Preprint

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We introduce a system for Autonomic Management of Power Consumption in setups that involve Internet of Things (IoT) and Fog Computing. The Central IoT (CIoT) is a Fog Computing based solution to provide advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. The solution works by adjusting Home (H) and Away (A) cycles based on contextual information, like environmental conditions, user behavior, behavior variation, regulations on energy and network resources utilization, among others. Performance analysis through a proof of concept present average energy savings of 58.4%, reaching up to 61.51% when augmenting with a scheduling system and variable long sleep cycles (LS). However, there is no linear relation increasing LS time and more savings. The significance of this research is to promote autonomic management as a solution to develop more energy efficient buildings and smarter cities, towards sustainable goals.

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Autonomic IoT Battery Management with Fog Computing

Cited by 9 publications

References 10 publications

Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations

Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations

Development of a low-cost and low-power air quality and weather monitoring system

Autonomic Management of Power Consumption with IoT and Fog Computing

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