TY - GEN
T1 - A Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain
AU - Somayaji, Siva Rama Krishnan
AU - Alazab, Mamoun
AU - Manoj, M. K.
AU - Bucchiarone, Antonio
AU - Chowdhary, Chiranji Lal
AU - Gadekallu, Thippa Reddy
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.
AB - As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.
KW - Battery LIfe
KW - Blockchain
KW - Deep Neural Networks
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85102939075&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps50303.2020.9367413
DO - 10.1109/GCWkshps50303.2020.9367413
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85102939075
VL - 1
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
SP - 1
EP - 6
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway, NJ
T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020
Y2 - 7 December 2020 through 11 December 2020
ER -