Research Article
Protected Bidding Against Compromised Information Injection in IoT-Based Smart Grid
@INPROCEEDINGS{10.1007/978-3-030-05928-6_8, author={Md Bhuiyan and Mdaliuz Zaman and Guojun Wang and Tian Wang and Md. Rahman and Hai Tao}, title={Protected Bidding Against Compromised Information Injection in IoT-Based Smart Grid}, proceedings={Smart Grid and Internet of Things. Second EAI International Conference, SGIoT 2018, Niagara Falls, ON, Canada, July 11, 2018, Proceedings}, proceedings_a={SGIOT}, year={2019}, month={1}, keywords={Internet of Things (IoT) Smart grid Demand response Security attack Privacy Compromised information injection}, doi={10.1007/978-3-030-05928-6_8} }- Md Bhuiyan
Mdaliuz Zaman
Guojun Wang
Tian Wang
Md. Rahman
Hai Tao
Year: 2019
Protected Bidding Against Compromised Information Injection in IoT-Based Smart Grid
SGIOT
Springer
DOI: 10.1007/978-3-030-05928-6_8
Abstract
The smart grid is regarded as one of the important application field of the Internet of Things (IoT) composed of embedded sensors, which sense and control the behavior of the energy world. IoT is attractive for features of grid catastrophe prevention and decrease of grid transmission line and reliable load fluctuation control. Automated Demand Response (ADR) in smart grids maintain demand-supply stability and in regulating customer side electric energy charges. An important goal of IoT-based demand-response using IoT is to enable a type of DR approach called automatic demand bidding (ADR-DB). However, compromised information board can be injected into during the DR process that influences the data privacy and security in the ADR-DB bidding process, while protecting privacy oriented consumer data is in the bidding process is must. In this work, we present a bidding approach that is secure and private for incentive-based ADR system. We use cryptography method instead of using any trusted third-party for the security and privacy. We show that proposed ADR bidding are computationally practical through simulations performed in three simulation environments.
