Journal
IEEE ACCESS
Volume 6, Issue -, Pages 11278-11288Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2799543
Keywords
Internet of Energy (IoE); fog computing; demand response (DR); consensus; access control encryption (ACE)
Categories
Funding
- National Natural Science Foundation of China [61571300, 61431008]
- National Key Research and Development Program of China [2016QY01W0104]
- National Natural Science Foundation of China [61571300, 61431008]
- National Key Research and Development Program of China [2016QY01W0104]
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Internet of Energy (IoE) is a novel decentralized energy supplying paradigm, which integrated highly scalable and distributed energy resources to satisfy the various demands in future green applications. The existing works focus on the monitor and control of the state of networked energy storage devices. However, optimizing the security of demand response (DR) management with given energy states under IoE circumstance is rarely studied. Due to the connection to the Internet, the DR management in IoE faces a number of unique cyber-physical security challenges. First, as distributed energy resources have a large number of stakeholders and any illegal skip-level energy access may cause disastrous results, it requires fog computing paradigm to enforce a more secure DR management. Second, in the localized energy networks of IoE, a corrupt DR participator can maliciously read and write DR strategies by using collusion attacks (e.g., reputation-based cheating and unfair competing). To address these issues, we propose a fog computing-enabled secure demand response (FSDR) scheme for IoE against collusion attacks using consensus and access control encryption. In FSDR, the fog node was reconstructed as a sanitizer to randomly transfer encrypted energy states and DR strategies with homomorphic operations. Moreover, a simulated annealing-based consensus algorithm was presented to examine the validity of the energy states and DR strategies. In addition, we establish the mathematical models of collusion attacks and attack defense approaches. The performance evaluation validated its efficiency.
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