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Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows–Abadi–Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.

With the development in wireless communication and low-power device, users can receive various useful services such as electric vehicle (EV) charging, smart building, and smart home services at anytime and anywhere in smart grid (SG) environments. The SG devices send demand of electricity to the remote control center and utility center (UC) to use energy services, and UCs handle it for distributing electricity efficiently. However, in SG environments, the transmitted messages are vulnerable to various attacks because information related to electricity is transmitted over an insecure channel. Thus, secure authentication and key agreement are essential to provide secure energy services for legitimate users. In 2019, Kumar et al. presented a secure authentication protocol for demand response management in the SG system. However, we demonstrate that their protocol is insecure against masquerade, the SG device stolen, and session key disclosure attacks and does not ensure secure mutual authentication. Thus, we propose a privacy-preserving lightweight authentication protocol for demand response management in the SG environments to address the security shortcomings of Kumar et al.’s protocol. The proposed protocol withstands various attacks and ensures secure mutual authentication and anonymity. We also evaluated the security features of the proposed scheme using informal security analysis and proved the session key security of proposed scheme using the ROR model. Furthermore, we showed that the proposed protocol achieves secure mutual authentication between the SG devices and the UC using Burrows–Abadi–Needham (BAN) logic analysis. We also demonstrated that our authentication protocol prevents man-in-the-middle and replay attacks utilizing AVISPA simulation tool and compared the performance analysis with other existing protocols. Therefore, the proposed scheme provides superior safety and efficiency other than existing related protocols and can be suitable for practical SG environments.

In this report, we discuss low-density parity-check (LDPC) code for a holographic digital data storage (HDDS) system. In conventional LDPC decoding, the exact log likelihood ratio (LLR) value improves the error performance of the proposed LDPC code. A channel of an HDDS system has a nonlinear and complex characteristic. Therefore, it is difficult to obtain an exact probability model for an HDDS channel. In this study, an effective bit likelihood mapping method is developed and evaluated for LDPC decoding with a 6 : 8 balanced modulation code as a recording code. The LLR value, which represents the reliability of the 6 : 8 decision, is determined by considering demodulation error cases. An additional step is applied to a conventional LDPC decoding using feedback information in an iterative decoding in order to improve decoding performance. Feedback information can be helpful in accelerating the convergence of LDPC iterative decoding and in improving the error performance at the same number of decoding iterations. A simulation shows that the proposed algorithm scheme is effective and reliable.