Salsa20 based lightweight security scheme for smart meter communication in smart grid

The traditional power gird is altering dramatically to a smart power grid with the escalating development of information and communication technology (ICT). Among thousands of electronic devices connected to the grid through communication network, smart meter (SM) is the core networking device. The consolidation of ICT to the electronic devices centered on SM open loophole for the adversaries to launch cyber-attack. Therefore, for protecting the network from the adversaries it is required to design lightweight security mechanism for SM, as conventional cryptography schemes poses extensive computational cost, processing delay and overhead which is not suitable to be used in SM. In this paper, we have proposed a security mechanism consolidating elliptic curve cryptography (ECC) and Salsa20 stream cipher algorithm to ensure security of the network as well as addressing the problem of energy efficiency and lightweight security solution. We have numerically analyzed the performance of our proposed scheme in case of energy efficiency and processing time which reveals that the suggested mechanism is suitable to be used in SM as it consumes less power and requires less processing time to encrypt or decrypt

Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model

With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data

Blockchain-enable contact tracing for preserving user privacy during COVID-19 outbreak.

Contact tracing has become an indispensable tool of various extensive measures to control the spread of COVID-19 pandemic due to novel coronavirus. This essential tool helps to identify, isolate and quarantine the contacted persons of a COVID-19 patient. However, the existing contact tracing applications developed by various countries, health organizations to trace down the contacts after identifying a COVID-19 patient suffers from several security and privacy concerns. In this work, we have identified those security and privacy issues of several leading contact tracing applications and proposed a blockchain-based framework to overcome the major security and privacy challenges imposed by the applications. We have discussed the security and privacy measures that are achieved by the proposed framework to show the effectiveness against the security and privacy issues raised by the existing mobile contact tracing applications