Smart grids lay the foundation for future communities. Smart homes, smart buildings, smart streets, and smart offices are built when intelligent devices piles on intelligent devices. To reach the maximum capacity, they all must be supported by an intelligent power supply. For optimal and real-time electricity consumption, monitoring and trading, blockchain posses several potential benefits in its application to electricity infrastructure. To analyze the performance of the blockchain-based smart grid, this paper presents a virtual smart grid. A smart grid equipped with smart contracts, capable of executing virtual activities is evaluated and possible strengths and weaknesses are discussed. The paper draws a performance analysis of the blockchain-based smart grid by using the Ethereum and Hyperledger Fabric-based implementations.

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Existing ICT networks are characterized by high level of energy consumption. In order to power up 5G base station sites, rising energy cost and high carbon emissions are major concerns that need to be dealt with. To achieve carbon neutrality, ICT sector needs to transform base station sites in a self-sustainable manner using renewable energy sources, local batteries and energy conservation techniques, even in adverse weather conditions and unexpected power outages. In this paper, short term-forecasting models are studied for accurate energy consumption and production forecast. The proposed architecture provides adaptive energy conservation technique using time series data analysis and Long Short-Term Memory for 5GNR base station site which is independent of traditional power sources and is completely powered by green energy. The accuracy analysis of this study was performed by the Mean Square Error (MSE) and Root Mean Square Error (RMSE). The results show high accuracy levels of LSTM model in guiding short-term energy forecasting for green ICT networks.

Technologies are growing with the passage of time and providing solutions for the existing problems. One of such problems is to manage the crowd according to available capacity. Especially when the crowd density is dynamic because of dynamic position of the persons (Pilgrims). Not only dynamic position of the pilgrims makes crowd capacity dynamic but also special events increase and decrease in number of pilgrims that affect the capacity in a specific place. Therefore, it is not an easy job to estimate the available capacity according to the dynamic position and event. To overcome such problems of dynamic position and event-based crowd management different techniques and approaches are adopted. To solve the above-mentioned problem, this paper proposes a proactive approach to estimate the space occupy by the pilgrims in different positions in a zone or level. Maximum capacity in each zone and level is define on the basis of each position. On the basis of maximum and occupy capacities, available (remaining) capacity has been estimated in a zone and level according to the events such as Pilgrimage, Ramadan, Jummah etc. The occupied and available capacities in a zone, level or in the whole building can be estimated with the help of technologies such as Wireless Sensor Network (WSN), cloud computing, Internet of Things (IoT) and sensor topologies according to position and event. Simulation shows different scenarios according to the zones and different levels of building to prove the proactive approach. According to the results, it is concluded that zones, levels and point of entrances should be allocated to the pilgrims to avoid the congestion problems. Further for massive crowd and large area multi sink solution is better than single sink solution to estimate available capacity.