A distributed power system operation and control node privacy and security are attractive research questions that deliver electrical energy systems to the participating stakeholders without being physically connected to the grid system. The increased use of renewable energy in the power grid environment creates serious issues, for example, connectivity, transmission, distribution, control, balancing, and monitoring volatility on both sides. This poses extreme challenges to tackle the entire bidirectional power flow throughout the system. To build distributed monitoring and a secure control operation of node transactions in the real-time system that can manage and execute power exchanging and utilizing, balancing, and maintaining energy power failure. This paper proposed a blockchain Hyperledger Sawtooth enabling a novel and secure distributed energy transmission node in the EPS-ledger network architecture with a robust renewable power infiltration. The paper focuses on a cyber–physical power grid control and monitoring system of renewable energy and protects this distributed network transaction on the blockchain and stores a transparent digital ledger of power. The Hyperledger Sawtooth-enabled architecture allows stakeholders to exchange information related to power operations and control monitoring in a private ledger network architecture and investigate the different activities, preserved in the interplanetary file systems. Furthermore, we design, create, and deploy digital contracts of the cyber–physical energy monitoring system, which allows interaction between participating stakeholders and registration and presents the overall working operations of the proposed architecture through a sequence diagram. The proposed solution delivers integrity, confidentiality, transparency, availability, and control access of the distribution of the power system and maintains an immutable operations and control monitoring ledger by secure blockchain technology.
In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.
MnO2 has advantages such as the simple and diverse preparation methods, low cost and high theoretical capacity, but its industrial application is affected by its poor conductivity and fast attenuation of cycle performance. In order to improve its conductivity, battery capacity and performance, MnO2/carbon nanofibers (MnO2/CNFs) are obtained by using electrospinning technology, and the electrochemical performance was confirmed by XRD, SEM, TEM. Confirmed by comparison, the 20% MnO2/CNFs exhibit superior and excellent long cycling performance with a reversible capacity of 835 mA h g−1 at 0.1 A g−1 after the 133th cycle and a high initial specific capacity of 1094 mA h g−1 at a current density of 0.1 A g−1. The MnO2/CNFs have notable specific capacities with a coulombic efficiency of 99.5%, which greatly improve the reaction rate. This can also be used as a flexible electrode material because of its good flexibility. Due to the fact that carbon has better electron/ion conductivity, it shows better kinetics.
Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life.
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