Challenges of blockchain in new generation energy systems and future outlooks

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Managmnt and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods. Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust. We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat the COVID-19 pandemic. The proposed conceptual framework can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network. We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems. Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.

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Blockchain-Empowered Multi-Robot Collaboration to Fight COVID-19 and Future Pandemics

RBT: A distributed reputation system for blockchain-based peer-to-peer energy trading with fairness consideration

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Controlling and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods. Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust. We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat COVID-19 pandemic. The framework of our conceptual proposed can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network. We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems. Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.

Blockchain for Multi-Robot Collaboration to Combat COVID-19 and Future Pandemics.

Recently, the blockchain technology has attracted widespread attention due to its advantageous features, e.g., decentralization, transparency, traceability, and immutability. To make full use of renewable energy resources, new generation energy systems advocate the deep integration of information technology in real-world energy projects, among which blockchain has become one of the most used technologies. However, with the continuous development of related studies and projects, blockchain has begun to expose more and more limitations. As a result, the application of energy blockchain, i.e., blockchain applied in energy systems, is facing various challenges caused by these limitations. This paper briefly reviews popular application scenarios of energy blockchain, analyzes generic limitations of blockchain and their impacts on energy systems, and looks into possible directions that could deal with these limitations for future blockchain-based energy systems. We argue that blockchain is not a panacea for energy systems because blockchain’s component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems. As far as we know, this paper is one of the few works that deeply analyze the shortcomings of the blockchain technology and potential countermeasures in the energy field. 

The energy blockchain is a distributed Internet protocol for energy transactions between nodes in power systems. The consensus algorithm is the core component of the energy blockchain and has an es...

https://journals.sagepub.com/doi/pdf/10.1177/1550147720907335

Dynamic reputation–based consensus mechanism: Real-time transactions for energy blockchain:

Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.

Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications.

An Effective Credit Evaluation Mechanism with Softmax Regression and Blockchain in Power IoT

Entity linking refers to the process of mapping the entity reference in the text to the entity in the knowledge base, which is one of the key steps in the field of knowledge graph and knowledge fusion. We propose a joint graph feature entity linking method based on electrical knowledge base. Firstly, we construct a knowledge base with relatively rich entity information and professional characteristics of electricity domain, then identify the named entity mentions in the text. Based on the semantic similarity of multi features such as joint topic, context and metadata, the referenced linked entities are selected by restart random walk and joint disambiguation in the extended graph model. The experimental results show that the graph based joint feature entity linking method effectively improves the entity linking effect, especially in electricity domain.

Wei Jiang

Papers

Dynamic reputation–based consensus mechanism: Real-time transactions for energy blockchain:

Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking

An Effective Credit Evaluation Mechanism with Softmax Regression and Blockchain in Power IoT

An entity linking method based on graph feature and electricity domain knowledge base