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With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources. [ABSTRACT FROM AUTHOR]

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce impact of the availability of smart grid communication services caused by random failures and random time to repair. Firstly, we create a service channel violation risk degree (SCVRD) model to precisely track the violation risk change of communication service channel. It is denoted by the probability of service channel cumulative failure duration exceeding the prescribed duration. Secondly, a service channel violation risk degree routing mechanism is proposed to improve the availability of communication service. At last, the simulation is implemented with MATLAB and network data in one province are used as data instance. The simulation results show that the average service channel failure rate of availability-aware routing based on statistics (AAR-OS) algorithm and risk-aware provisioning algorithm are reduced by 15% and 6%, respectively. [ABSTRACT FROM AUTHOR]