Decentralized Storage Networks(DSNs)represent a paradigm shift in data storage methodology,distributing and housing data across multiple network nodes rather than relying on a centralized server or data center archite...Decentralized Storage Networks(DSNs)represent a paradigm shift in data storage methodology,distributing and housing data across multiple network nodes rather than relying on a centralized server or data center architecture.The fundamental objective of DSNs is to enhance security,reinforce reliability,and mitigate censorship risks by eliminating a single point of failure.Leveraging blockchain technology for functions such as access control,ownership validation,and transaction facilitation,DSN initiatives aim to provide users with a robust and secure alternative to traditional centralized storage solutions.This paper conducts a comprehensive analysis of the developmental trajectory of DSNs,focusing on key components such as Proof of Storage protocols,consensus algorithms,and incentive mechanisms.Additionally,the study explores recent optimization tactics,encountered challenges,and potential avenues for future research,thereby offering insights into the ongoing evolution and advancement within the DSN domain.展开更多
A decentralized battery energy storage system(DBESS)is used for stabilizing power fluctuation in DC microgrids.Different state of charge(SoC)among various battery energy storage units(BESU)during operation will reduce...A decentralized battery energy storage system(DBESS)is used for stabilizing power fluctuation in DC microgrids.Different state of charge(SoC)among various battery energy storage units(BESU)during operation will reduce batteries’service life.A hierarchical distributed control method is proposed in this paper for SoC balancing and power control according to dispatching center requirement in DBESS.A consensus algorithm with pinning node is employed to allocate power among BESUs in the secondary control whereas in the primary control,the local controller of BESU adjusts output power according to the reference power from secondary control.Part of BESUs are selected to be pinning node for accepting command from dispatching center while other BESUs as following nodes which exchange output power and SoC information with the adjacent nodes through communication network.After calculating reference power of each BESU by adopting consensus algorithm,the power sharing in DBESS is achieved according to their respective SoC of BESUs.Meanwhile,the total output power of DBESS follows the varying requirements of dispatching center.The stability of DBESS is also improved because of having no center controller.The feasibility of the proposed control strategy is validated by simulation results.展开更多
This paper introduces a novel privacy-aware Federated Proximal Policy Optimization(FPPO)method combined with action masking.As a Federated Reinforcement Learning(FRL)approach,the proposed method is used for optimizing...This paper introduces a novel privacy-aware Federated Proximal Policy Optimization(FPPO)method combined with action masking.As a Federated Reinforcement Learning(FRL)approach,the proposed method is used for optimizing the reloading of Domestic Hot Water(DHW)storage tanks,with a focus on energy savings and DHW thermal comfort in collective heating systems.The proposed approach combines FedProx as the Federated Learning(FL)method and Proximal Policy Optimization(PPO)as the Deep Reinforcement Learning(DRL)technique to address the challenges of distributed control while ensuring data privacy.Key contributions include:(1)employing action masking to guarantee compliance with comfort level,(2)designing a global reward function to align agents actions toward collective energy savings,(3)implementing a privacy-aware design where only model parameters are shared with a global aggregator,avoiding raw data transmission,and(4)optimizing PPO’s loss function for improved performance.PPO was benchmarked using a common FL method(FedAvg)alongside two other DRL methods,where PPO outperformed both in scalability and energy savings,especially in larger systems.Then,PPO-based FRL was refined into FPPO by integrating a proximal term with coefficient into the loss function to enhance the performance.Experiments were conducted with both fixed and dynamically adjusted,with the latter demonstrating better energy savings and comfort.Results show that FPPO achieves up to 10.08%energy savings while maintaining DHW discomfort below 8.72%in systems with at least 20 dwellings.These findings highlight FPPO as a scalable,privacy-aware,and energy-efficient solution for distributed control in collective heating systems.展开更多
The content-based addressing method of Interplanetary File System(IPFS)leads to the lack of the function of retrieving relevant data through key information.To solve this problem,this paper proposes an efficient IPFS ...The content-based addressing method of Interplanetary File System(IPFS)leads to the lack of the function of retrieving relevant data through key information.To solve this problem,this paper proposes an efficient IPFS keyword retrieval model–IPFS-DKRM(IPFS-Distributed keyword retrieval model).This model combines the global index with Adaptive Radix Tree,and optimizes the storage mode of IPFS network and node-local data index:The model adopts the global index method and uses ART to store the key information of global index locally and stores the complete index information in IPFS to reduce the time of data retrieval and update;The nodes of network through the Publish-Subscribe Pattern synchronization index,and the use of Conflict-free Replication Data Type(CRDT)to maintain the final consistency of the global index of each node,to ensure that all nodes in the local to provide efficient retrieval services.In the simulation experiment,the index of open source data set Crosswikis was constructed,and the performance was analyzed based on the results of Siva data.The experimental results showed that compared with the Siva model,the response retrieval time of IPFS-DKRM was reduced by 75%,and the space occupied by node local storage index was reduced by 70%.It proves that the model only needs to occupy a small amount of space to store the global index information in the system to provide efficient retrieval function for IPFS,so that IPFS canmeet more application scenarios in the future.展开更多
基金supported by the National Key R&D Program of China(2022YFB4501000)the National Natural Science Foundation of China(62232010,62302266,and U23A20302)+1 种基金Shandong Science Fund for Excellent Young Scholars(2023HWYQ-008)Shandong Science Fund for Key Fundamental Research Project(ZR2022ZD02)。
文摘Decentralized Storage Networks(DSNs)represent a paradigm shift in data storage methodology,distributing and housing data across multiple network nodes rather than relying on a centralized server or data center architecture.The fundamental objective of DSNs is to enhance security,reinforce reliability,and mitigate censorship risks by eliminating a single point of failure.Leveraging blockchain technology for functions such as access control,ownership validation,and transaction facilitation,DSN initiatives aim to provide users with a robust and secure alternative to traditional centralized storage solutions.This paper conducts a comprehensive analysis of the developmental trajectory of DSNs,focusing on key components such as Proof of Storage protocols,consensus algorithms,and incentive mechanisms.Additionally,the study explores recent optimization tactics,encountered challenges,and potential avenues for future research,thereby offering insights into the ongoing evolution and advancement within the DSN domain.
基金The part of establishing DBESS model was supported by National Natural Science Foundation of China(61473238,51407146)the primary droop control analysis got support of Sichuan Provincial Youth Science and Technology Fund(2015JQ0016)the part of distributed consensus algorithm was supported by Doctoral Innovation Funds of Southwest Jiaotong University(D-CX201714).
文摘A decentralized battery energy storage system(DBESS)is used for stabilizing power fluctuation in DC microgrids.Different state of charge(SoC)among various battery energy storage units(BESU)during operation will reduce batteries’service life.A hierarchical distributed control method is proposed in this paper for SoC balancing and power control according to dispatching center requirement in DBESS.A consensus algorithm with pinning node is employed to allocate power among BESUs in the secondary control whereas in the primary control,the local controller of BESU adjusts output power according to the reference power from secondary control.Part of BESUs are selected to be pinning node for accepting command from dispatching center while other BESUs as following nodes which exchange output power and SoC information with the adjacent nodes through communication network.After calculating reference power of each BESU by adopting consensus algorithm,the power sharing in DBESS is achieved according to their respective SoC of BESUs.Meanwhile,the total output power of DBESS follows the varying requirements of dispatching center.The stability of DBESS is also improved because of having no center controller.The feasibility of the proposed control strategy is validated by simulation results.
基金funded by a PhD fellowship of the Research Foundation Flanders(FWO)[1S08624N].
文摘This paper introduces a novel privacy-aware Federated Proximal Policy Optimization(FPPO)method combined with action masking.As a Federated Reinforcement Learning(FRL)approach,the proposed method is used for optimizing the reloading of Domestic Hot Water(DHW)storage tanks,with a focus on energy savings and DHW thermal comfort in collective heating systems.The proposed approach combines FedProx as the Federated Learning(FL)method and Proximal Policy Optimization(PPO)as the Deep Reinforcement Learning(DRL)technique to address the challenges of distributed control while ensuring data privacy.Key contributions include:(1)employing action masking to guarantee compliance with comfort level,(2)designing a global reward function to align agents actions toward collective energy savings,(3)implementing a privacy-aware design where only model parameters are shared with a global aggregator,avoiding raw data transmission,and(4)optimizing PPO’s loss function for improved performance.PPO was benchmarked using a common FL method(FedAvg)alongside two other DRL methods,where PPO outperformed both in scalability and energy savings,especially in larger systems.Then,PPO-based FRL was refined into FPPO by integrating a proximal term with coefficient into the loss function to enhance the performance.Experiments were conducted with both fixed and dynamically adjusted,with the latter demonstrating better energy savings and comfort.Results show that FPPO achieves up to 10.08%energy savings while maintaining DHW discomfort below 8.72%in systems with at least 20 dwellings.These findings highlight FPPO as a scalable,privacy-aware,and energy-efficient solution for distributed control in collective heating systems.
文摘The content-based addressing method of Interplanetary File System(IPFS)leads to the lack of the function of retrieving relevant data through key information.To solve this problem,this paper proposes an efficient IPFS keyword retrieval model–IPFS-DKRM(IPFS-Distributed keyword retrieval model).This model combines the global index with Adaptive Radix Tree,and optimizes the storage mode of IPFS network and node-local data index:The model adopts the global index method and uses ART to store the key information of global index locally and stores the complete index information in IPFS to reduce the time of data retrieval and update;The nodes of network through the Publish-Subscribe Pattern synchronization index,and the use of Conflict-free Replication Data Type(CRDT)to maintain the final consistency of the global index of each node,to ensure that all nodes in the local to provide efficient retrieval services.In the simulation experiment,the index of open source data set Crosswikis was constructed,and the performance was analyzed based on the results of Siva data.The experimental results showed that compared with the Siva model,the response retrieval time of IPFS-DKRM was reduced by 75%,and the space occupied by node local storage index was reduced by 70%.It proves that the model only needs to occupy a small amount of space to store the global index information in the system to provide efficient retrieval function for IPFS,so that IPFS canmeet more application scenarios in the future.
