With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders...With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.展开更多
Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance...Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.展开更多
The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups,each of which handles a subset of all transactions.One of the issues with blockchain shardin...The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups,each of which handles a subset of all transactions.One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard.Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect,i.e.,that efficiency is more dependent on slower processing shard.Most of the existing studies focus on how to deal with cross-shard transactions,but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding.We perform a sharding analysis on 100,000 real transactions data on Ethereum,and the results show that there is a large difference in gas prices between different shards indeed.In this paper,we propose an Adaptive Weight Incentive(AWI)for Blockchain Sharding,which uses adaptive weight in place of traditional incentive,to address the problem of differing incentive costs for each shard.Take Ethereum as an example,AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price,the latency of the transaction,and the urgency of the transaction.Then the node chooses which transaction to pack based on the AWI-BS.Lastly,we also perform an in-depth analysis of AWI-BS's security and effectiveness.The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency,transaction hit rate,and system throughput.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant U1905211,Grant 61872088,Grant 62072109,Grant 61872090,and Grant U1804263in part by the Guangxi Key Laboratory of Trusted Software under Grant KX202042+3 种基金in part by the Science and Technology Major Support Program of Guizhou Province under Grant 20183001in part by the Science and Technology Program of Guizhou Province under Grant 20191098in part by the Project of High-level Innovative Talents of Guizhou Province under Grant 20206008in part by the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province under Grant ZCL21015.
文摘With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.
基金supported by National Natural Science Foundation of China under grants 52077213 and 62003332Youth Innovation Promotion Association CAS 2021358+1 种基金Shenzhen Science and Technology Research and Development Fund JCYJ20200109114839874NSFC-FDCT under its Joint Scientific Research Project Fund(Grant No.0051/2022/AFJ),China&Macao.
文摘Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.
基金supported by FDCT under its General R&D Subsidy Program Fund(0038/2022/A)。
文摘The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups,each of which handles a subset of all transactions.One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard.Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect,i.e.,that efficiency is more dependent on slower processing shard.Most of the existing studies focus on how to deal with cross-shard transactions,but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding.We perform a sharding analysis on 100,000 real transactions data on Ethereum,and the results show that there is a large difference in gas prices between different shards indeed.In this paper,we propose an Adaptive Weight Incentive(AWI)for Blockchain Sharding,which uses adaptive weight in place of traditional incentive,to address the problem of differing incentive costs for each shard.Take Ethereum as an example,AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price,the latency of the transaction,and the urgency of the transaction.Then the node chooses which transaction to pack based on the AWI-BS.Lastly,we also perform an in-depth analysis of AWI-BS's security and effectiveness.The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency,transaction hit rate,and system throughput.
