Node Repair on Connected Graphs

Patra, Adway; Barg, Alexander

doi:10.1109/tit.2022.3145824

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…The traditional "end-device-data-centre" https://doi.org/10.15837/ijccc.2023.6.5998 2 architecture can hardly meet the low latency requirements of real-time data computation tasks [1,2]. In intelligent edge computing, although the use of edge devices (which have stronger computing power than terminal devices) can reduce the high latency caused by data transmission and improve the quality of service, there are also obvious limitations [3,4]. For example, when processing applications with high bandwidth requirements, such as high-definition video stream processing or big data analysis, intelligent edge computing may not be able to meet data transmission requirements due to the limitation of broadband resources of edge devices, resulting in an increase in service delay.…”

Section: Introductionmentioning

confidence: 99%

Iot Data Processing and Scheduling Based on Deep Reinforcement Learning

Jiang,

Wang,

Jin

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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With the continuous integration of IoT technology and information technology, edge computing, as an emerging computing paradigm, makes full use of terminals to process and analyse real-time data. The explosion of Internet of Things (IoT) devices has created challenges for traditional cloud-based data processing models due to high latency and availability requirements. This paper proposes a new edge computation-based framework for iot data processing and scheduling using deep reinforcement learning. The system architecture incorporates distributed iot data access, realtime processing, and an intelligent scheduler based on Deep q networks (DQN). A large number of experiments show that compared with traditional scheduling methods, the average task completion time is reduced by 20% and resource utilization is increased by 15%. The unique integration of edge computing and deep reinforcement learning provides a flexible and efficient platform for lowlatency iot applications. Key results obtained from testing the proposed system, such as reduced task completion time and increased resource utilization.

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Section: Introductionmentioning

confidence: 99%

Iot Data Processing and Scheduling Based on Deep Reinforcement Learning

Jiang,

Wang,

Jin

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Regenerating codes use the communication cost (repair bandwidth or more generally the tradeoff between the storage cost and communication cost) as the performance metric while locally repairable codes use the number of coded symbols contacted during repair (called locality) as the per-formance metric. While early work considers the symmetric case where the number of coded symbols contacted is a constant, some recent work brings graphical topology into the picture [65,70,8,9] where graphs are used to model the network connectivity (which links or coded symbols can be used for recovery). Note that these graphs, which describe coded symbol repair constraint, are different from ours, which describe source symbol recovery constraint although a similar term -storage codes on graphs is used.…”

Section: Related Work and More Backgroundmentioning

confidence: 99%

Conditional Disclosure of Secrets and Storage over Graphs

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In the era of big data, it is essential to implement practical security and privacy measures to ensure the lawful use of data and provide users with trust and assurance. In the dissertation, I address this issue through several key steps. Firstly, I delve into the problem of conditional secret disclosure, representing it using graphs to determine the most efficient approach for storing and disclosing secrets. Secondly, I extend the conditional disclosure of secrets problem from a single secret to multiple secrets and from a bipartite graph to an arbitrary graph. Thirdly, I remove security constraints to observe how they affect the efficiency of storage and recovery. In our final paper, I explore the secure summation problem, aiming to determine the capacity of total noise. Throughout the dissertation, I leverage information-theoretic tools to address security and privacy concerns.

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