To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve ti...
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ISBN:
(纸本)9798350387964;9798350387957
To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.
A hierarchical approximate dynamic programming (ADP) strategy is presented to determine intra-day operations of distributed energy storage cluster for demand management and frequency response service. According to the...
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The integration of the blockchain technology into electronic voting systems is an innovation that has gained popularity due to the issues prevailing with the conventional voting methods. The purpose of the work is to ...
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For future networks, communications and computing will converge to provide services;Federated Learning (FL), as one of the typical distributedcomputing technologies, needs to be integrated with networking. For such i...
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ISBN:
(纸本)9781538674628
For future networks, communications and computing will converge to provide services;Federated Learning (FL), as one of the typical distributedcomputing technologies, needs to be integrated with networking. For such integration, FL suffers from the straggler effect that the entire learning speed can be lowered down, because of the existence of the devices taking more time to complete their tasks. There are many existing works targeting at reducing straggler effects;However, they lacks the detailed investigations on the reasons and the impact of each cause when integrating FL with networking. To carefully investigate those aspects, we classify the reasons of such effects into 3 categories, computing power, communication capability and data distributions, and conduct the extensive experiments with carefully designs. After investigations, it is observed that learning completion time cannot be estimated by formulation with FLoating-point Operations Per second (FLOPs) if the device's computing capability is low. Also the communication time can be reduced by intentionally selecting appropriate devices when the computing powers of devices are heterogeneous, and the model parameters can be discarded if the device holds independent and identically distributed (i.i.d.) dataset.
The tremendous increase in implementing IoT devices for critical infrastructures yells for higher performance and robust protection. Traditional cloud based IoT architecture is unsuitable for delay-sensitive or real-t...
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With the increasing integration of distributed energy resources (DERs) into power grids, the security of distributed power dispatching control systems becomes a critical concern. This paper presents a comprehensive se...
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Real-time embedded systems filed tend to deploy functions of different critical levels on a unified platform for reasons related to SWaP (size, weight and power) and cost considerations. Highly critical tasks represen...
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To protect sensitive data in cloud outsourcing smart systems, we implemented a private function evaluation system with fully homomorphic encryption (FHE) and trusted execution environment (TEE) using lookup tables (LU...
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ISBN:
(纸本)9798350349955;9798350349948
To protect sensitive data in cloud outsourcing smart systems, we implemented a private function evaluation system with fully homomorphic encryption (FHE) and trusted execution environment (TEE) using lookup tables (LUT), which preserves the privacy of data in various smart systems, including anomaly detection in smart grids. The proposed system introduces a trusted authority (TA) to manage FHE keys and generate private information retrieval (PIK) queries to extract the output from the LUT. The remaining problem in our previous work is that the TA knows the index distribution of data points in LUTs and the index of matched data points, which may cause security issues. In this work, we solve the security issues by executing operations in the TA with the TEE and reducing the runtime using unencrypted LUTs. Our system allows the computation of arbitrary multi-input functions to improve efficiency and expand the scope of applications with FHE in smart systems. We confirmed the evaluation runtime of an 18-bit one-input function needs 2.49 s. Compared to our previous work, our system achieved 2.1x speed up for an 8-bit three-input function with one thread.
Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applie...
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ISBN:
(纸本)9781665473156
Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.
The proliferation of edge computing brings new challenges due to the complexity of decentralized edge networks. Software-defined networking (SDN) takes advantage of programmability and flexibility in handling complica...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
The proliferation of edge computing brings new challenges due to the complexity of decentralized edge networks. Software-defined networking (SDN) takes advantage of programmability and flexibility in handling complicated networks. However, it remains a problem of designing a both trusted and scalable SDN control plane, which is the core component of the SDN architecture for edge computing. In this paper, we propose Curb, a novel group-based SDN control plane that seamlessly integrates blockchain and BFT consensus to ensure byzantine fault tolerance, verifiability, traceability, and scalability within one framework. Curb supports trusted flow rule updates and adaptive controller reassignment. Importantly, we leverage a group-based control plane to realize a scalable network where the message complexity of each round is upper bounded by O(N), where N is the number of controllers, to reduce overheads caused by blockchain consensus. Finally, we conduct extensive simulations on the classical Internet2 network to validate our design.
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