In this paper, we present an innovative approach to passenger flow monitoring for light rail transportation networks. We propose a distributed system based on two main concepts. On each vehicle in the transportation n...
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ISBN:
(纸本)9781643683850
In this paper, we present an innovative approach to passenger flow monitoring for light rail transportation networks. We propose a distributed system based on two main concepts. On each vehicle in the transportation network, a set of sensors is used to count people at a given place. On a cloud-based server, a data synchronization and storage system aggregates the data sent from all vehicles and provides a global view of the transportation network. The contribution, with respect to the state of the art, of our approach is twofold. First, the proposed distributed architecture is able to reduce the system global cost via its flexibility and ease of deployment, since the main part of the system is onboard each vehicle and not fixed at stations or track sections. Second, the novel vision-based passenger counting approach guarantees high levels of reliability in the estimation of the number of people in a given area, and the ability to provide real-time data on the global transportation network. Experimental results demonstrate the validity and the advantages of the proposed approach, paving way to future uses of the system as the base of additional network optimization modules for the global light rail transportation.
Federated learning has been introduced into network intrusion detection to address the data silos among intrusion detection systems in distributed environments. However, existing federated learning-based methods suffe...
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Existing multi-FPGA architectures often leverage high-speed interconnect technologies to achieve higher performance by exploiting ample communication bandwidth. In this paper, we propose an effective mapping approach ...
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The 6th generation (6G) network targets the Internet of Everything (IoE) implementation, and distributed Deep Learning (DDL) can promote this progress with innovative performance in generating intelligence. Meanwhile,...
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The 6th generation (6G) network targets the Internet of Everything (IoE) implementation, and distributed Deep Learning (DDL) can promote this progress with innovative performance in generating intelligence. Meanwhile, the 6G networks support ultra-reliable and low-latency communication (uRLLC) and thus can further elevate the DDL performance to empower the IoE development. However, DDL designs mostly focus on individual areas and yield separate intelligence which is insufficient for the IoE;besides, DDL platforms are usually managed in the centralized fashion, which is vulnerable for data preservation and task execution;the complexity of 6G networks involving heterogeneous devices and relations aggravates issues about reliability and efficiency of DDL. To this end, we propose a novel BC-escorted 6G-based DDL design for trustworthy model training. In this system, the 6G network design is utilized for system-wide uRLLC;non-homogeneous edge devices are grouped up with weighted consideration for DDL to train CNN models;macro base stations (MBSs) and small base stations (SBSs) jointly provide two-tiers parameter aggregation to elevate the knowledge level;a dual -driven BC consensus is designed to verify tasks and models;users anyplace can retrieve models via the BC nodes for object detection. The proposed design is evaluated in comparison with Cloud-based and P2P-based DDLs, and the results demonstrate better performance on accuracy and latency achieved in the proposed system.(c) 2022 Elsevier B.V. All rights reserved.
We study age of information in multi-hop multi-cast cache-enabled networks where the inter-update times on the links are not necessarily exponentially distributed. We focus on the set of non-arithmetic distributions f...
An important invariant of an interconnection network is its surface area, the number of vertices at distance i from a node. Although much work has been done to obtain formulas for the surface areas for many interconne...
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The proceedings contain 7 papers. The special focus in this conference is on distributed Applications and Interoperable systems. The topics include: Transactional Causal Consistent Microservices Simulator;the Impact o...
ISBN:
(纸本)9783031352591
The proceedings contain 7 papers. The special focus in this conference is on distributed Applications and Interoperable systems. The topics include: Transactional Causal Consistent Microservices Simulator;the Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural networks;preface;foreword;runtime Load-Shifting of distributed Controllers Across Networked Devices;edgeEmu - Emulator for Android Edge Devices.
The proceedings contain 9 papers. The topics discussed include: towards sustainable deployment of microservices over the Cloud-IoT continuum, with FREEDA;towards enabling observability of energy demand, with NEST;urge...
ISBN:
(纸本)9798400706417
The proceedings contain 9 papers. The topics discussed include: towards sustainable deployment of microservices over the Cloud-IoT continuum, with FREEDA;towards enabling observability of energy demand, with NEST;urgent edge computing;optimizing resource allocation in the edge: a minimum weighted vertex cover approach;towards energy-aware execution and offloading of serverless functions;reactive autoscaling of Kubernetes nodes;OUTFIT: crowdsourced data feeding noise maps in digital twins;encoding consistency: optimizing self-driving reliability with real-time speed data;and striking trade-off between high performance and energy efficiency in an edge computing application for detecting floating plastic debris.
The management and security of medical health records are of utmost importance in a higher education institution. Traditional centralized methods of managing and preserving medical records are prone to single points o...
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Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled non-linear ordinary differential equations. In many applications, for example, in systems b...
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ISBN:
(纸本)9783031751066;9783031751073
Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled non-linear ordinary differential equations. In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs to steer the system toward a given target. Unfortunately, the resulting optimal control problem is non-linear, therefore, computationally very challenging. We address this issue by introducing an optimality-preserving reduction algorithm for CRNs. The algorithm partitions the original state variables into a reduced set of macro-variables for which one can define a reduced optimal control problem with provably identical optimal values. The reduction algorithm runs with polynomial time complexity in the size of the CRN. We use this result to reduce verification and control problems of large-scale vaccination models over real-world networks.
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