Intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. They are therefore expected not just to consume ITS services, but also to actively contribute t...
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ISBN:
(数字)9781665468800
ISBN:
(纸本)9781665468800
Intelligent vehicles are quickly becoming mobile, powerful computers, able to collect, exchange, and process sensed data. They are therefore expected not just to consume ITS services, but also to actively contribute to the implementation of relevant ITS applications. With an increasing role of machine learning (ML) approaches, vehicles are called to put into use their computing capabilities and sensed data for the training of ML models. This can be enacted through distributed learning approaches, which however may lead to significant communication overhead or to learners converging to different models. In this work, we envision a new distributed learning scheme, named EAGLE, that, with the assistance of the network edge, aims at exploiting the vehicles' data and computing capabilities, while enabling an efficient learning process. To this end, EAGLE combines the advantages of two existing schemes, namely, federated learning and gossiping learning, yielding a distributed paradigm that ensures both scalability and model consistency. Our results, obtained using two different real-world data sets, show that EAGLE can improve learning accuracy by 20%, while reducing the communication overhead by 45%.
Cloud-edge orchestration manages a large pool of heterogeneous resources to improve latency and bandwidth for real-time use cases. It dynamically places computational resources and applications, usually microservices,...
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ISBN:
(纸本)9798350322392
Cloud-edge orchestration manages a large pool of heterogeneous resources to improve latency and bandwidth for real-time use cases. It dynamically places computational resources and applications, usually microservices, at cloud and edge locations to enable low-latency communication. For this complex task, various optimization techniques have emerged over recent years. However, the majority implement custom and prototypical infrastructures for evaluation. This limits the general applicability, reusability, and comparability. Therefore, our work wants to contribute a universal and lightweight cloud-edge orchestration platform that fully decouples the optimization logic from the infrastructure. We analyzed the state-of-the-art cloud-edge orchestration and designed a generic cloud-edge orchestration platform. Besides the scientific perspective, we aligned our proposal to the OpenFog Reference Architecture to consider a perspective also from the industry. Therefore, our conceptually designed platform is based on the perspective of both academia and industry. We conclude that our platform can foster advanced cloud-edge orchestration techniques' general applicability, reusability, and comparability.
Opportunistic Underwater sensor Network (OUSN) comprised of Unmanned Underwater Vehicles (UUV) has been deployed for the surveillance of various underwater events, and in some military environments an OUSN could be in...
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ISBN:
(纸本)9781665473156
Opportunistic Underwater sensor Network (OUSN) comprised of Unmanned Underwater Vehicles (UUV) has been deployed for the surveillance of various underwater events, and in some military environments an OUSN could be invaded by some underwater spy-robots termed eavesdroppers. Typically, the eavesdroppers move around OUSN nodes and eavesdrop on their communication channels silently. Thus, the eavesdroppers are hard to be perceived by the nodes, which implies that the nodes are blind to the eavesdroppers. The nodes probably disseminate the held data messages while they are blithely unaware of the adjacent eavesdroppers, and the eavesdroppers could capture these data messages. To reduce the theft ratio of data messages and guarantee the required delivery ratio in a storage-limited OUSN, a propagation model of data messages is formulated to analyse the proportion variation of message holders. Based on this model, the reduction of theft ratio is specially investigated to obtain the appropriate disseminating probabilities, storing probabilities, and discarding probabilities of the nodes with different in-degrees. In the proposed Blind Message Dissemination Method (BMDM), the data messages are disseminated, stored, and discarded according to these probabilities. Simulation results demonstrate that BMDM can reduce the theft ratio and guarantee the required delivery ratio in a storage-limited OUSN effectively.
The rapid deployment of IoT networks in different industrial services has caused the emanation of a huge volume of data from sensors and monitors. The efficient analysis and compact representation of the big data gene...
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Due to its capacity to manage big datasets and enable real-time decision-making across a variety of areas, online real-time distributed machine learning has attracted considerable attention in recent years. This resea...
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This paper introduces a framework for an indoor autonomous mobility system that can perform patient transfers and materials handling. Unlike traditional systems that rely on onboard perception sensors, the proposed ap...
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Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time members...
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The development of sophisticated monitoring systems that can do thorough and real-time assessments has been spurred by growing worries about the quality of water. In this study, we suggest a unique method for dynamica...
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Integrated systems that combine wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are emerging as state-of-the-art solutions for large-scale remote sensing. In order to achieve an energy-efficient pa...
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ISBN:
(纸本)9783031488023;9783031488030
Integrated systems that combine wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are emerging as state-of-the-art solutions for large-scale remote sensing. In order to achieve an energy-efficient path plan, this paper highlights the importance of considering parameters from sensor nodes beyond just UAV travel distance. For example, residual battery and buffer size of sensor nodes are equally important in enhancing data collection, reducing energy consumption, minimising data loss and extending the lifetime of WSNs. The paper presents an extract from a proposed algorithm that demonstrates how to generate UAV path plans based on the dynamic resources of the WSN. This algorithm harnesses parallelism by dividing WSN into clusters. The path plans only include a subset of sensors that interact with the UAV, serving as waypoints in the traversal process. The cost of each path plan is assessed by our proposed system model, which considers the energy-consuming actions the sensors can perform. Graph theory is used to map the problem of UAV path plan generation to the Travelling Salesman Problem (TSP).
Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential t...
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ISBN:
(纸本)9781665439299
Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential to greatly expand the range and scope of vibration monitoring applications by delivering long-life wireless sensors that can be cost-effectively embedded in hard to reach places such as;within machines, infrastructure or the built environment. However, achieving this vision is difficult due to the stringent resource constraints of contemporary IoT devices and networks. This has led the research community to develop a creative range of application-specific near-sensor processing firmware. However, systematic support for generic vibration monitoring on resource-poor IoT networks remains an open problem. We tackle this challenge by introducing ReFrAEN, a software framework that efficiently enables a wide range of vibration monitoring applications on IoT networks. ReFrAEN achieves this through a deeply configurable combination of compression techniques and data processing algorithms. These features allow end-users to effectively trade-off between resource consumption and data resolution in order to meet battery life constraints while preserving sufficient data quality to support the target application. Our evaluation shows that ReFrAEN is capable of identifying bearing faults, while dramatically improving battery lifetime and reducing latency in comparison to prior approaches.
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