The proceedings contain 68 papers. The topics discussed include: efficient algorithms for obnoxious facility location on a line segment or circle;vehicular edge computing-driven optimized multihop clustering with data...
ISBN:
(纸本)9798350313062
The proceedings contain 68 papers. The topics discussed include: efficient algorithms for obnoxious facility location on a line segment or circle;vehicular edge computing-driven optimized multihop clustering with data aggregation;cloud telescope: a distributed architecture for capturing internet background radiation;a multi-stakeholder cloud-continuum framework for 6G networks security & service management;unveiling equity: exploring feature dependency using complex-valued neural networks and attention mechanism for fair data analysis;RoMA: resilient multi-agent reinforcement learning with dynamic participating agents;attribute-based searchable proxy re-encryption blockchain data sharing scheme;and a sensor predictive model for power consumption using machine learning.
Social sensing is emerging as an effective and pervasive sensing paradigm to collect timely data and observations from human sensors. This paper focuses on the problem of COVID-19 misinformation detection on social me...
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ISBN:
(纸本)9781665439299
Social sensing is emerging as an effective and pervasive sensing paradigm to collect timely data and observations from human sensors. This paper focuses on the problem of COVID-19 misinformation detection on social media. Our work is motivated by the lack of COVID-specific knowledge in current misinformation detection solutions, which is critical to assess the truthfulness of social media claims about the emerging COVID19 disease. In this paper, we leverage human intelligence on a crowdsourcing platform to obtain essential knowledge facts for detecting the COVID-19 misinformation on social media. Two critical challenges exist in solving our problem: i) how to efficiently acquire accurate and timely knowledge that is both inclusive and specific to COVID-19? ii) How to effectively coordinate the efforts from both expert and non-expert workers to detect COVID-19 misinformation? To address these challenges, we develop FakeSens, a social sensing based crowd knowledge graph approach that explicitly explores the knowledge facts specific to COVID-19 and models the reliability of different types of crowd workers to capture the misleading COVID-19 claims Evaluation results on a real-world dataset show that FakeSens significantly outperforms state-of-the-art baselines in accurately detecting misleading claims of COVID-19 on social media.
The TCP Westwood congestion control algorithm was designed to improve data transfer efficiency in LTE networks. It can be applied to optimize data transmission in structural health monitoring topologies using Wireless...
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A low-cost peripheral system refers to a set of devices or components that are affordable and cost-effective. These peripheral systems are typically used to complement or enhance the functionality of a main system or ...
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The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource effici...
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With the development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) has emerged as a novel network architecture in today's Internet era. It c...
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ISBN:
(纸本)9798350386288;9798350386271
With the development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) has emerged as a novel network architecture in today's Internet era. It can separate the control plane from the data plane, allowing rapid packet forwarding in the Internet through a centralized controller. However, SDN environments are vulnerable to traditional distributed Denial of Service (DDoS) attacks. This paper proposes a new dual layer strategy to try to mitigate the question. First, by using blockchain technology and smart contract in the northbound interface to store the flow tables required for SDN networks, security is increased. Then, we use the Token Bucket algorithm and Time Window algorithm to build the first-tier strategy to defend against obvious DDoS attacks. To detect unobvious DDoS attacks, we design the second-tier strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm to perform binary classification on data, thereby identifying abnormal traffic. We use the currently publicly available DDoS dataset CIC-DDoS2019 for experimental verification. The results show that using this strategy in SDN networks results in an average deviation of data Round-Rip Time (RTT) approximately 38.86% lower than in the original SDN networks without this strategy. Additionally, the accuracy of DDoS attack identification reaches 91.29%. This means that with the implementation of this strategy, DDoS attacks can be effectively identified without compromising the stability of data transmission in SDN network environments.
Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual averag...
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This article explores how IoT device systems handle multimedia data and presents a secure multimedia framework utilizing blockchain technology. The proposed framework high- lights how blockchain ensures the safety and...
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Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. The scene understanding can further be improved by including obje...
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ISBN:
(纸本)9781665472609
Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. The scene understanding can further be improved by including object-level information in the map. In this work, we extend a multi-view 3D semantic mapping system consisting of a network of distributed smart edge sensors with object-level information, to enable downstream tasks that need object-level input. Objects are represented in the map via their 3D mesh model or as an object-centric volumetric sub-map that can model arbitrary object geometry when no detailed 3D model is available. We propose a keypoint-based approach to estimate object poses via PnP and refinement via ICP alignment of the 3D object model with the observed point cloud segments. Object instances are tracked to integrate observations over time and to be robust against temporary occlusions. Our method is evaluated on the public Behave dataset where it shows pose estimation accuracy within a few centimeters and in real-world experiments with the sensor network in a challenging lab environment where multiple chairs and a table are tracked through the scene online, in real time even under high occlusions.
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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