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Blockchain-based crowdsourcing for human intelligence tasks with dual fairness

Blockchain(Research and Applications) 2024年第4期5卷 1-13页

作者： Yihuai Liang Yan Li Byeong-Seok Shin School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

Human intelligence tasks(HITs),such as labeling images for machine learning,are widely utilized for crowdsourcing human *** crowdsourcing platforms face challenges of a single point of failure and a lack of service *** blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust in evaluating the quality of workers’*** propose a blockchain-based crowdsourcing scheme for ensuring dual fairness(i.e.,preventing false reporting and free riding)and improving on-chain efficiency concerning on-chain storage and smart contract *** proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee dual *** efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic *** truth discovery scheme aims at inferring ground truth from workers’*** ground truth is further utilized to estimate the quality of workers’***,a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring *** scheme has O(n)complexity for both on-chain storage and smart contract computation,regardless of the number of questions,where𝑛denotes the number of *** security analysis is provided,and extensive experiments are conducted to evaluate its effectiveness and performance.

关键词： Crowdsourcing Dual fairness Blockchain Human intelligence task Truth discovery

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Black Hole Search in Dynamic Rings: The Scattered Case 27

Black Hole Search in Dynamic Rings: The Scattered Case

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27th International Conference on Principles of Distributed Systems, OPODIS 2023

作者： Di Luna, Giuseppe A. Flocchini, Paola Prencipe, Giuseppe Santoro, Nicola DIAG Sapienza University of Rome Italy School of Electrical Engineering and Computer Science University of Ottawa Canada Department of Computer Science University of Pisa Italy School of Computer Science Carleton University Ottawa Canada

ISBN: (纸本)9783959773089

In this paper we investigate the problem of searching for a black hole in a dynamic graph by a set of scattered agents (i.e., the agents start from arbitrary locations of the graph). The black hole is a node that silently destroys any agent visiting it. This kind of malicious node nicely models network failures such as a crashed host or a virus that erases the visiting agents. The black hole search problem is solved when at least one agent survives, and it has the entire map of the graph with the location of the black hole. We consider the case in which the underlining graph is a dynamic 1-interval connected ring: a ring graph in which at each round at most one edge can be missing. We first show that the problem cannot be solved if the agents can only communicate by using a face-to-face mechanism: this holds for any set of agents of constant size, with respect to the size n of the ring. To circumvent this impossibility we consider agents equipped with movable pebbles that can be left on nodes as a form of communication with other agents. When pebbles are available, three agents can localize the black hole in O(n2) moves. We show that such a number of agents is optimal. We also show that the complexity is tight, that is Ω(n2) moves are required for any algorithm solving the problem with three agents, even with stronger communication mechanisms (e.g., a whiteboard on each node on which agents can write messages of unlimited size). To the best of our knowledge this is the first paper examining the problem of searching a black hole in a dynamic environment with scattered agents. © Giuseppe A. Di Luna, Paola Flocchini, Giuseppe Prencipe, and Nicola Santoro

关键词： Mobile agents

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Priority based k-coverage hole restoration and m-connectivity using whale optimization scheme for underwater wireless sensor networks

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International Journal of Intelligent Networks 2023年第1期4卷 240-252页

作者： Kumari, Sangeeta Mishra, Pavan Kumar Sangaiah, Arun Kumar Anand, Veena School of Computer Science Engineering and Technology Bennett University U.P Greater Noida India India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon India

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate. © 2023 The Authors

关键词： Autonomous underwater vehicles

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UAV-Assisted Internet of Vehicles over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

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IEEE Internet of Things Magazine 2023年第3期6卷 78-84页

作者： Su, Yuhan Liwang, Minghui Chen, Zhong Wang, Xianbin School of Electronic Science and Engineering Xiamen University China School of Informatics Xiamen University China Western University Department of Electrical and Computer Engineering London Canada

Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated. © 2018 IEEE.

关键词： Wi-Fi

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Comparative analysis of navigation algorithms for mobile robot

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3861-3871页

作者： Heng, Han Ghazali, Mohamad Hazwan Mohd Rahiman, Wan School of Electrical and Electronic Engineering Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Daffodil Robotics Lab Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Various applications, including space exploration, transportation, factories, and the military, demand the presence of mobile robots. In those applications, navigation algorithms are essential for enabling mobile robots to operate efficiently and safely in static and dynamic environments. Due to the numerous navigation algorithms available, choosing a suitable and robust one for a specific mobile robot application can be challenging. Therefore, there is a pressing need to conduct a comparative analysis to evaluate the performance and adaptability of different navigation algorithms. This article presents a comparative study of five distinct algorithms in the domain of path planning, which are rapidly random tree (RRT), A*, genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO). The investigation was conducted on three distinct maps, and the strengths and weaknesses of each algorithm are demonstrated. Findings show that increasing the step distance and number of iteration parameters will result in an increase in the planning time. Consequently, the obtained path lengths are also reduced except for the PSO algorithm. Findings show that in the three different environments considered in this paper, the GA exhibits better performance, where a 90% reduction in the planning time is achieved while obtaining the same path length as the A* algorithm, which is the shortest path. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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A Machine Learning Model Predicts Crop-Energy Yields in Agrivoltaics 52

A Machine Learning Model Predicts Crop-Energy Yields in Agri...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Mahmood, Farrukh Ibne Alam, Muhammad Ashraful Butt, Nauman Zafar School of Science and Engineering Lums Department of Electrical Engineering Lahore Pakistan School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9781665464260

Agrivoltaic (AV) systems can simultaneously produce food and energy, providing a much-needed solution to balance the food-energy nexus for the growing global population. In this context, it is crucial to identify optimal design solutions applicable to worldwide locations with reliable food and energy output. Typically, physics-based or mechanistic models are used to predict the food-energy yield for AV. However, these models require significant processing power and time, particularly when applied to multiple locations. Therefore, in this paper, we employ machine learning (ML) to predict crop and photovoltaic (PV) yields for an AV system. Our data is derived from an in-house physics-based model validated with field measurements. We demonstrate how the ML-based model can rapidly (within minutes) and accurately (R2 0.99) predict yields for thousands of locations, a task that would take days using the physics-based model. © 2024 IEEE.

关键词： Agrivoltaics crop yield energy estimation Machine Learning Neural Networks Photovoltaics

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A Speech Enhancement Algorithm Combining Wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines Wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with Wavelet Transform significantly improves noise reduction, outperforming the use of Wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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A Multi-Modal Framework for Fake News Analysis for Detection to Deconstruction 2

A Multi-Modal Framework for Fake News Analysis for Detection...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Parveen, H. Summia School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350371055

The research discusses a multimodal framework for analyzing and detecting fake news and understanding its impact on society. This framework employs diverse strategies, including linguistic analysis, social network monitoring, and visual assessment, to capture various aspects of fabricated information and its dissemination. The first component of the framework focuses on linguistic analysis, examining the language and textual content used in fake news articles to identify misleading information, biased language, and sensationalized headlines. The second component analyzes the role of social networks in spreading fake news, tracking its propagation through platforms like social media and identifying key actors and influencers involved in its dissemination. The third component involves visual assessment, examining the images and videos used in fake news to manipulate public perceptions and emotions, including the detection of doctored images and misleading illustrations. © 2024 IEEE.

关键词： Fake News Detection Machine Learning NLP

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A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity∗ 4

A Host-based Intrusion Detection: Using Signature-based and ...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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FedSTGNN: A Federated Spatio-Temporal Graph Neural Network 15

FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yoon, Heeyong Chon, Kang-Wook Kim, Min-Soo Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Computer Science and Engineering Koreatech Cheonan Korea Republic of School of Computing Kaist Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy. © 2024 IEEE.

关键词： Spatio-temporal data

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