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Towards Efficient and Deposit-Free Blockchain-Based Spatial Crowdsourcing

ACM Transactions on Sensor Networks 2024年第3期20卷 1-22页

作者： Li, Mingzhe Wang, Wei Zhang, Jin Southern University of Science and Technology Guangdong Shenzhen China The Hong Kong University of Science and Technology Hong Kong Hong Kong Agency for Science Technology and Research Singapore Singapore Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Key Lab. of Safety and Secty. for Next Generation of Indust. Internet Shenzhen Guangdong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China

Spatial crowdsourcing leverages the widespread use of mobile devices to outsource tasks to a crowd of users based on their geographical location. Despite its growing popularity, current crowdsourcing systems often suffer from a lack of transparency, centralization, and other security issues. Blockchain technology has revolutionized this sector with its potential for decentralization, security, and transparency. However, existing blockchain-based crowdsourcing systems often overlook efficient task assignment mechanisms and expose users to potential losses due to the obligatory deposit payments to smart contracts, which might be vulnerable or *** article proposes EDF-Crowd, an Efficient and Deposit-Free blockchain-based spatial crowdsoucing framework, to address these challenges. EDF-Crowd introduces an efficient, customizable task assignment mechanism based on smart contracts, operating periodically and batch-wise. We also design a fair compensation mechanism to compensate users for the extra overhead caused by invoking certain smart contracts. More importantly, we propose a series of linkage protocols. By linking users' back-and-forth actions, EDF-Crowd can regulate user behavior without requiring users to deposit. The versatility of EDF-Crowd also allows its application to generic crowdsourcing systems with minimal modifications. We implement EDF-Crowd based on the EOS blockchain. Extensive evaluations show that EDF-Crowd achieves high task assignment efficiency and low cost. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart contract

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Weighted Fuzzy C Means: A Novel Tumor Segmentation Approach in MR Brain Images 2

Weighted Fuzzy C Means: A Novel Tumor Segmentation Approach ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Poshitha, M. Ramaraj, Kottaimalai Dilipkumar, S. Meena, Durga R. Ambika, B. Thilagaraj, M. Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Tamilnadu Madurai India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (纸本)9798350340235

The most prevalent primary brain tumor, glioma, is caused by glial cell carcinogenesis in the central nervous system. For numerous applications in the area of health care evaluation, brain tumor localization and separation from magnetic resonance images (MRI) are challenging yet crucial tasks. Several recently developed methods utilized four modalities: T1, T1c, T2, and FLAIR. This is because each brain imaging modality provides distinct and important information concerning every region of the tumor. The process of diagnosis, therapy selection, and risk variables detection depends on trustworthy and precise tumor segmentation and survival of patients forecasting. In this article, a state-of-the-art fuzzy-based system is introduced that uses multimodal MRI images to categorize brain tumors and estimate glioma survival. To address the drawbacks of the FCM, the suggested approach combined the weight function with the conventional Fuzzy C-means (FCM). Extensive tests are carried out on the different BRATS challenge datasets, demonstrating that the suggested approach achieves competitive outcomes. Evaluation on the BraTS dataset confirms the effectiveness of the developed Weighted FCM (WFCM), and the segmented results are compared with the ground truth images. A small number of performance metrics were also used for assessing the qualitative as well as quantitative outcomes. The resulting dissected images help medical professionals diagnose, medicate, or plan for medical intervention for the affected individuals earlier. © 2023 IEEE.

关键词： Brain tumor Brain Tumor Segmentation (BraTS) dataset Glioma Magnetic Resonance Imaging (MRI) Segmentation Weighted Fuzzy C-means (WFCM)

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Smart LPG Monitoring System: Enhanced Safety with Leakage Detection 2

Smart LPG Monitoring System: Enhanced Safety with Leakage De...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Hotur, Varsha P. Prianka, R.R. Lynsha Helena Pratheeba, H.P. Padmavathi, M. Ramaraj, Kottaimalai Thilagaraj, M. MVJ College of Engineering Department of Electronics and Communication Engineering Bengaluru India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India MVJ College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367461

Gas leaks can lead to significant accidents causing both material damage and harm to individuals. Two significant factors that contribute to gas leaks are inadequate upkeep of systems and inadequate public awareness. Therefore, the detection of Liquefied Petroleum Gas (LPG) leaks is crucial in order to prevent accidents and safeguard human lives. LPG cylinders are so important to our everyday existence, making it essential for users to keep track of their gas levels to manage consumption effectively. Monitoring usage not only helps users stay informed about their LPG levels but also ensures timely booking of a new cylinder when needed. Given the high flammability of LPG, gas leakage poses serious risks, leading to accidents with potential loss of lives and property damage. To address these concerns, this project introduces a comprehensive LPG level monitoring system with leakage detection. These systems often utilize advanced sensors that can quickly and accurately identify the release of LPG. Additionally, smart systems may integrate with other technologies, such as communication modules, to alert users or authorities in real-time when leakage of gas is identified. Employing a load cell, the equipment constantly monitors the gas content of the cylinder and uses a GSM module to notify the consumer when the replacement needs to be scheduled. Upon receiving confirmation, the device takes appropriate action. Additionally, the system features gas sensors to detect LPG leakage, notifying the user via a GSM module, while activating alarms and exhaust mechanisms for added safety. © 2024 IEEE.

关键词： Liquefied petroleum gas

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Two-Stage Resource Scheduling for Deterministic Communication and Computation Integration

Two-Stage Resource Scheduling for Deterministic Communicatio...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Weiting Tang, Nian Zhang, Chuan Guo, Ruibin Li, Mingyan Ying, Chenhao Jin, Jian Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Shanghai Jiao Tong University Department of Computer Science Shanghai200030 China Caict Research Institute of Industrial Internet of Things Beijing100095 China

ISBN: (纸本)9798350351255

In this paper, we investigate a resource orchestration and transmission scheduling problem for data-intensive services with diversified service requirements. A three-layer collaborative architecture is presented to support dynamic networking and computing resource allocation. To obtain optimal orchestration and scheduling policies, we formulate a constrained resource scheduling problem with the objective to maximizing resource utilization and scheduling success ratio. Since the complicated coupled constraints among decisions, we decouple the problem into a two-stage sub-problems of resource orchestration and transmission scheduling. To realize cross-domain resource orchestration and deterministic transmission of large-scale computing tasks, a two-stage resource scheduling scheme is proposed. Specifically, the first stage makes the resource orchestration decision by a greedy algorithm, and the second stage makes the transmission scheduling decision based on a deep reinforcement learning algorithm. Simulation results show that the proposed solution can effectively improve resource utilization and scheduling success ratio while satisfying diversified service requirements, as compared with benchmarks. © 2024 IEEE.

关键词： Deep reinforcement learning

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Energy-saving Potential Assessment of Building Energy System Considering Climate Impacts

Energy-saving Potential Assessment of Building Energy System...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Zhang, Zhenwei Hui, Hongxun State Key Laboratory of Internet of Things for Smart City University of Macau Macao 999078 China Department of Electrical and Computer Engineering University of Macau Macao 999078 China Zhuhai UM Science Technology Research Institute Zhuhai 519031 China

ISBN: (纸本)9798350322699

Photovoltaic/thermal integrated system (PV/T) and virtual energy storage system (VESS) are innovative and energy-saving techniques for multi-energy demand, providing building energy system (BES) with adjustable potential. However, duo to inherent deviations in climate conditions, building demands and equipment performance, lack of confidence in energy-saving potential has been a tricky problem. To copy with that, an energy-saving potential assessment model considering climate conditions and user satisfaction is proposed in this paper. First, a typical BES model coupling PV/T and VESS is established. Meanwhile, we develop numerical relationships between varying climate conditions (solar radiation, temperature) and PV/T conversion efficiency, coefficient of performance (COP) for air conditioners, and storage capacity of VESS, respectively. Then, an optimal dispatch model with minimize electricity consumption is applied to assess the energy-saving potential, taking into account the user satisfaction. Simulation results on building block in University of Macau demonstrate the effectiveness of the proposed method and verify that 76% energy-saving potential can be achieved. © 2023 IEEE.

关键词： building energy system climate conditions Photovoltaic/thermal integrated system virtual energy storage system

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors 7

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Computing on Surface: A Multi-Task Multi-Access Offloading Scheme in Maritime Edge Networks

Computing on Surface: A Multi-Task Multi-Access Offloading S...

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2024 International Conference on Cloud and Network Computing, ICCNC 2024

作者： Dai, Minghui Dou, Chenglong Wu, Yuan Qian, Liping Lin, Bin Su, Zhou Shen, Xuemin Sherman University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China Zhejiang University of Technology College of Information Engineering Hangzhou China Dalian Maritime University Department of Communication Engineering Dalian China Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350367805

The recent development of unmanned aerial vehicles (UAVs) technology has been envisioned as a promising paradigm to cater for the growing maritime activities. However, the increasing growth of marine services poses challenges for processing maritime data. In this paper, we propose a surface computing paradigm in maritime networks, in which multi-Task sensed by UAVs can be offloaded to multiple beacon stations. Multiple UAVs can process their workloads locally or offload to surface computing. Taking the system welfare and energy consumption into consideration, we present an optimization problem to determine the selection of beacon stations and the offloading decision, with the objective of maximizing the system welfare. We propose a hybrid auction and convex optimization approach to address the formulated problem. Finally, simulation results demonstrate the effectiveness of our proposed algorithms compared to several baselines. © 2024 IEEE.

关键词： Convex optimization

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Traffic Characterization for Efficient TWT Scheduling in 802.11ax IoT Networks

Traffic Characterization for Efficient TWT Scheduling in 802...

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IEEE Conference on Wireless Communications and Networking

作者： Jaykumar Sheth Vikram K. Ramanna Behnam Dezfouli Department of Computer Science and Engineering Internet of Things Research Lab Santa Clara University USA Infineon Technologies San Jose USA

To reduce packet collisions and enhance the energy efficiency of stations, Target Wake Time (TWT), which is a feature of the 802.11ax standard (WiFi 6), allows the allocation of communication service periods to stations. While effective TWT allocation requires characterizing the traffic pattern of stations, in this paper, we empirically study and reveal that the existing methods (i.e., channel utilization estimation, packet sniffing, and buffer status report) do not provide adequate accuracy. To remedy this problem, we propose a traffic characterization method that can accurately capture inter-packet and inter-burst intervals on a per-flow basis in the presence of factors such as channel access and packet preparation delay. We empirically evaluate the proposed method and confirm its superior traffic characterization performance against the existing ones. We also present a sample TWT allocation scenario that leverages the proposed method to enhance throughput.

关键词： Time-frequency analysis IEEE 802.11ax Standard Traffic control Throughput Energy efficiency Delays Resource management

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Device-to-Device Communications aided Integrated Sensing and Communication Networks: A Joint Design of Bandwidth and Power Allocations

Device-to-Device Communications aided Integrated Sensing and...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Dou, Chenglong Huang, Ning Wu, Yuan Qian, Liping Quek, Tony Q.S. University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519301 China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes. © 2024 IEEE.

关键词： Bandwidth

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RAPIDEST: A Framework for Obstructive Sleep Apnea Detection

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2023年 31卷 387-397页

作者： Lin, Xin-Xue Lin, Phone Yeh, En-Hau Liu, Gi-Ren Lien, Wan-Ching Fang, Yuguang National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Graduate Institute of Networking and Multimedia Institute of Medical Device and Imaging National Taiwan University Internet of Things Research Center Department of Computer Science and Information Engineering Taipei10617 Taiwan National Cheng Kung University Department of Mathematics Tainan City701 Taiwan National Taiwan University Hospital Department of Emergency Medicine Taipei100229 Taiwan College of Medicine National Taiwan University Department of Emergency Medicine Taipei10617 Taiwan City University of HongKong Department of Computer Science HongKong Hong Kong

The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages. More importantly, under this framework, we only need EEG signals, thus significantly simplifying the signal collection process and reducing the complexity of the severity determination of apnea and hypopnea. We have conducted extensive experiments to verify the relationship between the rarity score and AHI and demonstrate the effectiveness of our proposed approach. © 2001-2011 IEEE.

关键词： Electrocardiograms

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