2021 IEEE Globecom Workshops (GC Wkshps)最新文献

FAIR: A Blockchain-based Vaccine Distribution Scheme for Pandemics FAIR:基于区块链的流行病疫苗分配方案

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682114

Anuja R. Nair, Rajesh Gupta, S. Tanwar

{"title":"FAIR: A Blockchain-based Vaccine Distribution Scheme for Pandemics","authors":"Anuja R. Nair, Rajesh Gupta, S. Tanwar","doi":"10.1109/GCWkshps52748.2021.9682114","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682114","url":null,"abstract":"Demand forecasting, supply acquisition in healthcare supply chains are significant concerns spanning various organizations and bodies, rendering a crucial backbone to medical services necessary for everyday living. A global pandemic resulting in critical demand for medications and vaccines was an eye-opener in the current era. The intrinsic complexity among the bodies involved in the supply chain results in a lack of data transparency, security, privacy, and reliable communication. The counterfeited drug is an outcome of such limitations that adversely affects a global population. Consecutively, fair allocation and distribution of drugs and vaccines to administer them to a global mass equally is also a significant concern. Blockchain as technology grants an essential platform to track and manage transactions among communicating parties in the supply chain using a peer-to-peer, secured, distributed ledger, removing the need for intermediaries or entrusted third parties. Most existing studies focus on tracking and tracing supply chain systems in a centralized manner, leading to transparency, authenticity, data privacy, and authenticity concerns in healthcare supply chains. In this article, we propose a FAIR blockchain-based approach deploying smart contracts leading to transparent traceability of data and transactions in the healthcare supply chain between the communicating parties We propose an approach that allows fair allocation and distribution of vaccines as per the demand generated from the global population. We present a system architecture and algorithm representing the communication between parties that governs our proposed approach. We have computed network performance based and blockchain based evaluation of the proposed system. We have calculated the communication and computation cost of 1152 bits and 12.6 ms respectively.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75211780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multi-Dimensional Contract Design for Blockchain Deployment in WSN under Information Asymmetry 信息不对称下WSN区块链部署的多维契约设计

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682004

Weiyi Wang, Yutao Jiao, Jin Chen, Gui Fang, Yuhua Xu, Yang Zhang

引用次数: 1

Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment 使用WiFi信号进行坐式计数的集成学习:性能研究和可转移性评估

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682014

J. Bernaola, I. Sobrón, J. Ser, I. Landa, I. Eizmendi, M. Vélez

{"title":"Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment","authors":"J. Bernaola, I. Sobrón, J. Ser, I. Landa, I. Eizmendi, M. Vélez","doi":"10.1109/GCWkshps52748.2021.9682014","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682014","url":null,"abstract":"The detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could improve the energy management of a building. Due to human presence or movement over a particular area, the analysis of variations in wireless signal properties of already deployed wireless technology such as WiFi systems provides the information needed for Machine Learning models to accomplish the non-intrusive (device-free) detection and classification of different human activities. In this context, this work focuses on detecting seated people in an indoor scenario by using ensemble learning, a particular branch of Machine Learning models for supervised learning that hinges on combining the outputs of individual predictors. Furthermore, we evaluate the transferability of the knowledge modeled by ensemble learners. When trained in a particular frequency or channel, such models are used to classify data captured over another different frequency. Our experimental setup and discussed results reveal that while ensembles attain satisfactory levels of predictive accuracy predictions, their knowledge cannot be transferred among different frequencies. This conclusion opens an exciting future towards new means to perform effective knowledge transfer over the frequency domain.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"12 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75899242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance 远程监视中多无人机导航的高效编队控制机制

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682094

G. Raja, Yashvandh Baskar, P. Dhanasekaran, R. Nawaz, Keping Yu

{"title":"An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance","authors":"G. Raja, Yashvandh Baskar, P. Dhanasekaran, R. Nawaz, Keping Yu","doi":"10.1109/GCWkshps52748.2021.9682094","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682094","url":null,"abstract":"Multiple Unmanned Aerial Vehicles (UAVs) have a greater potential to be widely used in civil and military applications. Swarm of UAVs can be deployed in a multitude of 24/7 security and surveillance. The network management and pattern formation are crucial for multi-UAV formation control mechanisms while cautiously navigating the surveillance areas. A Deep Reinforcement Learning (DRL) based Formation Flight Control for Navigation (FFCN) is used to efficiently build the UAV swarm, which decreases networking load by minimizing communication and processing involved in pattern formation. Moreover, through the leader-follower navigation, the network management of the swarm is substantially simplified. The leader-follower approach in FFCN is efficient for multi-UAV as the navigation system needs to find only the leader's trajectory. However, the failure of the leader due to actuator faults decreases the efficiency of the system. The proposed FFCN addresses the above by including a fault-tolerance mechanism, thus improving the system's reliability. Simulation results show that the FFCN model achieves faster convergence in less time with a lower collision rate. The model's usage reduced the collision rate to 3.4% in successful formation without colliding with other UAVs.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81847166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Improved physical-layer security for OFDM using data-based subcarrier scrambling 利用基于数据的子载波置乱提高OFDM的物理层安全性

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682170

M. Banat, J. Bas, A. Dowhuszko

{"title":"Improved physical-layer security for OFDM using data-based subcarrier scrambling","authors":"M. Banat, J. Bas, A. Dowhuszko","doi":"10.1109/GCWkshps52748.2021.9682170","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682170","url":null,"abstract":"This paper presents a novel physical-layer security approach to protect the information exchanged in a wireless communication system based on OFDM. In this method, QAM symbols that are fed into the IFFT block are split into two subsets. The first subset of symbols is placed on non-scrambled (indexing) subcarriers, whereas the remaining symbols are transmitted on scrambled (data) subcarriers. Based on the bits placed on the indexing subcarriers, a permutation matrix that defines the (data-based) scrambling sequence of the data subcarriers is determined using an algorithm that is known a priori between the transmitter and receiver. The mapping between indexing bits and scrambling sequences is designed to minimize error propagation when there are erroneous received indexing bits (i.e. Gray-mapped sequences). Closed form formulas that approximate the Bit Error Probability (BEP) of the baseline (non-scrambled) and proposed (scrambled) OFDM transmissions are determined for different link configurations. The impact of the proposed physical-layer security scheme on the BEP is minimal, while increasing notably the number of combinations that an eavesdropper must check in order to execute a brute-force search attack.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"19 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81856733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Doppler Effect Mitigation using Reconfigurable Intelligent Surfaces with Hardware Impairments 使用硬件受损的可重构智能表面减轻多普勒效应

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681939

Ke Wang, C. Lam, B. Ng

引用次数: 10

Diagnostic Decision Support for Medical Imaging and COVID-19 Image Classification on ARM Mali GPU 基于ARM Mali GPU的医学成像诊断决策支持及COVID-19图像分类

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682104

S. Shreyas, J. Rao

{"title":"Diagnostic Decision Support for Medical Imaging and COVID-19 Image Classification on ARM Mali GPU","authors":"S. Shreyas, J. Rao","doi":"10.1109/GCWkshps52748.2021.9682104","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682104","url":null,"abstract":"The abrupt rise in Coronavirus cases has led to shortage of rapid and highly sensitive reverse transcriptase polymerase chain reaction (RT-PCR) testing kits for the diagnosis of coronavirus disease 2019 (COVID-19). Radiologists have found X-ray images could be useful for diagnosis of COVID. In this work, Diagnostic Decision Support for Medical Imaging (DDSM)++ is introduced to detect the different abnormal conditions in lung including COVID. The scarcity of COVID dataset is handled by using various spatial transform augmentation techniques, such as power law transformation, Gaussian blur, and sharpening. Also, to get the benefit of inference accelerators, an android mobile application is developed which is quantized and optimized for ARM Mali GPU. The DDSM++ model is an extended version of DDSM model (inspired from Densenet-121), and the X-ray images are preprocessed with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the quality of X-ray images. The COVID X-ray images are obtained from the open source and the proposed method has obtained almost 98.47% accuracy for COVID detection. Further, the model is quantized to FP-16 using TFLITE and is utilized to benchmark the inference acceleration on Edge devices with ARM Mali GPU. About 30% and 80% reduction in inference time was observed for FP-32 and FP-16 models when run on ARM Mali GPU. Post quantization, about 5% drop in accuracy is observed for COVID detection.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85540565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Distributed Signal Strength Prediction using Satellite Map empowered by Deep Vision Transformer 基于深度视觉变压器的卫星地图分布式信号强度预测

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682021

Haiyao Yu, Zhanwei Hou, Yifan Gu, Peng Cheng, Wanli Ouyang, Yonghui Li, B. Vucetic

{"title":"Distributed Signal Strength Prediction using Satellite Map empowered by Deep Vision Transformer","authors":"Haiyao Yu, Zhanwei Hou, Yifan Gu, Peng Cheng, Wanli Ouyang, Yonghui Li, B. Vucetic","doi":"10.1109/GCWkshps52748.2021.9682021","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9682021","url":null,"abstract":"The accurate prediction of received signal strength (RSS) is the key to coverage optimization and interference management in network planning, as well as proactive resource allocation and anticipated network management. Traditional methods for RSS prediction are based on ray tracing or stochastic radio propagation model. The former requires the detailed 3D geometry and dielectric properties of the reflectors, which may not be available practically. The latter roughly classify the environment as either urban, suburban and rural scenarios and does not make full use of the environment information. In this paper, by leveraging accessible satellite maps to capture the features of radio environment, a distributed federated learning (FL) RSS prediction framework is proposed to fully exploit the user generated real-time data while preserving the users’ privacy. To further improve the prediction accuracy, the deep vision transformer (DeepVIT) is utilized to process the images of the satellite map, because it is capable of learning to \"pay attention to\" important parts of an image such as reflection surfaces and blockages. The proposed method is evaluated by the real-world data set including around 60, 000 individual measurements. Simulations results verified that the prediction accuracy of the proposed method outperforms baseline methods including ray tracing, Urban Macro (UMa) model and convolutional neural network (CNN) based method. Moreover, the computational time is reduced five times compared with CNN based method.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"405 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76623587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Blockchain-Enabled Parallel Learning in Industrial Edge-Cloud Network: a Fuzzy DPoSt-PBFT Approach 基于区块链的工业边缘云网络并行学习:一种模糊DPoSt-PBFT方法

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9681977

Fan Yang, Jianwei Tian, Tao Feng, Fangmin Xu, Chao Qiu, Chenglin Zhao

{"title":"Blockchain-Enabled Parallel Learning in Industrial Edge-Cloud Network: a Fuzzy DPoSt-PBFT Approach","authors":"Fan Yang, Jianwei Tian, Tao Feng, Fangmin Xu, Chao Qiu, Chenglin Zhao","doi":"10.1109/GCWkshps52748.2021.9681977","DOIUrl":"https://doi.org/10.1109/GCWkshps52748.2021.9681977","url":null,"abstract":"Recently, parallel reinforcement learning (PRL) based Industrial Internet of Things (IIoT) edge-cloud resource scheduling has elicited escalating attention. However, with the scale of IIoT expands, there are several challenges in the existing researches: 1) large number of parallel servers slows down the convergence rate of PRL; 2) malicious parallel server affects resource allocation efficiency. In order to solve the above efficiency and security problem, blockchain-based approaches are introduced in PRL based resource allocation problem. However, traditional consensus algorithm in blockchain is not suitable for resource allocation and is inefficient. Thus, in this article, based on a novel fuzzy delegated proof of state and practical byzantine fault tolerance (fuzzy DPoSt+PBFT) consensus algorithm, we propose a blockchain-enabled collaborative parallel Q-learning (CPQL) approach to address the above challenges. To be specific, we first construct an edge-cloud collaborative architecture for executing the diversity intelligence IIoT applications. Then, we propose a CPQL algorithm for edge-cloud resource allocation and choosing the optimal number of parallel edge servers to speed up the Q-table training. In the Q-table aggregation process in CPQL, a fuzzy DPoSt+PBFT algorithm is designed for secure CPQL training and efficient consensus. Experimental results show the superior performance of the proposed approach. And the proposed approach has great potential in IIoT resource allocation problem.","PeriodicalId":6802,"journal":{"name":"2021 IEEE Globecom Workshops (GC Wkshps)","volume":"93 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77208600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Phase Shift Design for Intelligent Reflecting Surface Aided mmWave MIMO Systems 智能反射表面辅助毫米波MIMO系统的相移设计

2021 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2021-12-01 DOI: 10.1109/GCWkshps52748.2021.9682115

Sung Hyuck Hong, Junil Choi

引用次数: 1