Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996532
F. Fereydouni-Forouzandeh, O. Mohamed
Tiny implantable biosensor nodes in the human body suffer from a lack of energy and low lifetime in Implantable Wireless Body Sensor Networks (IWBSN). They must remain and function well in the body as a source of comfort for a long time. The major challenge is related to the ultra-low capacity of the tiny battery inside the biosensors for wireless transmission. In this paper, a serial triple modular redundancy (S-TMR) is proposed to enhance the reliability of the wireless data transmission based on TBCD protocol. A thorough fault injection simulation is performed to demonstrate up to 11 times gain in fault coverage when using S-TMR technique.
{"title":"Fault Tolerant Analysis using Serial-Triple Modular Redundancy (S-TMR) on TBCD Ultra Low Energy Communication Protocol for Biosensors","authors":"F. Fereydouni-Forouzandeh, O. Mohamed","doi":"10.1109/EDiS57230.2022.9996532","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996532","url":null,"abstract":"Tiny implantable biosensor nodes in the human body suffer from a lack of energy and low lifetime in Implantable Wireless Body Sensor Networks (IWBSN). They must remain and function well in the body as a source of comfort for a long time. The major challenge is related to the ultra-low capacity of the tiny battery inside the biosensors for wireless transmission. In this paper, a serial triple modular redundancy (S-TMR) is proposed to enhance the reliability of the wireless data transmission based on TBCD protocol. A thorough fault injection simulation is performed to demonstrate up to 11 times gain in fault coverage when using S-TMR technique.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115738143","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}
Advanced devices, such as artificial intelligence, robots, and the Internet of Things, play an integral part in expanding agricultural output and eco-system efficiency. Greenhouse farming is an agricultural management approach that has proven effective in increasing food output and ensuring sustainability. Technology has been able to tackle the problems of greenhouse farming by helping to overcome its constraints, rectify negative effects, and ensure system sustainability. The purpose of this research is to look at global greenhouse technology research trends in order to determine the technology used and the most noteworthy research lines in the literature. The analysis was conducted using a variety of approaches, both descriptive and inferential. The findings of this study showcase that the domain is essential to worldwide food security and is very active in terms of study. This does not, however, exclude out further innovation and development, which will be the focus of our future efforts.
{"title":"IoT and WSNs Technology for Control in the Greenhouse Agriculture - Review","authors":"Mokeddem Kamal Abdelmadjid, Seddiki Noureddine, Bourouis Amina, Benahmed Khelifa, Benahmed Tariq, Lairedj Boubakeur","doi":"10.1109/EDiS57230.2022.9996500","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996500","url":null,"abstract":"Advanced devices, such as artificial intelligence, robots, and the Internet of Things, play an integral part in expanding agricultural output and eco-system efficiency. Greenhouse farming is an agricultural management approach that has proven effective in increasing food output and ensuring sustainability. Technology has been able to tackle the problems of greenhouse farming by helping to overcome its constraints, rectify negative effects, and ensure system sustainability. The purpose of this research is to look at global greenhouse technology research trends in order to determine the technology used and the most noteworthy research lines in the literature. The analysis was conducted using a variety of approaches, both descriptive and inferential. The findings of this study showcase that the domain is essential to worldwide food security and is very active in terms of study. This does not, however, exclude out further innovation and development, which will be the focus of our future efforts.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121237708","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}
Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996480
Nour Mekki, Djamel Berrabah
Entity Resolution is the process of identifying whether or not various entities from different sources are referring to the same real-world entity. Entity Resolution hasn't been extensively researched in graph databases, whereas it has been for relational databases. This paper focuses on providing comparisons of experiments on various datasets to determine the most appropriate method used in the Entity Resolution process from among literature's similarity algorithms, graph embedding techniques, and graph embedding algorithms combined to link prediction. Moreover, if the embedding algorithm employed has an impact on the given results. The results show that the Entity Resolution process performed better when graph embedding techniques were paired with link prediction, and the chosen graph embedding algorithm also has an impact on the results.
{"title":"Entity Resolution in graph databases: comparison study","authors":"Nour Mekki, Djamel Berrabah","doi":"10.1109/EDiS57230.2022.9996480","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996480","url":null,"abstract":"Entity Resolution is the process of identifying whether or not various entities from different sources are referring to the same real-world entity. Entity Resolution hasn't been extensively researched in graph databases, whereas it has been for relational databases. This paper focuses on providing comparisons of experiments on various datasets to determine the most appropriate method used in the Entity Resolution process from among literature's similarity algorithms, graph embedding techniques, and graph embedding algorithms combined to link prediction. Moreover, if the embedding algorithm employed has an impact on the given results. The results show that the Entity Resolution process performed better when graph embedding techniques were paired with link prediction, and the chosen graph embedding algorithm also has an impact on the results.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980022","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}
Pub Date : 2022-11-02DOI: 10.1109/edis57230.2022.9996460
Pierre Boulet
In the “bio-inspired information processing” project of the IRCICA interdisciplinary institute, we tackle the scientific challenges of the emerging neuromorphic architectures. These computer architectures mimic the brain by handling the information as spike trains and by processing this information with spiking neural networks. They have a strong potential for ultra low power artificial intelligence. Based on our last 10 years of research, we will present the state-of-the-art of these architectures, the applications we focus on, and the scientific hot topics.
{"title":"Designing neuromorphic architectures: towards an ultra low power AI","authors":"Pierre Boulet","doi":"10.1109/edis57230.2022.9996460","DOIUrl":"https://doi.org/10.1109/edis57230.2022.9996460","url":null,"abstract":"In the “bio-inspired information processing” project of the IRCICA interdisciplinary institute, we tackle the scientific challenges of the emerging neuromorphic architectures. These computer architectures mimic the brain by handling the information as spike trains and by processing this information with spiking neural networks. They have a strong potential for ultra low power artificial intelligence. Based on our last 10 years of research, we will present the state-of-the-art of these architectures, the applications we focus on, and the scientific hot topics.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085000","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}
Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996494
S. Niar
eep learning (DL) models such as convolutional neural networks (CNN) are being deployed to solve various computer vision and natural language processing tasks at the edge. It is a challenge to find the right DL architecture that simultaneously meets the accuracy, power and performance budgets of such resource-constrained devices. Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models for a variety of target hardware platform.However, such algorithms require excessive computational resources. Thousands of GPU days are required to evaluate and explore modern DL architecture search space. In this talk I will present state-of-the-art approaches that are based on two components: a) Surrogate models to predict quickly architecture accuracy and hardware performances to speed up HW-NAS, b) Efficient search algorithm that explores only promising hardware and software regions of the search space.
{"title":"Optimizing Deep Learning Application for Edge Computing","authors":"S. Niar","doi":"10.1109/EDiS57230.2022.9996494","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996494","url":null,"abstract":"eep learning (DL) models such as convolutional neural networks (CNN) are being deployed to solve various computer vision and natural language processing tasks at the edge. It is a challenge to find the right DL architecture that simultaneously meets the accuracy, power and performance budgets of such resource-constrained devices. Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models for a variety of target hardware platform.However, such algorithms require excessive computational resources. Thousands of GPU days are required to evaluate and explore modern DL architecture search space. In this talk I will present state-of-the-art approaches that are based on two components: a) Surrogate models to predict quickly architecture accuracy and hardware performances to speed up HW-NAS, b) Efficient search algorithm that explores only promising hardware and software regions of the search space.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122349558","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}
Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996539
Brahim Achour, Idir Filali, Malika Belkadi, M. Laghrouche
Fall detection helps to provide medical assistance quickly and to avoid the aggravation of injuries. In this paper, we propose a new noninvasive and energy-efficient smart sensor for fall detection. The sensor is based on accelerometer data and is attached to 20 building workers. To reduce power consumption, a new method of data selection is proposed. This method is based on the use of sensor timers, which allows for the reduction of 91% of the acquired data and 94% of the transmitted data. Regarding the classification, a new classification approach is proposed. Indeed, each data segment is displayed as a graph. Then, a convolution neural network is trained to detect the presence or absence of falls in each graph. An accuracy of 98% was obtained. This result exceeds that obtained in several studies and shows the effectiveness of the proposed approach.
{"title":"Efficient energy smart sensor for fall detection based on accelerometer data and CNN model","authors":"Brahim Achour, Idir Filali, Malika Belkadi, M. Laghrouche","doi":"10.1109/EDiS57230.2022.9996539","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996539","url":null,"abstract":"Fall detection helps to provide medical assistance quickly and to avoid the aggravation of injuries. In this paper, we propose a new noninvasive and energy-efficient smart sensor for fall detection. The sensor is based on accelerometer data and is attached to 20 building workers. To reduce power consumption, a new method of data selection is proposed. This method is based on the use of sensor timers, which allows for the reduction of 91% of the acquired data and 94% of the transmitted data. Regarding the classification, a new classification approach is proposed. Indeed, each data segment is displayed as a graph. Then, a convolution neural network is trained to detect the presence or absence of falls in each graph. An accuracy of 98% was obtained. This result exceeds that obtained in several studies and shows the effectiveness of the proposed approach.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115166234","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}
Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996475
M. Moumni, H. Zahaf, Lakhdar Loukil, A. Benyamina
The technology of the internet of things (IoT) describes wireless sensor network devices able to quantify physical phenomena in a digital data format and send them to a base station to analyze. Their performance, however, is highly reliant on the problem of sensor node placement. An efficient deployment approach is required to improve network performance. In this paper, we suggest a novel deployment approach using a regular square grid pattern. The suggested method reduces iteratively the scale of dis-cretization until it gets to the scale that can be used to solve the problem. The suggested method considers the coverage problem. We compared our suggested strategy to the standard way of solving the problem. According to simulation findings, our method outperforms the existing workflow in terms of time to solve the problem while lowering the number of deployed nodes.
{"title":"Toward an iterative discretization approach for optimal sensor placement","authors":"M. Moumni, H. Zahaf, Lakhdar Loukil, A. Benyamina","doi":"10.1109/EDiS57230.2022.9996475","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996475","url":null,"abstract":"The technology of the internet of things (IoT) describes wireless sensor network devices able to quantify physical phenomena in a digital data format and send them to a base station to analyze. Their performance, however, is highly reliant on the problem of sensor node placement. An efficient deployment approach is required to improve network performance. In this paper, we suggest a novel deployment approach using a regular square grid pattern. The suggested method reduces iteratively the scale of dis-cretization until it gets to the scale that can be used to solve the problem. The suggested method considers the coverage problem. We compared our suggested strategy to the standard way of solving the problem. According to simulation findings, our method outperforms the existing workflow in terms of time to solve the problem while lowering the number of deployed nodes.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"83 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113943365","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}
Pub Date : 2022-11-02DOI: 10.1109/EDiS57230.2022.9996490
Abderrezzaq Ziane, R. Dabou, NECAIBIA Ammar, A. Rouabhia, K. Bouchouicha, N. Sahouane, S. Lachtar, A. Bouraiou, Ahmed Amine Larbi
The integration of novel technology trends namely IoT and cloud computing in the renewable energy sector is an ambitious goal and it has a lot of benefits for the sector. In this work, an IoT platform for the online monitoring of renewable energy systems is proposed. The platform is based on low-cost novel technology hardware with open-source software which could solve several problems related to industrial monitoring and SCADA systems. As a proof of concept, an IoT-based monitoring system was developed for a grid-connected PV station in URERMS.
{"title":"IoT Platform For Online Monitoring Of Renewable Energy Systems","authors":"Abderrezzaq Ziane, R. Dabou, NECAIBIA Ammar, A. Rouabhia, K. Bouchouicha, N. Sahouane, S. Lachtar, A. Bouraiou, Ahmed Amine Larbi","doi":"10.1109/EDiS57230.2022.9996490","DOIUrl":"https://doi.org/10.1109/EDiS57230.2022.9996490","url":null,"abstract":"The integration of novel technology trends namely IoT and cloud computing in the renewable energy sector is an ambitious goal and it has a lot of benefits for the sector. In this work, an IoT platform for the online monitoring of renewable energy systems is proposed. The platform is based on low-cost novel technology hardware with open-source software which could solve several problems related to industrial monitoring and SCADA systems. As a proof of concept, an IoT-based monitoring system was developed for a grid-connected PV station in URERMS.","PeriodicalId":288133,"journal":{"name":"2022 3rd International Conference on Embedded & Distributed Systems (EDiS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517691","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}
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