Pub Date : 2021-07-27DOI: 10.1109/IAICT52856.2021.9532570
Sani Makusidi Suleiman, D. S. Shu'aibu, S. A. Babale
This paper investigates the effect of antenna power roll-off on 4G cellular networks from high altitude platform (HAP). This work proposes the deployment of HAP to link long distant separated base transceiver station (BTS) and those separated by bad terrain that will otherwise need a full terrestrial network to link to the core network. The paper also investigates the power roll-off for 4G and its effect on coverage extension. It investigates the best trajectory path for aerodynamic HAP. It also investigates the effect of varying antenna configurations such as the frequency, bandwidth and transmitting power on system coverage. Simulation results show that 200km coverage can be maintained by keeping a reduced trajectory path of 10km. Also maintaining a transmit power of 35dBm at 3.5GHz and 100MHz bandwidth with a roll-off factor of 5 can give a coverage of up to 200km.
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Pub Date : 2021-06-29DOI: 10.1109/IAICT52856.2021.9532556
Hamed Moasses, A. Ghaderzadeh, K. Khamforoosh
Network lifetime is always a challenging issue in battery-powered networks due to the difficulty of recharging or replacing nodes in some scenarios. Clustering methods are a promising approach to tackle this challenge and prolong lifetime by efficiently distributing tasks among nodes in the cluster. The present study aimed to improve energy consumption in heterogeneous IoT devices using an energy-aware clustering method. In a heterogeneous IoT network, nodes (i.e., battery-powered IoT devices) can have a variety of energy profiles and communication capabilities. Most of the existing clustering algorithms have neglected the heterogeneity of energy capacity among nodes and assumed that they are of the same energy level. In this work, we present HetEng, a Cluster Head (CH) selection process that extended an existing clustering algorithm, named Smart-BEEM. To this end, we proposed a statistical approach that distributes energy consumption among highly energetic nodes in the network topology by constantly changing the CH role between the nodes based on their real energy levels (in joules). Experimental results showed that HetEng resulted in a 6.6% increase of alive nodes and 3% improvement in residual energy among the nodes in comparison with Smart-BEEM. Moreover, our method reduced the total number of iterations by 1 % on average.
{"title":"HetEng: An Improved Distributed Energy Efficient Clustering Scheme for Heterogeneous IoT Networks","authors":"Hamed Moasses, A. Ghaderzadeh, K. Khamforoosh","doi":"10.1109/IAICT52856.2021.9532556","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532556","url":null,"abstract":"Network lifetime is always a challenging issue in battery-powered networks due to the difficulty of recharging or replacing nodes in some scenarios. Clustering methods are a promising approach to tackle this challenge and prolong lifetime by efficiently distributing tasks among nodes in the cluster. The present study aimed to improve energy consumption in heterogeneous IoT devices using an energy-aware clustering method. In a heterogeneous IoT network, nodes (i.e., battery-powered IoT devices) can have a variety of energy profiles and communication capabilities. Most of the existing clustering algorithms have neglected the heterogeneity of energy capacity among nodes and assumed that they are of the same energy level. In this work, we present HetEng, a Cluster Head (CH) selection process that extended an existing clustering algorithm, named Smart-BEEM. To this end, we proposed a statistical approach that distributes energy consumption among highly energetic nodes in the network topology by constantly changing the CH role between the nodes based on their real energy levels (in joules). Experimental results showed that HetEng resulted in a 6.6% increase of alive nodes and 3% improvement in residual energy among the nodes in comparison with Smart-BEEM. Moreover, our method reduced the total number of iterations by 1 % on average.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131532","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 : 2021-05-02DOI: 10.1109/IAICT52856.2021.9532584
Masoud Jalayer, R. Jalayer, A. Kaboli, C. Orsenigo, C. Vercellis
A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.
{"title":"Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN","authors":"Masoud Jalayer, R. Jalayer, A. Kaboli, C. Orsenigo, C. Vercellis","doi":"10.1109/IAICT52856.2021.9532584","DOIUrl":"https://doi.org/10.1109/IAICT52856.2021.9532584","url":null,"abstract":"A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.","PeriodicalId":416542,"journal":{"name":"2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130452644","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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