Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408898
Hussain Albarakati, R. Ammar, Raafat S. Elfouly
Underwater wireless acoustic sensor networks (UWASNs) have emerged as a powerful communication technology for discovering and extracting data in aquatic environments. UWASNs have numerous applications in areas such as fisheries, resource exploration, mine reconnaissance, oil and gas inspection, marine exploration and military surveillance. However, these applications are limited by the capacity of networks to detect, discover, transmit, and forward big data. In particular, transmitting and receiving large volumes of data requires great lengths of time and substantial power, and thus fails to meet the real-time constraints. This problem has motivated us to focus on developing an underwater computer-embedded system capable of efficient big-data management. Thus, we have developed methods to discover and extract valuable information beneath the ocean using data-mining approaches. Previously, we introduced real-time underwater system architectures (RTUSAs) that use a single computer. In this study, we extend our results and propose a new RTUSA for large-scale networks. This novel RTUSA uses multi-computers and aims to enhance the reliability of our proposed system. Determining the optimal location of computers with respect to their membership of acoustic sensor nodes, so as to minimize delay time, power consumption, and balance loads, are NP-hard problems. Therefore, we propose a heuristic approach that enables optimization of computer locations and their memberships of acoustic sensor nodes. We conduct simulations to show the merits of our findings and measure the performance of our proposed solution.
{"title":"Optimal Localization of Multi-Computer Architecture for Large-Scale Underwater Wireless Sensor Networks","authors":"Hussain Albarakati, R. Ammar, Raafat S. Elfouly","doi":"10.1109/ISSPIT51521.2020.9408898","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408898","url":null,"abstract":"Underwater wireless acoustic sensor networks (UWASNs) have emerged as a powerful communication technology for discovering and extracting data in aquatic environments. UWASNs have numerous applications in areas such as fisheries, resource exploration, mine reconnaissance, oil and gas inspection, marine exploration and military surveillance. However, these applications are limited by the capacity of networks to detect, discover, transmit, and forward big data. In particular, transmitting and receiving large volumes of data requires great lengths of time and substantial power, and thus fails to meet the real-time constraints. This problem has motivated us to focus on developing an underwater computer-embedded system capable of efficient big-data management. Thus, we have developed methods to discover and extract valuable information beneath the ocean using data-mining approaches. Previously, we introduced real-time underwater system architectures (RTUSAs) that use a single computer. In this study, we extend our results and propose a new RTUSA for large-scale networks. This novel RTUSA uses multi-computers and aims to enhance the reliability of our proposed system. Determining the optimal location of computers with respect to their membership of acoustic sensor nodes, so as to minimize delay time, power consumption, and balance loads, are NP-hard problems. Therefore, we propose a heuristic approach that enables optimization of computer locations and their memberships of acoustic sensor nodes. We conduct simulations to show the merits of our findings and measure the performance of our proposed solution.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132903039","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408730
Mohammad Alsulami, Raafat S. Elfouly, R. Ammar, Abdullah Alenizi
Identifying the number and location of processing machines in underwater Wireless Sensor Networks (UWSNs) is one of the hot topics nowadays. UWSNs are vital in monitoring and detecting objects or phenomenon in underwater environment [11]. UWSNs, however, have some limitations and challenges. The low bandwidth capacity is a key challenge [10] [5]. The next main challenge in UWSNs is having long propagation delay [8] [5]. These two challenges negatively impact the performance of UWSNs even if the number and location of processing machines are chosen optimally. Therefore, in paper, we propose a framework including a Modified K-Medoids algorithm that can help to identify the location of processing machines that we need to deploy. We study the effectiveness of having such algorithm on end to end delay and load balancing. Semi-uniform distribution outperforms in term of load balancing comparing to the other two distributions. We consider three different scenario to show merits of our work.
{"title":"A Modified K-Medoids Algorithm for Deploying a Required Number of Computing Systems in a Three Dimensional Space in Underwater Wireless Sensor Networks","authors":"Mohammad Alsulami, Raafat S. Elfouly, R. Ammar, Abdullah Alenizi","doi":"10.1109/ISSPIT51521.2020.9408730","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408730","url":null,"abstract":"Identifying the number and location of processing machines in underwater Wireless Sensor Networks (UWSNs) is one of the hot topics nowadays. UWSNs are vital in monitoring and detecting objects or phenomenon in underwater environment [11]. UWSNs, however, have some limitations and challenges. The low bandwidth capacity is a key challenge [10] [5]. The next main challenge in UWSNs is having long propagation delay [8] [5]. These two challenges negatively impact the performance of UWSNs even if the number and location of processing machines are chosen optimally. Therefore, in paper, we propose a framework including a Modified K-Medoids algorithm that can help to identify the location of processing machines that we need to deploy. We study the effectiveness of having such algorithm on end to end delay and load balancing. Semi-uniform distribution outperforms in term of load balancing comparing to the other two distributions. We consider three different scenario to show merits of our work.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128736996","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408944
Danyal Maheshwari, B. G. Zapirain, Daniel Sierra-Sosa
This paper presents a Quantum versus classical implemented of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. Using novel Quantum computing (QC) along with Quantum Machine Learning (QML) techniques in the healthcare system to improve and accelerate the computing of existing ML models that allows the different approach to understanding the complex patterns of the disease. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system. Our study has proved that QC improves the computational speed and its inclusion in medical applications will deliver faster results to physicians and caregivers.
{"title":"Machine learning applied to diabetes dataset using Quantum versus Classical computation","authors":"Danyal Maheshwari, B. G. Zapirain, Daniel Sierra-Sosa","doi":"10.1109/ISSPIT51521.2020.9408944","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408944","url":null,"abstract":"This paper presents a Quantum versus classical implemented of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. Using novel Quantum computing (QC) along with Quantum Machine Learning (QML) techniques in the healthcare system to improve and accelerate the computing of existing ML models that allows the different approach to understanding the complex patterns of the disease. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system. Our study has proved that QC improves the computational speed and its inclusion in medical applications will deliver faster results to physicians and caregivers.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063147","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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