Pradeep Katta, Karunanithi Kandasamy, Raja Soosaimarian Peter Raj, R. Subramanian, C. Perumal
The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.
{"title":"Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique","authors":"Pradeep Katta, Karunanithi Kandasamy, Raja Soosaimarian Peter Raj, R. Subramanian, C. Perumal","doi":"10.14201/adcaij.28435","DOIUrl":"https://doi.org/10.14201/adcaij.28435","url":null,"abstract":"The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"9 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84408251","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}
The current process of scientific and technological development is the outcome of the epochal Cultural Revolution in the West: i.e. the emergence of the Age of Enlightenment and its pursuit of "rationality". Today, "rationality" combined with "logic" has mutated into a "strong belief" in the power of rationality and "computational processes" as a 'safer' and only way to acquire knowledge. This is the main driving force behind the emergence of AI. At the core of this mind-set is the fundamental duality of the observer and the observed. After the imperial expansion of Western Europe – in alliance with religion, its previous foe (“Christianity”) – this world-view became the globally dominant mind-set. The paper explores the dominant narrative of rationality and reason of Western science, and seeks an alternative world of cultural diversity.
{"title":"The question of \"Mind-sets\" and AI: Cultural origins and limits of the current AI Ethical AIs and Cultural Pluralism","authors":"Badrudin Amershi","doi":"10.30564/aia.v4i2.5156","DOIUrl":"https://doi.org/10.30564/aia.v4i2.5156","url":null,"abstract":"The current process of scientific and technological development is the outcome of the epochal Cultural Revolution in the West: i.e. the emergence of the Age of Enlightenment and its pursuit of \"rationality\". Today, \"rationality\" combined with \"logic\" has mutated into a \"strong belief\" in the power of rationality and \"computational processes\" as a 'safer' and only way to acquire knowledge. This is the main driving force behind the emergence of AI. At the core of this mind-set is the fundamental duality of the observer and the observed. After the imperial expansion of Western Europe – in alliance with religion, its previous foe (“Christianity”) – this world-view became the globally dominant mind-set. The paper explores the dominant narrative of rationality and reason of Western science, and seeks an alternative world of cultural diversity.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"61 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89447883","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 : 2023-01-01DOI: 10.1007/978-3-031-25549-6
{"title":"Distributed Artificial Intelligence: 4th International Conference, DAI 2022, Tianjin, China, December 15–17, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-25549-6","DOIUrl":"https://doi.org/10.1007/978-3-031-25549-6","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73579323","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}
Over the last fifty years, as the field of automated essay evaluation has progressed, several ways have been offered. The three aspects of style, substance, and semantics are the primary focus of automated essay evaluation. The style and content attributes have received the most attention, while the semantics attribute has received less attention. A smaller fraction of the essay (window) is chosen to measure semantics, and the essay is broken into smaller portions using this window. The goal of this work is to determine an acceptable window size for measuring semantic coherence between different parts of the essay with more precision.
{"title":"Optimization of Window Size for Calculating Semantic Coherence Within an Essay","authors":"Kshitiz Srivastava, Namrata Dhanda, Anurag Shrivastava","doi":"10.14201/adcaij.27184","DOIUrl":"https://doi.org/10.14201/adcaij.27184","url":null,"abstract":"Over the last fifty years, as the field of automated essay evaluation has progressed, several ways have been offered. The three aspects of style, substance, and semantics are the primary focus of automated essay evaluation. The style and content attributes have received the most attention, while the semantics attribute has received less attention. A smaller fraction of the essay (window) is chosen to measure semantics, and the essay is broken into smaller portions using this window. The goal of this work is to determine an acceptable window size for measuring semantic coherence between different parts of the essay with more precision.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81741668","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}
In this paper, we have looked at how easy it is for users in an organisation to be given different roles, as well as how important it is to make sure that the tasks are done well using predictive analytical tools. As a result, ensemble of classification and regression tree link Neural Network was adopted for evaluating the effectiveness of role-based tasks associated with organization unit. A Human Resource Manangement System was design and developed to obtain comprehensive information about their employees’ performance levels, as well as to ascertain their capabilities, skills, and the tasks they perform and how they perform them. Datasets were drawn from evaluation of the system and used for machine learning evaluation. Linear regression models, decision trees, and Genetic Algorithm have proven to be good at prediction in all cases. In this way, the research findings highlight the need of ensuring that users tasks are done in a timely way, as well as enhancing an organization’s ability to assign individual duties.
{"title":"An Ensemble Classification and Regression Neural Network for Evaluating Role-based Tasks Associated with Organizational Unit","authors":"M. Abbod, A. Alrashedi","doi":"10.14201/adcaij.26764","DOIUrl":"https://doi.org/10.14201/adcaij.26764","url":null,"abstract":"In this paper, we have looked at how easy it is for users in an organisation to be given different roles, as well as how important it is to make sure that the tasks are done well using predictive analytical tools. As a result, ensemble of classification and regression tree link Neural Network was adopted for evaluating the effectiveness of role-based tasks associated with organization unit. A Human Resource Manangement System was design and developed to obtain comprehensive information about their employees’ performance levels, as well as to ascertain their capabilities, skills, and the tasks they perform and how they perform them. Datasets were drawn from evaluation of the system and used for machine learning evaluation. Linear regression models, decision trees, and Genetic Algorithm have proven to be good at prediction in all cases. In this way, the research findings highlight the need of ensuring that users tasks are done in a timely way, as well as enhancing an organization’s ability to assign individual duties.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"56 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85924762","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}
The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.
{"title":"CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN","authors":"Vipul Narayan, A. Daniel","doi":"10.14201/adcaij.27271","DOIUrl":"https://doi.org/10.14201/adcaij.27271","url":null,"abstract":"The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"2 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85140237","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}
This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.
{"title":"Deep Learning Approach to Technician Routing and Scheduling Problem","authors":"Engin Pekel","doi":"10.14201/adcaij.27393","DOIUrl":"https://doi.org/10.14201/adcaij.27393","url":null,"abstract":"This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"73 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85745667","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}
Vehicle routing problem (VRP); it is defined as the problem of planning the best distribution or collection routes of the vehicles assigned to serve the scattered centers from one or more warehouses in order to meet the demands of the customers. Vehicle routing problem has been a kind of problem in which various studies have been done in recent years. Many vehicle routing problems include scheduling visits to customers who are available during certain time windows. These problems are known as vehicle routing problems with time windows (VRPTWs). In this study, a tabu search optimization is proposed for the solution of time window vehicle routing problem (VRPTWs). The results were compared with the current situation and the results were interpreted.
{"title":"Time-Windowed Vehicle Routing Problem: Tabu Search Algorithm Approach","authors":"Hasibe Berfu Demir, Ebru Pekel Özmen, Şakir Esnaf","doi":"10.14201/adcaij.27533","DOIUrl":"https://doi.org/10.14201/adcaij.27533","url":null,"abstract":"Vehicle routing problem (VRP); it is defined as the problem of planning the best distribution or collection routes of the vehicles assigned to serve the scattered centers from one or more warehouses in order to meet the demands of the customers. Vehicle routing problem has been a kind of problem in which various studies have been done in recent years. Many vehicle routing problems include scheduling visits to customers who are available during certain time windows. These problems are known as vehicle routing problems with time windows (VRPTWs). In this study, a tabu search optimization is proposed for the solution of time window vehicle routing problem (VRPTWs). The results were compared with the current situation and the results were interpreted.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"20 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74679509","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}
The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.
{"title":"Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames","authors":"Altaf Hussain, Mehtab Ahmad, Tariq Hussain, Ijaz Ullah","doi":"10.14201/adcaij.27430","DOIUrl":"https://doi.org/10.14201/adcaij.27430","url":null,"abstract":"The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"129 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76745818","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}
In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors.
在无线传感器网络中，使用关系查询访问传感器数据。连接查询通常用于从存储在无线传感器网络不同部分的多个表中检索数据。然而，这样的查询需要大量的能量。许多研究都试图降低查询能耗。然而，大多数建议的技术都解决了在静态表之间执行的二进制连接。很少考虑数据流之间的n路连接。使用数据流的连接查询持续工作并且需要越来越多的能量，这就是为什么涉及多个表的n-way连接消耗如此多的能量。因此，挑战在于减少能量耗散。此外，有必要确定n-way连接的适当执行顺序。n路连接可能实现的数量随着表的数量呈指数增长。本文对数据流之间n路连接的一些有趣的方法进行了评价。所比较的方法有Stern等人的extern-join、Sens-join以及NSLJ (N-way Stream Local Join)和NSLSJ (N-way Stream Local Semi-Join)两种技术。根据几个参数进行比较，以确定哪个用例适合每种技术。NSLSJ最适合具有低连接选择因子的连接查询，而外部连接更适合具有非常高选择因子的查询。
{"title":"Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks","authors":"Boubekeur Djail","doi":"10.14201/adcaij.27777","DOIUrl":"https://doi.org/10.14201/adcaij.27777","url":null,"abstract":"In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"83 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73959957","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：