This research addresses the challenge of enhancing fault detection and tolerance in wireless sensor networks (WSNs) to ensure reliable data transmission in adverse conditions. Through simulation, experimentation, and modeling, the study develops techniques and algorithms for improving WSN fault resilience. Key evaluation criteria include Detection Accuracy, Response Time, Energy Efficiency, and Scalability. Redundancy-based methods, such as node and path redundancy, are explored as effective fault tolerance techniques. Results demonstrate lower response times, improved detection accuracy, energy efficiency, and scalability. The findings contribute to WSN technology by enhancing data accuracy, network resilience, and energy conservation, though challenges and limitations persist.
{"title":"Fault Detection and Tolerance in Wireless Sensor Networks: A Study on Reliable Data Transmission using Machine Learning Algorithms","authors":"","doi":"10.33140/jsndc.04.01.03","DOIUrl":"https://doi.org/10.33140/jsndc.04.01.03","url":null,"abstract":"This research addresses the challenge of enhancing fault detection and tolerance in wireless sensor networks (WSNs) to ensure reliable data transmission in adverse conditions. Through simulation, experimentation, and modeling, the study develops techniques and algorithms for improving WSN fault resilience. Key evaluation criteria include Detection Accuracy, Response Time, Energy Efficiency, and Scalability. Redundancy-based methods, such as node and path redundancy, are explored as effective fault tolerance techniques. Results demonstrate lower response times, improved detection accuracy, energy efficiency, and scalability. The findings contribute to WSN technology by enhancing data accuracy, network resilience, and energy conservation, though challenges and limitations persist.","PeriodicalId":517894,"journal":{"name":"Journal of Sensor Networks and Data Communications","volume":"101 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285440","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 work is interdisciplinary in nature. This work tries to apply latest discoveries in Artificial Intel-ligence to classic testing methodologies. Machine Learning which is the field of Artificial Intelligence is explored in this work. The work demonstrates that provided the test team maintains the required data, Machine Learning Algorithms can aid in deciphering patterns from the test data. Patterns of interest are the relation between testers experience in the project and bugs uncovered, relations between the testers experience and the efficiency of test case with respect to code coverage and test execution time. Relation between testers experience and efficiency of test case with respect to code coverage and execution time, relation between testers experience and bugs uncovered are explored using classic statistical techniques and clustering Machine Learning Algorithms. This clustering can be of immense help in test selection, prioritization, pruning and Regression test execution time reduction.
{"title":"Regression Test Suite Study Using Classic Statistical Methods and Machine Learning","authors":"Abhinandan H. Patil, Sangeeta A. Patil","doi":"10.33140/jsndc.04.01.02","DOIUrl":"https://doi.org/10.33140/jsndc.04.01.02","url":null,"abstract":"This work is interdisciplinary in nature. This work tries to apply latest discoveries in Artificial Intel-ligence to classic testing methodologies. Machine Learning which is the field of Artificial Intelligence is explored in this work. The work demonstrates that provided the test team maintains the required data, Machine Learning Algorithms can aid in deciphering patterns from the test data. Patterns of interest are the relation between testers experience in the project and bugs uncovered, relations between the testers experience and the efficiency of test case with respect to code coverage and test execution time. Relation between testers experience and efficiency of test case with respect to code coverage and execution time, relation between testers experience and bugs uncovered are explored using classic statistical techniques and clustering Machine Learning Algorithms. This clustering can be of immense help in test selection, prioritization, pruning and Regression test execution time reduction.","PeriodicalId":517894,"journal":{"name":"Journal of Sensor Networks and Data Communications","volume":"44 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140455070","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}
Regression test suite execution time study focus is essentially on two aspects. They are execution time reduction and making effective use of available hardware resources and manpower. This paper investigates how the regression test suite can be split into subsets to make use of parallel execution across several machines with identical execution speeds and asymmetrical execution speeds. In the symmetrical execution speed setup, long test execution time test cases are evenly distributed across all the hardware machines. However, in asymmetrical execution speed machines, more test cases are distributed to speed machines to make efficient use of hardware resources. In all the situations where there is automation tool to execute the individual test cases of test suite this approach can be employed to make effective use of hardware resources and to keep the execution time within bounds. The Algorithms can also be used in situations where there are queues involved and serial, fixed time service takes place for each of the entity being served.
{"title":"Quantification of Regression Test Suite Execution Time in Parallel Execution Setup with Weighted Test Suite Split Algorithm","authors":"Abhinandan H. Patil, Sangeeta A. Patil Karnataka","doi":"10.33140/jsndc.04.01.01","DOIUrl":"https://doi.org/10.33140/jsndc.04.01.01","url":null,"abstract":"Regression test suite execution time study focus is essentially on two aspects. They are execution time reduction and making effective use of available hardware resources and manpower. This paper investigates how the regression test suite can be split into subsets to make use of parallel execution across several machines with identical execution speeds and asymmetrical execution speeds. In the symmetrical execution speed setup, long test execution time test cases are evenly distributed across all the hardware machines. However, in asymmetrical execution speed machines, more test cases are distributed to speed machines to make efficient use of hardware resources. In all the situations where there is automation tool to execute the individual test cases of test suite this approach can be employed to make effective use of hardware resources and to keep the execution time within bounds. The Algorithms can also be used in situations where there are queues involved and serial, fixed time service takes place for each of the entity being served.","PeriodicalId":517894,"journal":{"name":"Journal of Sensor Networks and Data Communications","volume":"231 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462280","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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