We propose an efficient routing mechanism called PAEER (Power-Aware Energy Efficient Routing) for meeting Network Lifetime Maximization and energy efficiency in the Wireless Sensor Networks(WSN). The different contributions of the PAEER approach are following (a) Multisink node approach which can lead to increase the nodes network lifetime and event detection mechanism that meets reliability requirement of the WSN (b) Using PAEER mechanism sends the data to sink node by covering multi-path routes to aggregate the nodes data. Thus energy consumption of the WSN can be reduced maximum level therefore network lifetime increased. This can be proved both theoretical and experiment solutions can be better when compared to other solutions. By using Network Simulator-3 (NS-3) testbed the results show the better results for the all Quality of Service parameters (QoS) like Throughput, Network Lifetime, Power Consumption, etc.
为了满足无线传感器网络(WSN)的网络寿命最大化和能效要求，提出了一种高效的路由机制PAEER (Power-Aware Energy efficient routing)。PAEER方法的不同贡献在于:(1)多汇聚节点方法可以增加节点的网络生存时间和满足WSN可靠性要求的事件检测机制;(2)使用PAEER机制通过覆盖多路径路由将数据发送到汇聚节点，以聚合节点数据。从而最大限度地降低无线传感器网络的能耗，提高网络寿命。这可以证明理论解决方案和实验解决方案都可以比其他解决方案更好。通过网络模拟器-3 (NS-3)测试，结果表明，在吞吐量、网络寿命、功耗等所有服务质量参数(QoS)方面都取得了较好的结果。
{"title":"Minimizing Delay and Maximizing Network Lifetime by Power-Aware Energy Efficient Routing [PAEER] Mechanism in Wireless Sensor Networks","authors":"Ramprakash S, Vijayakumari B, Subathra P","doi":"10.3233/apc200177","DOIUrl":"https://doi.org/10.3233/apc200177","url":null,"abstract":"We propose an efficient routing mechanism called PAEER (Power-Aware Energy Efficient Routing) for meeting Network Lifetime Maximization and energy efficiency in the Wireless Sensor Networks(WSN). The different contributions of the PAEER approach are following (a) Multisink node approach which can lead to increase the nodes network lifetime and event detection mechanism that meets reliability requirement of the WSN (b) Using PAEER mechanism sends the data to sink node by covering multi-path routes to aggregate the nodes data. Thus energy consumption of the WSN can be reduced maximum level therefore network lifetime increased. This can be proved both theoretical and experiment solutions can be better when compared to other solutions. By using Network Simulator-3 (NS-3) testbed the results show the better results for the all Quality of Service parameters (QoS) like Throughput, Network Lifetime, Power Consumption, etc.","PeriodicalId":354831,"journal":{"name":"Intelligent Systems and Computer Technology","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800824","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}
There are several existing wireless system in 5G technology, originating interference in same frequency band and degenerate the concert of received signal. Antenna System comprise of different Beam forming methods in which direction of required signal is generated by the beam and nulls and the voids are set in the direction of unwanted signal (Interference). The survey of different blind and non-blind beam forming algorithms are discussed using smart antenna and phased array. It involves Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), Sample Matrix Inversion(SMI), Linear Constrained Minimum Variance (LCMV), Constant Modulus (CMA), Decision feedback equalization based LMS (DFE-LMS) are considered. These algorithms are outlined to be claimed in 5G network to provide good quality, capacity and dealing with coincidence of signals and interference.
{"title":"An Inclusive Survey on Various Adaptive Beam Forming Algorithm for 5G Communications Systems","authors":"R. KaviyaK, S. Deepa","doi":"10.3233/apc200182","DOIUrl":"https://doi.org/10.3233/apc200182","url":null,"abstract":"There are several existing wireless system in 5G technology, originating interference in same frequency band and degenerate the concert of received signal. Antenna System comprise of different Beam forming methods in which direction of required signal is generated by the beam and nulls and the voids are set in the direction of unwanted signal (Interference). The survey of different blind and non-blind beam forming algorithms are discussed using smart antenna and phased array. It involves Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), Sample Matrix Inversion(SMI), Linear Constrained Minimum Variance (LCMV), Constant Modulus (CMA), Decision feedback equalization based LMS (DFE-LMS) are considered. These algorithms are outlined to be claimed in 5G network to provide good quality, capacity and dealing with coincidence of signals and interference.","PeriodicalId":354831,"journal":{"name":"Intelligent Systems and Computer Technology","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124205472","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 present the compact branch line balun to operate at the frequency range of 2.4GHz. The compact branchlinebalun is designed using the substrate material with the dielectric constant of FR4 material. The proposed balun is designed using different transmission lines. Thus the balun should achieves -3dB power division and 1800 phase differences between the outputs. The main objective of this design focuses on size reduction. To reduce the size, A balun is realized using the equivalent T-shape structure. After the reduction techniques the implemented size of the balun is 29.41x44.32 mm2 achieves 35% of size reduction. Thus the measured S11 are -23 dB and the S21,S31 remains -3dB and provide 1790 phase difference between the outputs at the frequency of 2.4GHz.
{"title":"Design of Compact BranchlineBalun","authors":"Indhumathi J, Maheswari S","doi":"10.3233/apc200165","DOIUrl":"https://doi.org/10.3233/apc200165","url":null,"abstract":"This paper present the compact branch line balun to operate at the frequency range of 2.4GHz. The compact branchlinebalun is designed using the substrate material with the dielectric constant of FR4 material. The proposed balun is designed using different transmission lines. Thus the balun should achieves -3dB power division and 1800 phase differences between the outputs. The main objective of this design focuses on size reduction. To reduce the size, A balun is realized using the equivalent T-shape structure. After the reduction techniques the implemented size of the balun is 29.41x44.32 mm2 achieves 35% of size reduction. Thus the measured S11 are -23 dB and the S21,S31 remains -3dB and provide 1790 phase difference between the outputs at the frequency of 2.4GHz.","PeriodicalId":354831,"journal":{"name":"Intelligent Systems and Computer Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127064682","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}
Breast cancer is one of the most deadly diseases encountered among women for which the cause is not clearly defined yet. Early diagnosis may help the physicians in the treatment of this deadly disease which could turn out fatal otherwise. Machine Learning techniques are employed in the process of detecting breast cancer with greater accuracy. Individual classifiers employed in this process, predicted the disease with less accuracy when compared with ensemble models. Ensemble methods employ a group of classifiers to individually classify the data. It then combines the result of the individual classifiers using weighted voting of their predictions. Ensemble machines perform better than individual models and show improved levels in the accuracy of the prediction system. This paper examines and evaluates different ensemble machines that are used in the prediction of breast cancer and tries to identify the combinations that prove to be better than the existing ones.
{"title":"Evaluation of Ensemble Machines in Breast Cancer Prediction","authors":"S. LeenaNesamani, S. NirmalaSugirthaRajini","doi":"10.3233/apc200173","DOIUrl":"https://doi.org/10.3233/apc200173","url":null,"abstract":"Breast cancer is one of the most deadly diseases encountered among women for which the cause is not clearly defined yet. Early diagnosis may help the physicians in the treatment of this deadly disease which could turn out fatal otherwise. Machine Learning techniques are employed in the process of detecting breast cancer with greater accuracy. Individual classifiers employed in this process, predicted the disease with less accuracy when compared with ensemble models. Ensemble methods employ a group of classifiers to individually classify the data. It then combines the result of the individual classifiers using weighted voting of their predictions. Ensemble machines perform better than individual models and show improved levels in the accuracy of the prediction system. This paper examines and evaluates different ensemble machines that are used in the prediction of breast cancer and tries to identify the combinations that prove to be better than the existing ones.","PeriodicalId":354831,"journal":{"name":"Intelligent Systems and Computer Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126841508","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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