Pub Date : 2018-12-01DOI: 10.1109/IADCC.2018.8692111
A. Saggu, Kavita Pandey
Vehicular ad hoc networks (VANETs) have fetched great interest in both industry and research oriented fields owing to the highly mobile nature and randomly changing topology exhibited by these networks. These characteristics make them susceptible to frequent disconnections, contention and collision related problems. Designing a set of protocols which would cater to the characteristic features of VANETs is a very daunting task. This paper presents a detailed survey of a wide variety of Position-based routing (PBR) protocols. PBR protocols exploit the on-board global positioning receivers to acquire location information of vehicles. Moreover on-board maps are used to fetch the details regarding layout of the road thereby purging the need to set up and maintain routes between the vehicular nodes, making these protocols highly desirable for VANETs. Further a novel classification methodology of the protocols under study along with a comparative analysis depicting their similarity and dissimilarities has been presented.
车辆自组织网络(Vehicular ad hoc network, vanet)由于其具有高度移动性和随机变化的拓扑结构而引起了工业界和研究领域的极大兴趣。这些特点使它们容易受到频繁断开、争用和碰撞相关问题的影响。设计一套能够满足VANETs特性的协议是一项非常艰巨的任务。本文详细介绍了各种基于位置的路由(PBR)协议。PBR协议利用车载全球定位接收机获取车辆位置信息。此外，车载地图用于获取有关道路布局的详细信息，从而消除了在车辆节点之间建立和维护路线的需要，使这些协议非常适合VANETs。此外，还提出了一种新的正在研究的协议分类方法，并对其相似性和差异性进行了比较分析。
{"title":"Comparative Analysis of Position-Based Routing Protocols for VANETs","authors":"A. Saggu, Kavita Pandey","doi":"10.1109/IADCC.2018.8692111","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692111","url":null,"abstract":"Vehicular ad hoc networks (VANETs) have fetched great interest in both industry and research oriented fields owing to the highly mobile nature and randomly changing topology exhibited by these networks. These characteristics make them susceptible to frequent disconnections, contention and collision related problems. Designing a set of protocols which would cater to the characteristic features of VANETs is a very daunting task. This paper presents a detailed survey of a wide variety of Position-based routing (PBR) protocols. PBR protocols exploit the on-board global positioning receivers to acquire location information of vehicles. Moreover on-board maps are used to fetch the details regarding layout of the road thereby purging the need to set up and maintain routes between the vehicular nodes, making these protocols highly desirable for VANETs. Further a novel classification methodology of the protocols under study along with a comparative analysis depicting their similarity and dissimilarities has been presented.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122345921","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692105
R. Murugesh, Aravind Hanumanthaiah, Ullas Ramanadhan, Nirmala Vasudevan
Wireless sensor networks (WSNs) are often deployed remotely; hence, typical disposable chemical batteries with limited lifetimes may not be suitable for powering the network. In such cases, photovoltaic (PV) systems that generate electricity from sunlight can serve as a better alternative energy source. The intensity of sunlight varies over time, and thus the rates at which the batteries in the PV system get charged also vary. Monitoring the charging and discharging currents and voltages of the batteries enables us to modify the operation of the system in order to improve its overall efficiency. Moreover, it enables us to detect any fault in the solar panel, battery, or network node. We have designed an independent, low cost, ultra-low power microcontroller-based wireless solar power monitor that can be plugged easily into a PV system. The monitor measures the currents and voltages across the panels, batteries, and the load, and periodically transmits these values through an independent wireless interface to a control center for observation and analysis. We have performed a power analysis of the monitor and learnt about the power consumption in its various states. The use of this power monitor should extend the overall life of the PV system and also minimize power failures in the WSN nodes powered by the PV system. This paper reports about the design of the power monitor as well as the results of our analyses.
{"title":"Designing a Wireless Solar Power Monitor for Wireless Sensor Network Applications","authors":"R. Murugesh, Aravind Hanumanthaiah, Ullas Ramanadhan, Nirmala Vasudevan","doi":"10.1109/IADCC.2018.8692105","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692105","url":null,"abstract":"Wireless sensor networks (WSNs) are often deployed remotely; hence, typical disposable chemical batteries with limited lifetimes may not be suitable for powering the network. In such cases, photovoltaic (PV) systems that generate electricity from sunlight can serve as a better alternative energy source. The intensity of sunlight varies over time, and thus the rates at which the batteries in the PV system get charged also vary. Monitoring the charging and discharging currents and voltages of the batteries enables us to modify the operation of the system in order to improve its overall efficiency. Moreover, it enables us to detect any fault in the solar panel, battery, or network node. We have designed an independent, low cost, ultra-low power microcontroller-based wireless solar power monitor that can be plugged easily into a PV system. The monitor measures the currents and voltages across the panels, batteries, and the load, and periodically transmits these values through an independent wireless interface to a control center for observation and analysis. We have performed a power analysis of the monitor and learnt about the power consumption in its various states. The use of this power monitor should extend the overall life of the PV system and also minimize power failures in the WSN nodes powered by the PV system. This paper reports about the design of the power monitor as well as the results of our analyses.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624892","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692119
T. Sharma, Nitya Kritin Valivati, Arvind Puthige, Unnikrishnan Hari
This paper aims to develop a method to extract 3D information from surrounding space in real time and to develop a control system to track a target object continuously. We used two cameras and utilized the concepts of ray optics, epipolar geometry and image processing to identify the target and find its world coordinates with reference to the cameras.
{"title":"Object Position Estimation Using Stereo Vision","authors":"T. Sharma, Nitya Kritin Valivati, Arvind Puthige, Unnikrishnan Hari","doi":"10.1109/IADCC.2018.8692119","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692119","url":null,"abstract":"This paper aims to develop a method to extract 3D information from surrounding space in real time and to develop a control system to track a target object continuously. We used two cameras and utilized the concepts of ray optics, epipolar geometry and image processing to identify the target and find its world coordinates with reference to the cameras.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182828","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692095
Lokesh Jain, R. Katarya
In today human life, a social network plays a significant role in the user’s decision-making. In the social network, an opinion leader is a critical person who influences the behavior of the person with their own knowledge and skills. The major contribution of this paper is to recommend an advance approach to discover the opinion leader in the social network using fuzzy logic and trust generation model. In the first step, we evaluate the fuzzy trust rules based on the user’s trust. In the next step, these fuzzy trust rules apply to the online social network and then the de-fuzzification process applied to find out the trust value for each user and at last, identify the top-N user according to their prominence value that directly used to obtain their trust value for each user. We demonstrate our approach on the synthesized dataset and show the result that is better than the standard Social network analysis measures with respect to accuracy, precision, F1-score, and recall.
{"title":"Identification of opinion leader in online social network using fuzzy trust system","authors":"Lokesh Jain, R. Katarya","doi":"10.1109/IADCC.2018.8692095","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692095","url":null,"abstract":"In today human life, a social network plays a significant role in the user’s decision-making. In the social network, an opinion leader is a critical person who influences the behavior of the person with their own knowledge and skills. The major contribution of this paper is to recommend an advance approach to discover the opinion leader in the social network using fuzzy logic and trust generation model. In the first step, we evaluate the fuzzy trust rules based on the user’s trust. In the next step, these fuzzy trust rules apply to the online social network and then the de-fuzzification process applied to find out the trust value for each user and at last, identify the top-N user according to their prominence value that directly used to obtain their trust value for each user. We demonstrate our approach on the synthesized dataset and show the result that is better than the standard Social network analysis measures with respect to accuracy, precision, F1-score, and recall.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131499054","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692092
Sai Satyanarayana Reddy Seelam, Shrawan Kumar, Chand M Gopi, Reddy T. Raghunadha
The Internet is growing rapidly with huge amount of data mainly through social media. Most of the text in the World Wide Web is anonymous. In recent days, knowing the details of the anonymous text is the hot research area to the research community. Author Profiling is one such area attracted by the several researchers to know the information about the anonymous text. Author Profiling is a technique of predicting the demographic characteristics like gender, age and location of the authors by analyzing their written texts. The field of Stylometry is one area used by the researchers to discriminate the authors style of writing. In Author Profiling approaches the researchers proposed various types of stylistic features to distinguish the authors style of writing. The accuracies of demographic characteristics of the authors are not satisfactory when stylometric features were used. Later the researchers experimented with different types of term weight measures to improve the accuracies. In this work, we concentrated on two demographic characteristics such as gender and age. The experimentation is performed on 2014 PAN competition reviews corpus in English language. In this work, a new Profile specific Supervised Term Weight measure is proposed to predict the accuracy of gender and age of the author’s anonymous text. The experimental results of proposed measure is compared with different weight measures and identified that the proposed weight measure obtained best results for predicting gender and age.
{"title":"A New Term Weight Measure for Gender and Age Prediction of the Authors by analyzing their Written Texts","authors":"Sai Satyanarayana Reddy Seelam, Shrawan Kumar, Chand M Gopi, Reddy T. Raghunadha","doi":"10.1109/IADCC.2018.8692092","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692092","url":null,"abstract":"The Internet is growing rapidly with huge amount of data mainly through social media. Most of the text in the World Wide Web is anonymous. In recent days, knowing the details of the anonymous text is the hot research area to the research community. Author Profiling is one such area attracted by the several researchers to know the information about the anonymous text. Author Profiling is a technique of predicting the demographic characteristics like gender, age and location of the authors by analyzing their written texts. The field of Stylometry is one area used by the researchers to discriminate the authors style of writing. In Author Profiling approaches the researchers proposed various types of stylistic features to distinguish the authors style of writing. The accuracies of demographic characteristics of the authors are not satisfactory when stylometric features were used. Later the researchers experimented with different types of term weight measures to improve the accuracies. In this work, we concentrated on two demographic characteristics such as gender and age. The experimentation is performed on 2014 PAN competition reviews corpus in English language. In this work, a new Profile specific Supervised Term Weight measure is proposed to predict the accuracy of gender and age of the author’s anonymous text. The experimental results of proposed measure is compared with different weight measures and identified that the proposed weight measure obtained best results for predicting gender and age.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131441340","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692127
Bharti Saneja, Rinkle Rani
IoT and big data technologies have embarked the modern data science. As nowadays lots of data have been generated from wireless sensors connected via a network. Detecting anomalous events in this large amount of data is the topic undergoing intense study among researchers. Most of the existing solutions for the detection of anomalous events in big data are based on machine learning models. The proposed technique is a hybrid approach to detect outliers in weather sensor data. The approach comprises of three phases. Initially, for handling big data efficiently, dimensionality reduction is performed in the first phase. In the second phase, the detection of anomalous events is done using multiple classifiers. Finally in the third phase, for final classification, the results of the different classifiers are combined. With the aid of the proposed approach, we can extract the meaningful information from a complex dataset. It can be perceived from the experimental results that the proposed approach outperforms the various state-of-the-art algorithms for outlier detection.
{"title":"A Hybrid Approach for Outlier Detection in Weather Sensor Data","authors":"Bharti Saneja, Rinkle Rani","doi":"10.1109/IADCC.2018.8692127","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692127","url":null,"abstract":"IoT and big data technologies have embarked the modern data science. As nowadays lots of data have been generated from wireless sensors connected via a network. Detecting anomalous events in this large amount of data is the topic undergoing intense study among researchers. Most of the existing solutions for the detection of anomalous events in big data are based on machine learning models. The proposed technique is a hybrid approach to detect outliers in weather sensor data. The approach comprises of three phases. Initially, for handling big data efficiently, dimensionality reduction is performed in the first phase. In the second phase, the detection of anomalous events is done using multiple classifiers. Finally in the third phase, for final classification, the results of the different classifiers are combined. With the aid of the proposed approach, we can extract the meaningful information from a complex dataset. It can be perceived from the experimental results that the proposed approach outperforms the various state-of-the-art algorithms for outlier detection.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129283580","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692088
D. S. Reddy, D. Rajesh Reddy, R. Usha, Ankit Chaudhary, SS Solanki
Imaging from space involves certain complications which are quite different from airborne platforms such as MAVs, UAVs and drones. All these platforms require mathematical models to represent the geometry of image acquisition and further georeferencing the acquired image. Conventionally, a Rigorous Sensor Model (RSM) involving mission critical parameters and a sequence of rotations serves the purpose, alternately Rational Functional Models (RFM) are developed which empirically mimics RSM to certain degree of acceptable accuracy. In this paper, a machine learning approach is proposed for georeferencing of satellite images and compares the results with RFM and RSM.
{"title":"A Machine Learning Approach to Georeferencing","authors":"D. S. Reddy, D. Rajesh Reddy, R. Usha, Ankit Chaudhary, SS Solanki","doi":"10.1109/IADCC.2018.8692088","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692088","url":null,"abstract":"Imaging from space involves certain complications which are quite different from airborne platforms such as MAVs, UAVs and drones. All these platforms require mathematical models to represent the geometry of image acquisition and further georeferencing the acquired image. Conventionally, a Rigorous Sensor Model (RSM) involving mission critical parameters and a sequence of rotations serves the purpose, alternately Rational Functional Models (RFM) are developed which empirically mimics RSM to certain degree of acceptable accuracy. In this paper, a machine learning approach is proposed for georeferencing of satellite images and compares the results with RFM and RSM.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120840341","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692126
H. Palo, Sangeet Sagar
The work attempts to characterize and classify speech emotions using the spectrogram. Initially, it extracts the individual Red, Green, and Blue parameters from the raw speech spectrogram image of every individual emotional utterance. Further, it computes the statistical parameters of individual RGB components to characterize the chosen emotional states. The utterances of anger, happiness, neutral, and sad emotional states from the standard Berlin (EMO-DB) database has been used for this purpose. The individual statistical R, G, and B spectrogram parameters are found to be different within an emotion as well as across emotional states. Thus, these values have been used as different feature sets to classify the designated emotional states using the popular Multilayer Perceptron Neural Network (MLPNN).
{"title":"Characterization and Classification of Speech Emotion with Spectrograms","authors":"H. Palo, Sangeet Sagar","doi":"10.1109/IADCC.2018.8692126","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692126","url":null,"abstract":"The work attempts to characterize and classify speech emotions using the spectrogram. Initially, it extracts the individual Red, Green, and Blue parameters from the raw speech spectrogram image of every individual emotional utterance. Further, it computes the statistical parameters of individual RGB components to characterize the chosen emotional states. The utterances of anger, happiness, neutral, and sad emotional states from the standard Berlin (EMO-DB) database has been used for this purpose. The individual statistical R, G, and B spectrogram parameters are found to be different within an emotion as well as across emotional states. Thus, these values have been used as different feature sets to classify the designated emotional states using the popular Multilayer Perceptron Neural Network (MLPNN).","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589067","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692137
S. Yadav, Aman Jain, Deepti Singh
Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.
{"title":"Early Prediction of Employee Attrition using Data Mining Techniques","authors":"S. Yadav, Aman Jain, Deepti Singh","doi":"10.1109/IADCC.2018.8692137","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692137","url":null,"abstract":"Bill Gates was once quoted as saying, \"You take away our top 20 employees and we [Microsoft] become a mediocre company\". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770173","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 : 2018-12-01DOI: 10.1109/IADCC.2018.8692096
Sweety Sharma, N. Mittal
Wireless sensor network (WSN) communication has gathered a lot of attention of research scholars due to its various features such as high wireless data transmission. A large number of techniques have been developed till now in order to achieve an energy efficient network. The clustering and cluster head selection is the major and difficult task to perform in a network. LEACH serves as a basic for rest of the energy efficient clustering protocols. This study considers the LEACH-Mobile Fuzzy (LEACH-MF) as base for developing the proposed work. Fuzzy Inference System (FIS) with LEACH along with threshold based data transmission concept is developed in this work. The major objective of this work is to utilize the allotted energy to sensor nodes in an effective way. The proposed model is parted in two forms i.e. Modified Parameter-LEACH-MF (MP-LEACH-MF) and Limited Communication-LEACH-MF (LC-LEACH-MF). LC-LEACH-MF is a reactive protocol whereas the former one is periodic. In order to assure the performance efficiency of the proposed work, the parameters such as Packet Delivery Ratio (PDR), Last Node Dead (LND), Half Node Dead (HND), First Node Dead (FND), Energy Consumption of the network are evaluated and along with this a comparison analysis has been done with traditional LEACH, LEACH–Mobile (LEACH-M), LEACH-MF. After analyzing the obtained results it is concluded that the LC-LEACH-MF outnumbers the rest of the traditional energy efficient clustering techniques.
无线传感器网络(WSN)通信以其无线数据传输能力强等特点受到了研究学者的广泛关注。为了实现高效节能的网络，迄今为止已经开发了大量的技术。聚类和簇头选择是网络中最主要也是最困难的任务。LEACH是其他节能聚类协议的基础。本研究将leach -移动模糊(LEACH-MF)作为开展所提出工作的基础。本文开发了基于LEACH的模糊推理系统(FIS)和基于阈值的数据传输概念。本工作的主要目的是有效地利用分配给传感器节点的能量。该模型分为两种形式，即修改参数-浸出- mf (MP-LEACH-MF)和有限通信-浸出- mf (LC-LEACH-MF)。LC-LEACH-MF是一种反应性协议，而LC-LEACH-MF是一种周期性协议。为了保证所提工作的性能效率，评估了网络的包传送率(PDR)、最后节点死亡(LND)、半节点死亡(HND)、第一节点死亡(FND)、网络能耗等参数，并与传统LEACH、LEACH- mobile (LEACH- m)、LEACH- mf进行了比较分析。通过对所得结果的分析，得出了LC-LEACH-MF优于其他传统的节能聚类技术的结论。
{"title":"An Improved LEACH-MF Protocol to Prolong Lifetime of Wireless Sensor Networks","authors":"Sweety Sharma, N. Mittal","doi":"10.1109/IADCC.2018.8692096","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692096","url":null,"abstract":"Wireless sensor network (WSN) communication has gathered a lot of attention of research scholars due to its various features such as high wireless data transmission. A large number of techniques have been developed till now in order to achieve an energy efficient network. The clustering and cluster head selection is the major and difficult task to perform in a network. LEACH serves as a basic for rest of the energy efficient clustering protocols. This study considers the LEACH-Mobile Fuzzy (LEACH-MF) as base for developing the proposed work. Fuzzy Inference System (FIS) with LEACH along with threshold based data transmission concept is developed in this work. The major objective of this work is to utilize the allotted energy to sensor nodes in an effective way. The proposed model is parted in two forms i.e. Modified Parameter-LEACH-MF (MP-LEACH-MF) and Limited Communication-LEACH-MF (LC-LEACH-MF). LC-LEACH-MF is a reactive protocol whereas the former one is periodic. In order to assure the performance efficiency of the proposed work, the parameters such as Packet Delivery Ratio (PDR), Last Node Dead (LND), Half Node Dead (HND), First Node Dead (FND), Energy Consumption of the network are evaluated and along with this a comparison analysis has been done with traditional LEACH, LEACH–Mobile (LEACH-M), LEACH-MF. After analyzing the obtained results it is concluded that the LC-LEACH-MF outnumbers the rest of the traditional energy efficient clustering techniques.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213267","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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