Pub Date : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824893
Sankari Subbiah, S. Ramya, G. Parvathy Krishna, S. Nayagam
Visual impairment can be termed as blindness or vision loss. This impairment causes many difficulties in their day-to-day activities such as in reading, walking, socializing, and driving. The white cane is considered to be the symbol of freedom, independence, and confidence. The proposed smart cane is designed with obstacle detection module, heat detection, water detection, light detection, pit and staircase detection using InfraRed (IR) sensor, GPS (Global Positioning System), and GSM(Global System for Mobile) which helps them to accomplish his/her daily tasks with ease. The obstacle detection module uses ultrasonic range along with camera to detect the obstacles which intimates that the obstacle is detected and also about what the obstacle is? We use Raspberry Pi to inform the impaired user about what the object is and it is sent as a voice message through headset. The GPS is used to identify the current location of the person which is sent as a text message and also as a voice message through headset. Traffic signals are identified by using Raspberry Pi and intimate the user through headset whether to wait for the signal or move. All these facilities are not at all possible if the visually challenged person has misplaced the cane somewhere else. For this purpose, we have fixed an alarm in the smart cane which is connected to their mobile phones. This alarm helps them to find their smart cane if they misplaced it.
{"title":"Smart Cane For Visually Impaired Based On IOT","authors":"Sankari Subbiah, S. Ramya, G. Parvathy Krishna, S. Nayagam","doi":"10.1109/ICCCT2.2019.8824893","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824893","url":null,"abstract":"Visual impairment can be termed as blindness or vision loss. This impairment causes many difficulties in their day-to-day activities such as in reading, walking, socializing, and driving. The white cane is considered to be the symbol of freedom, independence, and confidence. The proposed smart cane is designed with obstacle detection module, heat detection, water detection, light detection, pit and staircase detection using InfraRed (IR) sensor, GPS (Global Positioning System), and GSM(Global System for Mobile) which helps them to accomplish his/her daily tasks with ease. The obstacle detection module uses ultrasonic range along with camera to detect the obstacles which intimates that the obstacle is detected and also about what the obstacle is? We use Raspberry Pi to inform the impaired user about what the object is and it is sent as a voice message through headset. The GPS is used to identify the current location of the person which is sent as a text message and also as a voice message through headset. Traffic signals are identified by using Raspberry Pi and intimate the user through headset whether to wait for the signal or move. All these facilities are not at all possible if the visually challenged person has misplaced the cane somewhere else. For this purpose, we have fixed an alarm in the smart cane which is connected to their mobile phones. This alarm helps them to find their smart cane if they misplaced it.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129858675","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824969
P. Varalakshmi, B. Y. Sivashakthivadhani, B. L. Sakthiram
Agriculture plays a crucial role in the Indian economy. It not only provides food and raw material but also provides employment opportunities and also helps in monitoring gas exchange problems, rainfall percolation and microbial activity. Hence it is important to find a technique which will classify infectious plant from healthy plant, in order to cure the diseased plant at early stages and also improve the yield of the agriculture. An hardware model is built using the Raspberry Pi 3 to indicate to the farmer about the temperature, pressure and soil moisture level when it goes below or above a threshold values. A new classification technique is developed based on the convolutional neural network (CNN) to classify infectious plant from the healthy plant and its efficiency is compared with SVM,KNN classifier and random forest.
{"title":"Automatic Plant Escalation Monitoring System Using IoT","authors":"P. Varalakshmi, B. Y. Sivashakthivadhani, B. L. Sakthiram","doi":"10.1109/ICCCT2.2019.8824969","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824969","url":null,"abstract":"Agriculture plays a crucial role in the Indian economy. It not only provides food and raw material but also provides employment opportunities and also helps in monitoring gas exchange problems, rainfall percolation and microbial activity. Hence it is important to find a technique which will classify infectious plant from healthy plant, in order to cure the diseased plant at early stages and also improve the yield of the agriculture. An hardware model is built using the Raspberry Pi 3 to indicate to the farmer about the temperature, pressure and soil moisture level when it goes below or above a threshold values. A new classification technique is developed based on the convolutional neural network (CNN) to classify infectious plant from the healthy plant and its efficiency is compared with SVM,KNN classifier and random forest.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121887784","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824945
Nibi Maouriyan, A. Krishna
Water shortage is fast becoming one of the biggest crises of this century. The recent, exponential rise in adoption of the most disparate Internet of Things (IoT) devices and technologies has reached also Water supply chains, drumming up substantial research and innovation interest towards developing reliable, auditable and transparent traceability systems. Current IoT-based traceability and provenance systems for water supply chains are built on top of centralized infrastructures and this leaves room for unsolved issues and major concerns, including data integrity, tampering and single points of failure. Blockchains, the distributed ledger technology underpinning cryptocurrencies such as Bitcoin, represent a new and innovative technological approach to realizing decentralized trustless systems. This will also eliminate corruption due to unmaintained record of sources. Indeed, the inherent properties of this digital technology provide fault-tolerance, immutability, transparency and full traceability of the stored transaction records, as well as coherent digital representations of physical assets and autonomous transaction executions. This paper presents Aqua-chain, a fully decentralized, blockchain-based traceability solution for Water supply chain management, able to seamless integrate IoT devices producing and consuming digital data along the chain. To effectively assess Aqua-chain, first, we defined a classical use-case within the given vertical domain, namely from-supplier-to-buyer. Then, we developed and deployed such use-case, achieving traceability using blockchain implementation, Ethereum. Finally, we evaluated and compared the performance deployments, in terms of latency, CPU, and network usage, also highlighting its main pros and cons.
{"title":"AQUACHAIN -Water Supply-Chain management using Distributed Ledger Technology","authors":"Nibi Maouriyan, A. Krishna","doi":"10.1109/ICCCT2.2019.8824945","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824945","url":null,"abstract":"Water shortage is fast becoming one of the biggest crises of this century. The recent, exponential rise in adoption of the most disparate Internet of Things (IoT) devices and technologies has reached also Water supply chains, drumming up substantial research and innovation interest towards developing reliable, auditable and transparent traceability systems. Current IoT-based traceability and provenance systems for water supply chains are built on top of centralized infrastructures and this leaves room for unsolved issues and major concerns, including data integrity, tampering and single points of failure. Blockchains, the distributed ledger technology underpinning cryptocurrencies such as Bitcoin, represent a new and innovative technological approach to realizing decentralized trustless systems. This will also eliminate corruption due to unmaintained record of sources. Indeed, the inherent properties of this digital technology provide fault-tolerance, immutability, transparency and full traceability of the stored transaction records, as well as coherent digital representations of physical assets and autonomous transaction executions. This paper presents Aqua-chain, a fully decentralized, blockchain-based traceability solution for Water supply chain management, able to seamless integrate IoT devices producing and consuming digital data along the chain. To effectively assess Aqua-chain, first, we defined a classical use-case within the given vertical domain, namely from-supplier-to-buyer. Then, we developed and deployed such use-case, achieving traceability using blockchain implementation, Ethereum. Finally, we evaluated and compared the performance deployments, in terms of latency, CPU, and network usage, also highlighting its main pros and cons.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121103091","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 : 2019-02-01DOI: 10.1109/iccct2.2019.8824968
{"title":"ICCCT 2019 Front Matter","authors":"","doi":"10.1109/iccct2.2019.8824968","DOIUrl":"https://doi.org/10.1109/iccct2.2019.8824968","url":null,"abstract":"","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888213","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824915
J. Nandhini, T. Gnanasekaran
Cloud computing is a large set of logical computational resources accessible via internet. Cloud computing offers services to obtain coherence, scalability, economy subscale with maximum efficiency and resource optimization. Fault tolerance is the characteristic that enables the system to stay operating and adhere SLA even when the in the system faults and failures. For a system to be fault tolerant the interval of fault identification and removal must be minimum to follow the QoS requirements. virtualization in the Data center can assist in fault prediction that makes the system fault tolerant. A cloud simluator is an extensible tool to analyse, evaluate and measure the system performance of the cloud cloud applications to satisfy the QoS provisions. This paper deals with the survey of the various cloud simulators with emphasis on using CloudSim
{"title":"An Assessment Survey of Cloud Simulators for Fault Identification","authors":"J. Nandhini, T. Gnanasekaran","doi":"10.1109/ICCCT2.2019.8824915","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824915","url":null,"abstract":"Cloud computing is a large set of logical computational resources accessible via internet. Cloud computing offers services to obtain coherence, scalability, economy subscale with maximum efficiency and resource optimization. Fault tolerance is the characteristic that enables the system to stay operating and adhere SLA even when the in the system faults and failures. For a system to be fault tolerant the interval of fault identification and removal must be minimum to follow the QoS requirements. virtualization in the Data center can assist in fault prediction that makes the system fault tolerant. A cloud simluator is an extensible tool to analyse, evaluate and measure the system performance of the cloud cloud applications to satisfy the QoS provisions. This paper deals with the survey of the various cloud simulators with emphasis on using CloudSim","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133072636","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824930
M. Suresh Kumar, V. Soundarya, S. Kavitha, E. Keerthika, E. Aswini
In this paper we mainly focus on credit card fraud detection in real world. Here the credit card fraud detection is based on fraudulent transactions. Generally credit card fraud activities can happen in both online and offline. But in today’s world online fraud transaction activities are increasing day by day. So in order to find the online fraud transactions various methods have been used in existing system. In proposed system we use Random Forest Algorithm(RFA) for finding the fraudulent transactions and the accuracy of those transactions. This algorithm is based on supervised learning algorithm where it uses decision trees for classification of the dataset. After classification of dataset a confusion matrix is obtained. The performance of Random Forest Algorithm is evaluated based on the confusion matrix. The results obtained from processing the dataset gives accuracy of about 90%.
{"title":"Credit Card Fraud Detection Using Random Forest Algorithm","authors":"M. Suresh Kumar, V. Soundarya, S. Kavitha, E. Keerthika, E. Aswini","doi":"10.1109/ICCCT2.2019.8824930","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824930","url":null,"abstract":"In this paper we mainly focus on credit card fraud detection in real world. Here the credit card fraud detection is based on fraudulent transactions. Generally credit card fraud activities can happen in both online and offline. But in today’s world online fraud transaction activities are increasing day by day. So in order to find the online fraud transactions various methods have been used in existing system. In proposed system we use Random Forest Algorithm(RFA) for finding the fraudulent transactions and the accuracy of those transactions. This algorithm is based on supervised learning algorithm where it uses decision trees for classification of the dataset. After classification of dataset a confusion matrix is obtained. The performance of Random Forest Algorithm is evaluated based on the confusion matrix. The results obtained from processing the dataset gives accuracy of about 90%.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123981214","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824836
P. Nithyakani, A. Shanthini, Godwin Ponsam
A human acknowledgment and recognizable proof is viewed these days as an essential field of research. The most unique parts of human are the ear, odor, heartbeat, voice, the iris, periocular portion of eye, fingerprint, gait, sweat, face, etc,. Without the human interaction to identify a person is quite challenging with low resolution images. Gait recognition is one of the biometric technology which can be used to identify people without their knowledge. The proposed system uses Deep Convolutional Neural Network to extract the gait features of a person by training the neural network architecture with Gait Energy Image.
{"title":"Human Gait Recognition using Deep Convolutional Neural Network","authors":"P. Nithyakani, A. Shanthini, Godwin Ponsam","doi":"10.1109/ICCCT2.2019.8824836","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824836","url":null,"abstract":"A human acknowledgment and recognizable proof is viewed these days as an essential field of research. The most unique parts of human are the ear, odor, heartbeat, voice, the iris, periocular portion of eye, fingerprint, gait, sweat, face, etc,. Without the human interaction to identify a person is quite challenging with low resolution images. Gait recognition is one of the biometric technology which can be used to identify people without their knowledge. The proposed system uses Deep Convolutional Neural Network to extract the gait features of a person by training the neural network architecture with Gait Energy Image.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"42 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145882","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824815
Swagata B. Sarkar
Human emotion detection is an emerging field. The greater impact of emotional intelligence in day to day life than intelligent quotient has been proved by psychologists. Numerous psychological problems are coming up every day posing serious challenges. These can be solved only through proper analysis of emotions. Emotion analysis is a challenging task. Most of the time single emotion cannot be identified. Basic emotions are happy, sad, fear, anger, surprise and neutral. Fear and anger are the two dominating emotions which can cause health problems as well as mental disorder. The main focus in this paper is fear analysis using image and signal processing. In this paper, analysis of fear is made using image processing, fused facial image processing, Field Programmable Grid Array features of facial image, emotional speech processing and emotional analysis using physical parameters. Statistical feature extraction from both time and signal domain has been done. Features have also been extracted from Field Programmable Grid Array. Speech features have been extracted using Mel Frequency Cepstral Coefficients algorithm. Physical parameters which are directly related to human emotions are analysed by fuzzy analysis. Multimodal emotion analysis is done using feature level fusion. Feature level fusion is done by discrete wavelet transform and regression analysis. The features are finally classified using back propagation algorithm of conventional neural network and back propagation algorithm of convolution neural network in the domain of deep learning. Out of all emotions fear has sensitivity and specificity of 97.36% and 91.67% respectively. As against the sensitivity and specificity for only physical parameters and facial images are 58.62%, 79.41%, 81.25%, 47.62% respectively. Human fear also has been analysed from speech signal using modified Mel Frequency Cepstral Coefficients algorithm. Kaiser window works best for happiness, hamming window is good for boredom and fear, Hanning window is fit for disgust and anger, Bartlett window is good for sad emotion. Emotion detection by image fusion technique using conventional back propagation network as classifier, sensitivity and specificity are increased by 16.72% and 27.75 % respectively. Fear emotion is best classified by taking combined feature set other than single feature set like human emotional faces or physical parameters. It can also be well classified by deep neural network. The features for fear emotion can be extracted using modified Mel Frequency Cepstral Coefficients algorithm using Hamming window.
{"title":"Human Fear Analysis using Signal and Image Processing","authors":"Swagata B. Sarkar","doi":"10.1109/ICCCT2.2019.8824815","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824815","url":null,"abstract":"Human emotion detection is an emerging field. The greater impact of emotional intelligence in day to day life than intelligent quotient has been proved by psychologists. Numerous psychological problems are coming up every day posing serious challenges. These can be solved only through proper analysis of emotions. Emotion analysis is a challenging task. Most of the time single emotion cannot be identified. Basic emotions are happy, sad, fear, anger, surprise and neutral. Fear and anger are the two dominating emotions which can cause health problems as well as mental disorder. The main focus in this paper is fear analysis using image and signal processing. In this paper, analysis of fear is made using image processing, fused facial image processing, Field Programmable Grid Array features of facial image, emotional speech processing and emotional analysis using physical parameters. Statistical feature extraction from both time and signal domain has been done. Features have also been extracted from Field Programmable Grid Array. Speech features have been extracted using Mel Frequency Cepstral Coefficients algorithm. Physical parameters which are directly related to human emotions are analysed by fuzzy analysis. Multimodal emotion analysis is done using feature level fusion. Feature level fusion is done by discrete wavelet transform and regression analysis. The features are finally classified using back propagation algorithm of conventional neural network and back propagation algorithm of convolution neural network in the domain of deep learning. Out of all emotions fear has sensitivity and specificity of 97.36% and 91.67% respectively. As against the sensitivity and specificity for only physical parameters and facial images are 58.62%, 79.41%, 81.25%, 47.62% respectively. Human fear also has been analysed from speech signal using modified Mel Frequency Cepstral Coefficients algorithm. Kaiser window works best for happiness, hamming window is good for boredom and fear, Hanning window is fit for disgust and anger, Bartlett window is good for sad emotion. Emotion detection by image fusion technique using conventional back propagation network as classifier, sensitivity and specificity are increased by 16.72% and 27.75 % respectively. Fear emotion is best classified by taking combined feature set other than single feature set like human emotional faces or physical parameters. It can also be well classified by deep neural network. The features for fear emotion can be extracted using modified Mel Frequency Cepstral Coefficients algorithm using Hamming window.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121943347","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824992
V. Akila, T. Sheela
Reliable and trustful data aggregation is an important to be addressed in Wireless Sensor Networks (WSNs). In this paper, we proposed a new technique to protect key and data in data aggregation called Secure Data Aggregation to Preserve Data and Key Privacy in Wireless Sensor Networks with Multiple Sinks (SAPDKP). The multiple sinks concepts used in this method consume less energy in the computational and communicational overhead. The security issues such as data confidentiality, data integrity, data freshness and data authentication are preserved in this technique. It uses very simple technique to perform aggregation and encryption. The multiple sink nodes perform analysis to identify the distrustful group and retransmission of data takes place for that group. Our simulation result proved that implementation of SAPDKP decreases the communication overhead and energy consumption compared with existing work. The multiple sinks concept increases the reliability of data and also reduces the number of data transmission in the network.
{"title":"Secure Data Aggregation to Preserve Data and Key Privacy in Wireless Sensor Networks with Multiple Sinks","authors":"V. Akila, T. Sheela","doi":"10.1109/ICCCT2.2019.8824992","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824992","url":null,"abstract":"Reliable and trustful data aggregation is an important to be addressed in Wireless Sensor Networks (WSNs). In this paper, we proposed a new technique to protect key and data in data aggregation called Secure Data Aggregation to Preserve Data and Key Privacy in Wireless Sensor Networks with Multiple Sinks (SAPDKP). The multiple sinks concepts used in this method consume less energy in the computational and communicational overhead. The security issues such as data confidentiality, data integrity, data freshness and data authentication are preserved in this technique. It uses very simple technique to perform aggregation and encryption. The multiple sink nodes perform analysis to identify the distrustful group and retransmission of data takes place for that group. Our simulation result proved that implementation of SAPDKP decreases the communication overhead and energy consumption compared with existing work. The multiple sinks concept increases the reliability of data and also reduces the number of data transmission in the network.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131441152","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 : 2019-02-01DOI: 10.1109/ICCCT2.2019.8824953
M. Meenaloshini, J. Ilakkiya, P. Sharmila, J.C Sheffi Malar, S. Nithyasri
Internet of Things (IoT) plays an indispensable role in bridging the gap between all the day to day things to the networking system, and creates an ease to access all the un-internet things from any distant location. Adaption to the growth in the recent trends is inexorable for the people. With all the advancement in the technology, finding a particular place to park our automobile becomes an exasperating issue. In our work we have designed a Smart Car Parking System (SCPS) with the help of infrared sensor and a database based on application of Iot, which permits the driver to find the proximate parking slot, and gives the number of free places available in that respective parking zone. This ideology mainly focuses on diminishing the time involved in discovering the parking space and also it decreases the unwanted travelling, through filled parking slots in a parking arena. This will in turn reduce the consumption of fuel, which would reduce carbon footprints in our environment. Thus, this will pave way for an eco friendly surrounding.
{"title":"Smart Car Parking System in Smart Cities using IR","authors":"M. Meenaloshini, J. Ilakkiya, P. Sharmila, J.C Sheffi Malar, S. Nithyasri","doi":"10.1109/ICCCT2.2019.8824953","DOIUrl":"https://doi.org/10.1109/ICCCT2.2019.8824953","url":null,"abstract":"Internet of Things (IoT) plays an indispensable role in bridging the gap between all the day to day things to the networking system, and creates an ease to access all the un-internet things from any distant location. Adaption to the growth in the recent trends is inexorable for the people. With all the advancement in the technology, finding a particular place to park our automobile becomes an exasperating issue. In our work we have designed a Smart Car Parking System (SCPS) with the help of infrared sensor and a database based on application of Iot, which permits the driver to find the proximate parking slot, and gives the number of free places available in that respective parking zone. This ideology mainly focuses on diminishing the time involved in discovering the parking space and also it decreases the unwanted travelling, through filled parking slots in a parking arena. This will in turn reduce the consumption of fuel, which would reduce carbon footprints in our environment. Thus, this will pave way for an eco friendly surrounding.","PeriodicalId":445544,"journal":{"name":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116580593","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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