Pub Date : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243390
Lamiaa A. Abou elazm, S. Ibrahim, Mohamed G. Egila, H. Shawkey, M. Elsaid, W. El-shafai, F. A. Abd El-Samie
The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.
{"title":"Hardware Implementation of Cancellable Biometric Systems","authors":"Lamiaa A. Abou elazm, S. Ibrahim, Mohamed G. Egila, H. Shawkey, M. Elsaid, W. El-shafai, F. A. Abd El-Samie","doi":"10.1109/I-SMAC49090.2020.9243390","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243390","url":null,"abstract":"The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127198883","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243600
R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, L. Sairamesh
Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.
{"title":"IoT based Classification Techniques for Soil Content Analysis and Crop Yield Prediction","authors":"R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, L. Sairamesh","doi":"10.1109/I-SMAC49090.2020.9243600","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243600","url":null,"abstract":"Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510850","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243326
Lokesh Singh, P. Gupta, R. Katarya, Pragyat Jayvant
As the innovation age encourages individuals to communicate their emotions via web-based media locales like Facebook, Instagram, Twitter, and so on. Numerous individuals share their musings and thoughts step by step in twitter similar to other tweets. This is considered as an exciting and attractive way to express ourselves as inspections are increasing bit by bit, due to which rundown of surveys are necessary for the job where summed up of text is required to give helpful data from the huge number of surveys. It is exceptionally hard for an individual to extricate helpful information or sum up it from the extremely enormous record. This paper focuses on comparison and analysis of different sentiment analysis techniques. It gives a far-reaching review about the recent and past examinations on sentiment analysis, challenges, and approaches for future angles.
{"title":"Twitter data in Emotional Analysis - A study","authors":"Lokesh Singh, P. Gupta, R. Katarya, Pragyat Jayvant","doi":"10.1109/I-SMAC49090.2020.9243326","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243326","url":null,"abstract":"As the innovation age encourages individuals to communicate their emotions via web-based media locales like Facebook, Instagram, Twitter, and so on. Numerous individuals share their musings and thoughts step by step in twitter similar to other tweets. This is considered as an exciting and attractive way to express ourselves as inspections are increasing bit by bit, due to which rundown of surveys are necessary for the job where summed up of text is required to give helpful data from the huge number of surveys. It is exceptionally hard for an individual to extricate helpful information or sum up it from the extremely enormous record. This paper focuses on comparison and analysis of different sentiment analysis techniques. It gives a far-reaching review about the recent and past examinations on sentiment analysis, challenges, and approaches for future angles.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692373","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243348
Bindu Madhuri Cheekati, Roje Spandana Rajeti
Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.
{"title":"Telugu handwritten character recognition using deep residual learning","authors":"Bindu Madhuri Cheekati, Roje Spandana Rajeti","doi":"10.1109/I-SMAC49090.2020.9243348","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243348","url":null,"abstract":"Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"28 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132027153","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243358
Shipra Singh, Kaptan Singh, A. Saxena
With the evolution of technology, usage of the Internet of Things (IoT) has grown immensely. IoT is now getting used in homes, traffic controls, mode of commute, and many more. IoT brings different physical entities together virtually. IoT is revolutionizing how different devices communicate with each other. With IoT, in the future, the transformation of the physical devices into smart devices with additional capabilities, are increased in ease of usage. Due to the advancement of wireless communication over the internet exposed to these devices and objects including several threats and security vulnerabilities. This paper discusses different issues related to the security and privacy of the Internet of Things along with proposed countermeasures.
{"title":"Security Domain, Threats, Privacy issues in the Internet of Things (IoT): A Survey","authors":"Shipra Singh, Kaptan Singh, A. Saxena","doi":"10.1109/I-SMAC49090.2020.9243358","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243358","url":null,"abstract":"With the evolution of technology, usage of the Internet of Things (IoT) has grown immensely. IoT is now getting used in homes, traffic controls, mode of commute, and many more. IoT brings different physical entities together virtually. IoT is revolutionizing how different devices communicate with each other. With IoT, in the future, the transformation of the physical devices into smart devices with additional capabilities, are increased in ease of usage. Due to the advancement of wireless communication over the internet exposed to these devices and objects including several threats and security vulnerabilities. This paper discusses different issues related to the security and privacy of the Internet of Things along with proposed countermeasures.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216422","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243454
Koduru Suresh, G. V. Kumar
With the advancement of Internet of Things, the utilization factor has shifted from computers to every gadget used by humans. It is important that the end user services across different applications are required to be offered at one-stop with cost effective solutions. Today sensors are installed in applications that need to be monitored in real-time but it must be taken into consideration with respect to the cost and maintenance involved with physical sensors. Hence there is need to depend on virtual internet of things for real time monitoring of applications with minimal use of physical sensors. Here virtual internet of things is coined as the use of various cloud technologies that control and monitor the applications using API's with minimal use of physical sensors. In this work a plug and play framework is devised based on VIoT (virtual internet of things) and IoT for implementing on applications that are dependent on environmental monitoring. Later based on the defined framework two use cases are illustrated. Here actuation and automation are done based on certain imposed constraints to respond as per inputs and outputs generated by various devices installed in real time applications.
{"title":"Integrated Cloud Internet of Things for Realtime Applications","authors":"Koduru Suresh, G. V. Kumar","doi":"10.1109/I-SMAC49090.2020.9243454","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243454","url":null,"abstract":"With the advancement of Internet of Things, the utilization factor has shifted from computers to every gadget used by humans. It is important that the end user services across different applications are required to be offered at one-stop with cost effective solutions. Today sensors are installed in applications that need to be monitored in real-time but it must be taken into consideration with respect to the cost and maintenance involved with physical sensors. Hence there is need to depend on virtual internet of things for real time monitoring of applications with minimal use of physical sensors. Here virtual internet of things is coined as the use of various cloud technologies that control and monitor the applications using API's with minimal use of physical sensors. In this work a plug and play framework is devised based on VIoT (virtual internet of things) and IoT for implementing on applications that are dependent on environmental monitoring. Later based on the defined framework two use cases are illustrated. Here actuation and automation are done based on certain imposed constraints to respond as per inputs and outputs generated by various devices installed in real time applications.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958307","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243564
A. Amudha, M. Ramkumar, V. Vijayalakshmi, G. Emayavaramban, V. Mansoor, S. Divyapriya, P. Nagaveni
The present work describes modelling, simulation and configuration of the Dual Loop Controller for the CCD Microgrid Operation of the Positive Performance Super Lift Luo Converters (POESLLC). The incorporation of green energy sources makes DC microgrids exciting. The super lifting Luo Converters is key in connecting a DC source in the DC microgrids, due to the low power rates of these DC sources. Traditional boost converters, buck converters and buck boost converters produced lower voltage transmission gain and ribs. POESLLC is recommended for DC micro grid applications in order to solve these issues. Dual loop controller regulates the output voltage and the inductor current of the converter The PI controller is the external voltage loop for the load voltage controlling and the P controller is the internal current loop for the inductor current control. By designing the MATLAB / SIMULINK model under various operating conditions, the dual loop controller of POESLLC is checked. Such converters could be included in electronic power courses to support graduate students and graduate students in their exploration of their knowledge in the field of application.
{"title":"Modeling, Simulation and Design of Luo Converter for DC Micro Grid Application","authors":"A. Amudha, M. Ramkumar, V. Vijayalakshmi, G. Emayavaramban, V. Mansoor, S. Divyapriya, P. Nagaveni","doi":"10.1109/I-SMAC49090.2020.9243564","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243564","url":null,"abstract":"The present work describes modelling, simulation and configuration of the Dual Loop Controller for the CCD Microgrid Operation of the Positive Performance Super Lift Luo Converters (POESLLC). The incorporation of green energy sources makes DC microgrids exciting. The super lifting Luo Converters is key in connecting a DC source in the DC microgrids, due to the low power rates of these DC sources. Traditional boost converters, buck converters and buck boost converters produced lower voltage transmission gain and ribs. POESLLC is recommended for DC micro grid applications in order to solve these issues. Dual loop controller regulates the output voltage and the inductor current of the converter The PI controller is the external voltage loop for the load voltage controlling and the P controller is the internal current loop for the inductor current control. By designing the MATLAB / SIMULINK model under various operating conditions, the dual loop controller of POESLLC is checked. Such converters could be included in electronic power courses to support graduate students and graduate students in their exploration of their knowledge in the field of application.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396084","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243342
N. MohanaSundaram
Artificial neural networks are powerful data computational models which have the capability of representation of complex input-output relationships of physical systems. Further they could perform “intelligent” tasks that performed by the human brain. In this work a predictive nonlinear model of an internal combustion engine is simulated using Elman recurrent neural work, Cascade Forward Neural Network and a Feed Forward Neural Network to predict the operational parameters engine torque and the nitrous oxide emissions. The parameters fuel rate and speed of the engine serve as input. A standard bench mark dataset is used for training the Elman neural network. The simulations results confirm that the Neural Network models can map the nonlinear input -output relationships in an effective manner. All the three different neural networks could map the input-output relationship and the test results confirm that Elman Neural Network has the best performance.
{"title":"Non Linear Predictive Modelling for IC Engine Using Artificial Neural Network","authors":"N. MohanaSundaram","doi":"10.1109/I-SMAC49090.2020.9243342","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243342","url":null,"abstract":"Artificial neural networks are powerful data computational models which have the capability of representation of complex input-output relationships of physical systems. Further they could perform “intelligent” tasks that performed by the human brain. In this work a predictive nonlinear model of an internal combustion engine is simulated using Elman recurrent neural work, Cascade Forward Neural Network and a Feed Forward Neural Network to predict the operational parameters engine torque and the nitrous oxide emissions. The parameters fuel rate and speed of the engine serve as input. A standard bench mark dataset is used for training the Elman neural network. The simulations results confirm that the Neural Network models can map the nonlinear input -output relationships in an effective manner. All the three different neural networks could map the input-output relationship and the test results confirm that Elman Neural Network has the best performance.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116367620","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243481
Abeer Mahmood Hassan, Saad Talib Hasson
After the spread of the coronavirus. It became necessary to presenting artificial intelligence algorithms to study social contact between people. On the other hand, social network considers as a complex network. The situation became urgent to reduce these networks and reduce links between people inside each network while maintaining full controllability of the networks to reduce the number of real contact and minimize the cost of the networks especially in these bad days, which the world lives in due to the spread of epidemics, viruses, and infection. This paper aims to present a model that computes controllability on real contact people's networks to reduce touches and keep the network in a controlled manner based on three ways. a structural controllability approach is using by applying the Bipartite-graph algorithm and the Hopcroft-Karp algorithm. In order to determine the drive nodes that must be controlled to gain full control of the network, normal driver nodes and weak driver nodes. Weak driver nodes can remove to enhance controllability. As a result, network controllability increased by 12.8%, reduced rate of drive nodes, reduced the chance of spread of infection by 67%.
{"title":"A Controllability Algorithm to Minimize the Spreading chance of COVID-19 in Individual Networks","authors":"Abeer Mahmood Hassan, Saad Talib Hasson","doi":"10.1109/I-SMAC49090.2020.9243481","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243481","url":null,"abstract":"After the spread of the coronavirus. It became necessary to presenting artificial intelligence algorithms to study social contact between people. On the other hand, social network considers as a complex network. The situation became urgent to reduce these networks and reduce links between people inside each network while maintaining full controllability of the networks to reduce the number of real contact and minimize the cost of the networks especially in these bad days, which the world lives in due to the spread of epidemics, viruses, and infection. This paper aims to present a model that computes controllability on real contact people's networks to reduce touches and keep the network in a controlled manner based on three ways. a structural controllability approach is using by applying the Bipartite-graph algorithm and the Hopcroft-Karp algorithm. In order to determine the drive nodes that must be controlled to gain full control of the network, normal driver nodes and weak driver nodes. Weak driver nodes can remove to enhance controllability. As a result, network controllability increased by 12.8%, reduced rate of drive nodes, reduced the chance of spread of infection by 67%.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118904","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 : 2020-10-07DOI: 10.1109/I-SMAC49090.2020.9243377
Madhavi Dave, Jyotika Doshi, Harshal A. Arolkar
Internet of Things (IoT) is a developing technology from recent decades which works on heterogeneous machines and technology interoperate without human involvement. The challenge of IoT automation is to coordinate autonomous devices, sensors and real-time applications for seamless communication. Many organizations and researchers have demonstrated various solutions for a wide range of IoT devices. However, the available system provides its specific IoT infrastructure, devices, APIs and data formats, which leads to interoperability issues. A new data interoperability solution MQTT-CoAP Interconnector (MCI) is designed for interoperability of MQTT and CoAP protocols at the application layer of IoT. It works as a bridge between the local message of MQTT and remote message of CoAP with data parsing capacity. MCIis the lightweight interoperability solution designed with consideration of resource-constrained devices of the IoT ecosystem. As compared to available open-source solutions, MCI performs better considering communication parameters like transmission time, throughput, latency and packet loss. Thus, the MCI interoperability solution can benefit to resolve one of the major issues of the IoT ecosystem.
{"title":"MQTT- CoAP Interconnector: IoT Interoperability Solution for Application Layer Protocols","authors":"Madhavi Dave, Jyotika Doshi, Harshal A. Arolkar","doi":"10.1109/I-SMAC49090.2020.9243377","DOIUrl":"https://doi.org/10.1109/I-SMAC49090.2020.9243377","url":null,"abstract":"Internet of Things (IoT) is a developing technology from recent decades which works on heterogeneous machines and technology interoperate without human involvement. The challenge of IoT automation is to coordinate autonomous devices, sensors and real-time applications for seamless communication. Many organizations and researchers have demonstrated various solutions for a wide range of IoT devices. However, the available system provides its specific IoT infrastructure, devices, APIs and data formats, which leads to interoperability issues. A new data interoperability solution MQTT-CoAP Interconnector (MCI) is designed for interoperability of MQTT and CoAP protocols at the application layer of IoT. It works as a bridge between the local message of MQTT and remote message of CoAP with data parsing capacity. MCIis the lightweight interoperability solution designed with consideration of resource-constrained devices of the IoT ecosystem. As compared to available open-source solutions, MCI performs better considering communication parameters like transmission time, throughput, latency and packet loss. Thus, the MCI interoperability solution can benefit to resolve one of the major issues of the IoT ecosystem.","PeriodicalId":432766,"journal":{"name":"2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414094","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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