{"title":"Low-Complex & Low-Cost Hardware Modelling of DCVNS Scheme for IoT Applications","authors":"P. Majumder, Punyasha Chatterjee, A. Ghosh","doi":"10.1109/PhDEDITS56681.2022.9955293","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955293","url":null,"abstract":"In this paper, a low-complexity and low-cost hard-ware model of a novel source encoding scheme termed Dualmessage Compression with Variable Null Symbol (DCVNS) is presented. Our recommended DCVNS approach addresses the process of interleaving of message bits originating from different sources, or from different sensing times of the same source. This interleaved message’s two successive bits are encoded and rendered by one of four symbol values, leading in a twofold reduction in the overall message length. Because Silent Communication is being used, the transmitter is maintained in deep-sleep state throughout the interim time of the high frequent dominant symbol appearing in the source-coded message during transmission. Our proposed design shows the hardware detail about the selection of the most dominating symbol which is dynamic in nature. Furthermore, the transmitter employs lowcost hybrid modulation/demodulation features integrating noncoherent FSK and ASK, designed to work in low-power and cost-effective transmission mode. Our proposed model is extremely suitable for circumstances in which a receiver node collects temporal or spatial data from two sources and transfers it to a sink node in most of the smart IoT applications.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133733547","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}
{"title":"Key generation in Cryptography using graph labeling techniques","authors":"Dhanyashree, Shivapriya. P, K. Meera","doi":"10.1109/PhDEDITS56681.2022.9955279","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955279","url":null,"abstract":"Key generation for Cryptographic algorithms is a challenging task. The Triple DES algorithm is one such important algorithm and this extended abstract deals with the concept of key generation for this algorithm using the path coloring of a star graph.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116807637","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}
{"title":"DAAL: A Deep Aggregated Assemble Learning Model for detecting Epileptic patients from EEG","authors":"Sricheta Parui, Uttam Ghosh, Puspita Chatterjee, Deborsi Basu","doi":"10.1109/PhDEDITS56681.2022.9955308","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955308","url":null,"abstract":"In this study, we developed a Deep Aggregated Assemble Learning(DAAL) model to diagnose Epilepsy that uses two-step learning and generates the final prediction utilizing the output predictions of the level 0 classifier model. In level 0 CNN, RNN and ANN model has been used, and then a prediction algorithm has been used which predicts the final output from each of the probability vector coming from each model.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133334645","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}
{"title":"Radio mean labeled paths in Cryptography","authors":"M. Saraswathi, K. Meera","doi":"10.1109/PhDEDITS56681.2022.9955298","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955298","url":null,"abstract":"Graph coloring or labeling is an NP-complete problem. The labeling technique in the scope of this paper is radio mean labeling. We integrate the radio mean labeling of graphs with the encryption/decryption process using matrices. An intruder can easily crack the secret message if the matrix or its inverse is known. The unique radio mean number of a graph is used to construct the key matrix for encryption. The inverse of this matrix is then the matrix for decryption. Out of all graphs of a given order, graphs isomorphic to path graphs have the maximum diameter. Since the mathematical constraint associated with the radio mean labeling of any given graph depends solely on the graph’s order and diameter, deriving the radio mean number of paths is difficult as order increases. Hence, we choose path graphs for constructing the key matrix for encryption.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441547","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}
{"title":"Impact of Maximum Hops Per Cycle On Network-on-Chip Router using Express Bypass Channels","authors":"Monika Katta, T. K. Ramesh, J. Plosila","doi":"10.1109/PhDEDITS56681.2022.9955262","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955262","url":null,"abstract":"We investigate the effect of maximum hops per cycle in a Network-on-Chip (NoC) router which uses Express Bypass Channels (EBC). To reduce transmission latency, NoC with EBC sends all bypass requests and flits at the same time. The flits are allowed to traverse both one-and two-dimension paths without getting latched in any of the routers. According to the simulation results, the transmission latency is reduced significantly even when only two nodes are permitted to be skipped using EBC.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024365","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}
{"title":"An algorithm to find a dominating set that secures any connected graph G","authors":"Nayana P G, R. R. Iyer","doi":"10.1109/PhDEDITS56681.2022.9955299","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955299","url":null,"abstract":"A social network or any computer network is vulnerable to an attack on any one of its nodes. For the network to function efficiently and without delays, we need to protect the node under attack and ensure that the node functions even after the attack has occurred. A set of nodes is a secure dominating set if it can monitor every other node of the network and which can take over the functionality of a failing node in an emergency. We propose an algorithm to find the secure dominating set in any connected network or graph. As a case study, we examine a network of CCTV cameras.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131014796","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}
{"title":"An Application of Radio Geometric Mean labeling in Encoding and Decoding","authors":"B. T. Manjunath, K. Meera","doi":"10.1109/PhDEDITS56681.2022.9955274","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955274","url":null,"abstract":"Graph theory is an ocean of NP-hard problems, which can be easily adapted to cryptography. Keys for cryptographic algorithms are lengthy number sequences that are generated by random number generators. These generators follow a uniform distribution that is easy to retrieve by hackers, as any number has an equal chance of being generated. Using graph labeling techniques, one can generate these keys in a more efficient and faster way. In this work, technique involves a labeling type called Radio geometric mean labeling and Radio geometric mean labeled graph is used as the cipher graph. The receiver receives the encoded text in the form of vertex or edge sequence of the cipher graph, which is not easy for an intruder to hack, as the labeling technique itself is an NP-hard problem.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663525","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}
{"title":"Application of Co-Secure Domination in Sierpinski Networks","authors":"M. P., R. R. Iyer","doi":"10.1109/PhDEDITS56681.2022.9955305","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955305","url":null,"abstract":"Security control is crucial in protecting a network from natural and human-made disasters. The theory of graph domination and its variants play a significant role in identifying the sensitive locations in a network where mobile guards have to be placed to protect the network nodes from the risk of attack. To ensure the network’s security, the physically weak or the attacked mobile guard at the node is to be replaced immediately by another guard so that the consequent set of guards continues to protect the network. The set of such nodes thus formed is a co-secure dominating set of the network, and least cardinality of the co-secure dominating set is the co-secure domination number. This article determines a tight bound for the co-secure domination number on the Generalized Sierpinski cycle graphs and Generalized Sierpinski complete graphs.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967350","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}
{"title":"Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes","authors":"Josephine Eskaline Joyce, Shoney Sebastian","doi":"10.1109/PhDEDITS56681.2022.9955300","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955300","url":null,"abstract":"Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116101844","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}
{"title":"Machine Learning Implementation for Refactoring Prediction","authors":"Rasmita Panigrahi, S. K. Kuanar, L. Kumar","doi":"10.1109/PhDEDITS56681.2022.9955297","DOIUrl":"https://doi.org/10.1109/PhDEDITS56681.2022.9955297","url":null,"abstract":"Refactorings improve the internal organization of object-oriented software project without altering the functionality to address the problem of architectural degradation. The application of refactoring leads to increased software quality and maintainability. However, finding refactoring chances is a complex topic that affects both developers and researchers. In a recent study, machine learning methods demonstrated significant promise for resolving this issue. Model refactoring prevents erosion of the program architecture at an early stage of the model-driven engineering paradigm-compliant software development project. However, difficulties such as variable data set distribution and the availability of duplicate and irrelevant variables hamper the efficacy of refactoring prediction models. We aim to develop a model for refactoring prediction using several machine learning classifiers, data sampling techniques, and feature selection techniques.","PeriodicalId":373652,"journal":{"name":"2022 IEEE 4th PhD Colloquium on Emerging Domain Innovation and Technology for Society (PhD EDITS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484807","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}