The document discusses the development of a NoSQL query processing system for wireless ad-hoc and sensor networks. It begins by reviewing existing SQL-based query processing systems like TinyDB and TikiriDB and noting their limitations for wireless sensor networks that lack consistent connectivity. The main objective is described as transforming the relational database model to a NoSQL model for better performance and scalability. The design of the NoSQL query processing system is then outlined, including components like NoSQL queries, a lexical analyzer and parser, query processor, data packets, mesh routing, and using the Redis NoSQL database architecture. Implementation details are also provided about generating NoSQL grammars, implementing data packets, and executing queries on sensor motes.
Secret keys and the packets transportation for privacy data forwarding method...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Secret keys and the packets transportation for privacy data forwarding method...eSAT Journals
Abstract The Cloud computing is the process of storing the data in the Remote server. This process doesn‘t speak much about confidentiality and robustness of the data. To improve the security and confidentiality the uploaded file from a data owner is splitted into multiple packets and stored in multiple cloud servers. These packets are encrypted using the primary key. These different keys are also distributed in multiple key servers. User id is appended for verification. If the data owner forwards the file then the keys are verified for the data access. In this we are proposing sending the secret key as SMS to the shared or forwarded nodes for the process of proper Security. This technique integrates the concepts of encryption, encoding and forwarding. Keywords-cloud computing, encryption, storage system
CYBER INFRASTRUCTURE AS A SERVICE TO EMPOWER MULTIDISCIPLINARY, DATA-DRIVEN S...ijcsit
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural
services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed
storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Key Management Schemes for Secure Communication in Heterogeneous Sensor NetworksIDES Editor
Hierarchical Sensor Network organization is
widely used to achieve energy efficiency in Wireless Sensor
Networks(WSN). To achieve security in hierarchical WSN,
it is important to be able to encrypt the messages sent
between sensor nodes and its cluster head. The key
management task is challenging due to resource constrained
nature of WSN. In this paper we are proposing two key
management schemes for hierarchical networks which
handles various events like node addition, node compromise
and key refresh at regular intervals. The Tree-Based
Scheme ensures in-network processing by maintaining some
additional intermediate keys. Whereas the CRT-Based
Scheme performs the key management with minimum
communication and storage at each node.
Secret keys and the packets transportation for privacy data forwarding method...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Secret keys and the packets transportation for privacy data forwarding method...eSAT Journals
Abstract The Cloud computing is the process of storing the data in the Remote server. This process doesn‘t speak much about confidentiality and robustness of the data. To improve the security and confidentiality the uploaded file from a data owner is splitted into multiple packets and stored in multiple cloud servers. These packets are encrypted using the primary key. These different keys are also distributed in multiple key servers. User id is appended for verification. If the data owner forwards the file then the keys are verified for the data access. In this we are proposing sending the secret key as SMS to the shared or forwarded nodes for the process of proper Security. This technique integrates the concepts of encryption, encoding and forwarding. Keywords-cloud computing, encryption, storage system
CYBER INFRASTRUCTURE AS A SERVICE TO EMPOWER MULTIDISCIPLINARY, DATA-DRIVEN S...ijcsit
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural
services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed
storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Key Management Schemes for Secure Communication in Heterogeneous Sensor NetworksIDES Editor
Hierarchical Sensor Network organization is
widely used to achieve energy efficiency in Wireless Sensor
Networks(WSN). To achieve security in hierarchical WSN,
it is important to be able to encrypt the messages sent
between sensor nodes and its cluster head. The key
management task is challenging due to resource constrained
nature of WSN. In this paper we are proposing two key
management schemes for hierarchical networks which
handles various events like node addition, node compromise
and key refresh at regular intervals. The Tree-Based
Scheme ensures in-network processing by maintaining some
additional intermediate keys. Whereas the CRT-Based
Scheme performs the key management with minimum
communication and storage at each node.
From Physical to Virtual Wireless Sensor Networks using Cloud Computing IJORCS
In the modern world, billions of physical sensors are used for various dedications: Environment Monitoring, Healthcare, Education, Defense, Manufacturing, Smart Home, Agriculture Precision and others. Nonetheless, they are frequently utilized by their own applications and thereby snubbing the significant possibilities of sharing the resources in order to ensure the availability and performance of physical sensors. This paper assumes that the immense power of the Cloud can only be fully exploited if it is impeccably integrated into our physical lives. The principal merit of this work is a novel architecture where users can share several types of physical sensors easily and consequently many new services can be provided via a virtualized structure that allows allocation of sensor resources to different users and applications under flexible usage scenarios within which users can easily collect, access, process, visualize, archive, share and search large amounts of sensor data from different applications. Moreover, an implementation has been achieved using Arduino-Atmega328 as hardware platform and Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud. Then this private Cloud has been connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. The testing was successful at 80%. The recommendation for future work would be to improve the effectiveness of virtual sensors by applying optimization techniques and other methods.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
Survey on cloud backup services of personal storageeSAT Journals
Abstract In widespread cloud environment cloud services is tremendously growing due to large amount of personal computation data. Deduplication process is used for avoiding the redundant data. A cloud storage environment for data backup in personal computing devices facing various challenge, of source deduplication for the cloud backup services with low deduplication efficiency. Challenges facing in the process of deduplication for cloud backup service are-1)Low deduplication efficiency due to exclusive access to large amount of data and limited system resources of PC based client site.2)Low data transfer efficiency due to transferring deduplicate data from source to backup server are typically small but that can be often across the WAN. Keywords- Cloud computing, Deduplication, cloud backup, application awareness
Micro services Architecture with Vortex -- Part IAngelo Corsaro
Microservice Architectures — which are the norm in some domains — have recently received lots of attentions in general computing and are becoming the mainstream architectural style to develop distributed systems. As suggested by the name, the main idea behind micro services is to decompose complex applications in, small, autonomous and loosely coupled processes communicating through a language and platform independent API. This architectural style facilitates a modular approach to system-building.
This webcast will (1) introduce the main principles of the Microservice Architecture, (2) showcase how the Global Data Space abstraction provided by Vortex ideally support thee microservices architectural pattern, and (3) walk you through the design and implementation of a micro service application for a real-world use case.
Mutual query data sharing protocol for public key encryption through chosen-c...IJECEIAES
In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
3
rd International Conference on Signal Processing, VLSI Design & Communication
Systems (SVC 2022) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of on Signal Processing, VLSI Design &
Communication Systems. The aim of the conference is to provide a platform to the
researchers and practitioners from both academia as well as industry to meet and share
cutting-edge development in the field.
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
From Physical to Virtual Wireless Sensor Networks using Cloud Computing IJORCS
In the modern world, billions of physical sensors are used for various dedications: Environment Monitoring, Healthcare, Education, Defense, Manufacturing, Smart Home, Agriculture Precision and others. Nonetheless, they are frequently utilized by their own applications and thereby snubbing the significant possibilities of sharing the resources in order to ensure the availability and performance of physical sensors. This paper assumes that the immense power of the Cloud can only be fully exploited if it is impeccably integrated into our physical lives. The principal merit of this work is a novel architecture where users can share several types of physical sensors easily and consequently many new services can be provided via a virtualized structure that allows allocation of sensor resources to different users and applications under flexible usage scenarios within which users can easily collect, access, process, visualize, archive, share and search large amounts of sensor data from different applications. Moreover, an implementation has been achieved using Arduino-Atmega328 as hardware platform and Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud. Then this private Cloud has been connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. The testing was successful at 80%. The recommendation for future work would be to improve the effectiveness of virtual sensors by applying optimization techniques and other methods.
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
Survey on cloud backup services of personal storageeSAT Journals
Abstract In widespread cloud environment cloud services is tremendously growing due to large amount of personal computation data. Deduplication process is used for avoiding the redundant data. A cloud storage environment for data backup in personal computing devices facing various challenge, of source deduplication for the cloud backup services with low deduplication efficiency. Challenges facing in the process of deduplication for cloud backup service are-1)Low deduplication efficiency due to exclusive access to large amount of data and limited system resources of PC based client site.2)Low data transfer efficiency due to transferring deduplicate data from source to backup server are typically small but that can be often across the WAN. Keywords- Cloud computing, Deduplication, cloud backup, application awareness
Micro services Architecture with Vortex -- Part IAngelo Corsaro
Microservice Architectures — which are the norm in some domains — have recently received lots of attentions in general computing and are becoming the mainstream architectural style to develop distributed systems. As suggested by the name, the main idea behind micro services is to decompose complex applications in, small, autonomous and loosely coupled processes communicating through a language and platform independent API. This architectural style facilitates a modular approach to system-building.
This webcast will (1) introduce the main principles of the Microservice Architecture, (2) showcase how the Global Data Space abstraction provided by Vortex ideally support thee microservices architectural pattern, and (3) walk you through the design and implementation of a micro service application for a real-world use case.
Mutual query data sharing protocol for public key encryption through chosen-c...IJECEIAES
In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
3
rd International Conference on Signal Processing, VLSI Design & Communication
Systems (SVC 2022) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of on Signal Processing, VLSI Design &
Communication Systems. The aim of the conference is to provide a platform to the
researchers and practitioners from both academia as well as industry to meet and share
cutting-edge development in the field.
In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-aService (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, dataintensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.
Database Management in Different Applications of IOTijceronline
In the recent years, the Internet of Things (IoT) is considered as a part of the Internet of future and makes it possible for connecting various smart objects together through the Internet. The use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. This paper discusses the different Databases used for different applications in IOT.
ACCELERATED DEEP LEARNING INFERENCE FROM CONSTRAINED EMBEDDED DEVICESIAEME Publication
Hardware looping is a feature of some processor instruction sets whose hardware can repeat the body of a loop automatically, rather than requiring software instructions which take up cycles (and therefore time) to do so. Loop Unrolling is a loop transformation technique that attempts to advance a program's execution speed to the detriment of its twofold size, which is a methodology known as space–time tradeoff. A convolutional neural network is created with simple loops, with hardware looping, with loop unrolling and with both hardware looping and loop unrolling, and a comparison is made to evaluate the effectiveness of hardware looping and loop unrolling. The hardware loops alone will add to a cycle check decline, while the mix of hardware loops and dot product instructions will decrease the clock cycle tally further. The CNN is simulated on Xilinx Vivado 2021.1 running on Zync-7000 FPGA.
Cloud computing and Software defined networkingsaigandham1
This is my Graduate defense presentation. I have interest in various topics like cloud computing and software defined networking. This slides includes the research of various researchers on cloud computing and SDN, presented their work as my comprehensive exam.
This volume of the Open Datacenter Interoperable Network (ODIN) describes software defined networking (SDN) and OpenFlow. SDN is used to simplify network control and management, automate network virtualization services, and provide a platform from which to build agile ....
NoSQL Databases: An Introduction and Comparison between Dynamo, MongoDB and C...Vivek Adithya Mohankumar
The research paper covers the consolidated interpretation of NoSQL systems, on the basis of performance, scalability and data aggregation, and compares the types of NoSQL databases based on their implementation and maintenance.
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applicat...Mydbops
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applications by Bhanu Jamwal, Head of Solution Engineering, PingCAP at the Mydbops Opensource Database Meetup 14.
This presentation discusses the challenges in choosing the right database for modern applications, focusing on MySQL alternatives. It highlights the growth of new applications, the need to improve infrastructure, and the rise of cloud-native architecture.
The presentation explores alternatives to MySQL, such as MySQL forks, database clustering, and distributed SQL. It introduces TiDB as a distributed SQL database for modern applications, highlighting its features and top use cases.
Case studies of companies benefiting from TiDB are included. The presentation also outlines TiDB's product roadmap, detailing upcoming features and enhancements.
Similar to No sql query processing system for wireless ad hoc and sensor networks (20)
We are a company that delivers value to our customers by lowering costs with digital marketing and increasing the efficiency of campaigns and their conversions. Using the most advanced artificial intelligence models in the neuro-marketing perspective, we have been able to predict the effectiveness of a marketing campaign before it is published. After its publication, we evaluated the campaign, segmenting the public according to the standard extracted from each market segment, delivering information for strategic and efficient management.
Aplicações de Alto Desempenho com JHipster Full StackJoão Gabriel Lima
Palestra apresentada no Meetup da comunidade Sou Java Campinas sobre o JHipster, desmistificando muitas premissas e validando aquilo que temos de melhor no mercado de tecnologias Java.
Palestra apresentada no FEMUG-PE de Setembro! Mostro o ARKit Framework e algumas aplicações muito interessantes do uso de realidade aumentada. Por fim, apresento o React-Native-ArKit, biblioteca para que você, desenvolvedor React Native, também utilize o ARkit em seus projetos de forma facilitada e muito prática.
Com a crescente onda de dados gerados, está cada vez mais claro que tecnologias de preparação e processamento de Big Data precisam se apoiar em Inteligência Artificial. Nesta palestra apresento o estado da arte em Big Data e IA, mostro claramente a relação entre esses tópicos, dando um direcionamento de como esses conceitos devem ser aplicados. Foi mostrado um estudo de caso da Operação Serenata de Amor, proposta por cientistas de dados e jornalistas para o combate à corrupção no Brasil.
O modelo de regressão é então usado para prever o resultado de uma variável dependente desconhecida, dados os valores das variáveis independentes.
Nesta aula, mostro um passo a passo com a bordage teórica e prática de como fazer regressão linear utilizando o WEKA
Nesta apresentação, foram discutidos os principais casos que ocorreram entre 2015 e 2016, detalhando como cada um foi executado, as técnicas utilizadas e principalmente, dicas de como proteger-se delas.
Mineração de Dados com RapidMiner - Um Estudo de caso sobre o Churn Rate em...João Gabriel Lima
Nesta palestra, vamos trabalhar uma abordagem passo a passo de como construir um modelo de classificação, para identificar os padrões de clientes de uma empresa de telefonia que cancelaram o serviço, de modo que a operadora possa prever o risco de cancelamento e iniciar um trabalho para evitar que isso aconteça.
Mineração de dados com RapidMiner + WEKA - ClusterizaçãoJoão Gabriel Lima
Nesta apresentação, apresento um passo a passo prático de como clusterizar e mais importante que isso, como interpretar os resultados aplicando isso para auxiliar a tomada de decisão.
No final temos um exercício de fixação muito interessante que nos dá a oportunidade de aplicar os conhecimentos adquiridos.
jgabriel.ufpa@gmail.com
Nessa apresentação apresento ambas arquiteturas e mostro que ao invés de escolher entre uma e outra, podemos tirar o que há de melhor em cada e utilizá-las de forma limpa, simples e objetiva.
Game of data - Predição e Análise da série Game Of Thrones a partir do uso de...João Gabriel Lima
Nesta apresentação mostro um estudo realizado pela universidade de Munique que visa prever a probabilidade de um personagem morrer na próxima temporada de acordo com 24 características pré-selecionadas
Apresentação sobre o aplicativo e-Trânsito cidadão: https://play.google.com/store/apps/details?id=com.huddle3.etranstitocidadaov2
Contendo notícias e provendo consulta sobre o IPVA
[Estácio - IESAM] Automatizando Tarefas com Gulp.jsJoão Gabriel Lima
Tutorial sobre Gulpjs
Especialização em Desenvolvimento Web - Instituto de Estudos Superiores da Amazônia
Neste tutorial apresento a facilidade proporcionada por automatizadores e abordo especificamente o [Gulp.js](gulpjs.com)
Palestra apresentada no JsDay Recife 2015, onde mostro uma visão geral sobre o cenário da Internet das Coisas com Javascript. Primeiramente destaco os conceitos gerais, em seguida justifico o uso de javascript, além disso, mostro as principais ferramentas, bibliotecas e API's. Cito os principais projetos na área e mostro um projeto na prática implementado em javascript, utilizando a tecnologia bluetooth para contrução de smarthomes, provendo a comunicação entre o dispositivo controlador e o smartphone do usuário.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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2. NoSQL Query Processing System for Wireless Ad-hoc and Sensor Networks 79
Here we show the example of TinyDB query; Relational Database Management Systems (RDBMS).
Considering on RDBMS, those databases are design for
SELECT COUNT ( * ) FROM sensors AS s, recentLight AS
guarantee ACID properties. But NoSQL databases did not
rl WHERE rl.nodeid=s.nodeid AND s.light < rUight
guarantee ACID properties. They basically design for the
SAMPLE PERIOD lOs; [2]
performances and scalability. Normally NoSQL databases are
B. TikiriDB suitable for large set of data.
TikiriDB [3] is another well known database abstraction for Working with large set of data using a table based database
WASNs. TikiriDB is the database abstraction layer for Contiki systems, it needs lot of resources to store such massive data
operating system. Comparing TikiriDB with TinyDB, and the operations are time consuming. With regards to
TikiriDB support shared WASNs. NoSQL databases handle massive amount of data is much
TikiriDB also provide a SQL query interface called easier, and the performances are very fast when comparing
TikiriSQL to query the sensor network [3]. It is much more RDBMS. The only limitation of NoSQL is the memory and
similar to conventional query language apart from additional the processing speed. NoSQL database systems uses key,
syntax to comply with sensor network environment. SELECT value pair to store data so, if you want to keep your data in a
temp, humid FROM sensors SAMPLE PERIOD 2 FOR 10; [3] persistent state and have access to them, then this would be an
This query returns node id, humidity level, and temperature ideal database system.
level in every 2 second intervals for duration of 10 seconds Currently there are several NoSQL database management
from all the available sensors nodes in the sensor network. systems available. Facebook's Cassandra, LinkedIn's Project
The results appended to the table as they are arriving to the Voldemort, Google's BigTable and Amazon's Dynamo are
user. Thus the resulting table dynamically expands according some of them. Chordless, CouchDB, Db4o, GT.M, Hbase,
to time. Hypertable, Memcachedb, Mnesia, MongoDB and Redis are
some popular open source NoSQL projects as well.
I) Client with TikiriSQL Library: The client side
functionalities of the TikiriDB is included in the TikiriSQL III. DESIGN OF NoSQL DATABASE ABSTRACTION
library and used by a user program. It provides functions to In this section we discuss the design of NoSQL database
issue SQL queries by the user program, parses the queries and abstraction for WSN from a higher level architecture to
sends them to the Serial Forwarder (SF). TikiriSQL library detailed design.
returns data to the user program which is received from the SF.
Its' main tasks are, 1) Accept queries from the user program, 2) A. Architecture of NoSQL Database Abstraction
Parse the query and put it to a manageable format, 3) If there Figure 1 illustrates the overall design architecture of this
are any syntactic and semantic errors, it returns warnings to research including all the main components, which discussed
the user. in detail in the sub sections 1) to 5).
The possible semantic errors are SELECT queries with As displayed in Figure 1, NoSQL database abstraction
undefined field names, EVENT queries with undefined event consists of eight main components which are directly
names. All the available field names and event names are kept contributing to the database abstraction. These eight main
in an XML configuration file. 1) If no errors, send this new components are; front end NoSQL query, Lexical analyzer
formatted query to the serial forwarder, 2) Returns the query and parser, Query processor, Data packet, Serial forwarder
10 returned from the SF to the user program, 3) This query ID plug-in, Mesh routing protocol, Executing query In sensor
can be used to issue a STOP query to stop an executing query
identified by the query ID 4) Put the data received from the SF
to data structures and make it available to the user for
manipulation.
C. Cougar @
Cougar is another well known approach to in-network
query processing in sensor networks. It supports a platform for
testing query processing techniques over ad-hoc sensor
networks. Cougar mainly has three-tier architecture. It consists ..,
of,
• A query proxy
• Front-end components
• A graphical user interface
Fig. I: Higher level design architecture
Cougar is designed for in-network query processing. In
network processing reduces energy consumption and increase motes and finally the Redis NoSQL database.
lifetime of sensor network significantly compared to 1) NoSQL Query: NoSQL queries play a major role in
traditional centralized data extraction and analysis. Thus one this database abstraction. This is a novel approach for sensor
of the main roles of the query proxy when processing user network database abstractions, because existing database
queries is to perform in-network processing [4]. abstractions consists of traditional SQL queries for querying
sensor networks. We designed NoSQL query syntaxes for
D. NoSQL querying sensor networks. Most of these queries are similar to
NoSQL means Not Only SQL. The concept of NoSQL RedisDB NoSQL queries, because we adopt RedisDB
starts from 1998. NoSQL databases are differing from architecture for our abstraction. Designed NoSQL queries are,
1sl & 2nd September 2011 The International Conference on Advances in rCT for Emerging Regions - ICTer2011
3. 80 T.A.M.C. Thantriwatte, and c.1. Keppetiyagama
• Select Query: Appropriate keyword followed by Redis key space is divided to 4096 hash slots. In order to
relevant key achieve that, different nodes hold a subset of hash slots. All
• Join Query: Appropriate keyword followed by relevant the nodes are connected to each other and the functionalities
key followed by valid set condition for key of each and every node is equivalent.
• Range Query: Appropriate keyword followed by
IV. IMPLEMENTATION
relevant key followed by valid range condition
This section discusses the implementation of the NoSQL
• Ranking Data: Appropriate keyword followed by key
database abstraction for wireless ad-hoc and sensor networks
and relevant member name
in more detail. The focus is on how the NoSQL queries are
• Get the key of members: Appropriate keyword followed executed in actual sensor motes and results are sent back to
by relevant key the base station. Following sub sections A, B, C and 0 will
NoSQL query interface is linked with a lexer. The lexer describe the implementation procedure of our NoSQL
syntactically analyses the NoSQL query according to the rules database abstraction for Contiki operating system.
defined. Following sub section 2) introduces the A. NoSQL Grammar Implementation
functionalities of the NoSQL lexer and the parser.
We have used ANTLR tool to define our NoSQL grammar
2) Lexical Analyser and Parser: Input NoSQL query definitions. Fist we defined the NoSQL grammars for our
pass to lexical analyser, and it read the query characters from sensor network and then the ANTLR tool generates the
the input stream which is tokenized. These token identified appropriate lexical analyser and parser for it. Using ANTLR
using the predefined NoSQL lexer rules in the grammar file we can test and generate the parse tree of our NoSQL queries
which are implemented using regular expressions. easily. Implemented NoSQL queries are;
The main functionality of lexer is, generating a stream of • GET temp SET temp 2 FOR 100;
tokens according to the NoSQL query and passing it to a
• GET humid SET temp 2 FOR 100;
parser for syntax analysis. It also ignores the whitespaces and
• ZRANK temp 2.0;
comments.
• GET humid SET humid < 45;
Parsing is the second stage of NoSQL grammar validation
according to the predefined NoSQL grammar rules. Parser • ZRANK light 5.0;
checks the syntax and semantics of the NoSQL query and 8. Data Packet Implementation
generate the parse tree. If parser can find syntactic or semantic
We had implemented the data packet according to the
errors in the query it produces an error message. Finally the
parsed NoSQL query from the lexical analyzer and parser.
parser produces a C code file according to the NoSQL query
The size of the data packet is 128 bits and it is divided into
and which is passed to the query processor.
following categories.
3) Query Processor: The query processor processes the
C file generated by the parser and distinguishes the parts of
the query such as query type, relevant keys and other query
conditions. According to these query parts it generates query
id, query message header and query pay load. After that the
processed query is considered as a data packet, which is ready I , f -.L 1 -r (
8 bits 8 bits 8 bits 8 bits 16 bits 32 bits
to be routed and executed in sensor motes.
Fig. 2: Structure of data packet
4) Mesh Routing: In wireless mesh network concept,
communication is done by using the ad-hoc mode, also called
as peer-to-peer. Nodes in a mesh network should be able to • No of fields: It mentions what are the fields we want to
discover each and every node and broadcast messages to all its sense from the sensor mote. According to the query
neighbors. relevant fields are set to the data packet.
In mesh networks we used hybrid routing protocol to pass • No of expressions: This indicates the number of
our queries from the base station to destinations. Hybrid expressions that are appeared in the SET clause.
protocols consist of both proactive and reactive routing • For example GET temp SET temp> 10 2 FOR 10; In
protocols. Initially routing starts in proactive mode and then this query "temp> 10" will goes to No of expression
move to reactive flooding in the network. In this research we section of the data packet.
used the inbuilt mesh routing protocol which is in Rime stack • Input buffer 10: This defines the data source for the
in Contiki [7] operating system for our network routing query. If this is zero, direct data from sensors is used
purpose. for the query. None zero positive integer represents the
5) Redis Architecture: RedisDB is an open source, input buffer identification number. Usually, input
advanced key-value storage [5]. It is often referred to as a data buffer is storage medium such as SO card.
structure server since keys can contain Strings, Hashes, List, • Output buffer 10: This defines the location where the
Sets and Sorted-sets. Redis protocol consists of a network results of a query are stored. If this is zero, results are
layer where clients connect to port 6379. In Redis server there sent to the node who issued the query. None zero
are different kinds of replies according to client requests. They positive integer represents the out buffer identification
are error reply, integer reply, bulk replies, multi-bulk replies number. Usually, output buffer is storage medium such
and nil elements in multi-bulk replies. as SO card.
The International Conference on Advances in ICT for Emerging Regions - ICTer20 l1 1sl & 2nd September 2011
4. NoSQL Query Processing System for Wireless Ad-hoc and Sensor Networks 81
• Epoch duration: This is used to define the time duration • ZINTERSTORE destination numkeys key [key ...]:
between two executions of the query. The value of Intersect multiple Sorted-sets and store the resulting
this should be greater than zero. The duration is Sorted-set in a new key.
specified in seconds. Epoch duration is set in the SET
clause of the query. V. RESULTS
• Number of epochs: This defines the number of query In this section, we evaluate the implementation of NoSQL
executions. Zero causes to execute the query infinite query processing system for wireless ad-hoc networks on the
number of times. top of Contiki operating system.
• Fields: Field consists with 16 bits. It consists of a A. Evaluation platform
unique id, result flag and operator. 3 bits are not used in We used two platforms to test and evaluate our system. One
field section. is COOJA [9] simulation platform while other is a hardware
• Expressions: Expression consists with 32 bits. Every platform. We often used COOJA simulation platform to
field had relevant expression. It consists with data develop and test our system, because testing with real sensor
which mapped to field id and operator. motes is little bit tricky part in sensor networks and most of
the system are platform independent. After successful testing
C. Implementation of Serial Forwarder with COOJA simulation platform, we test our system with real
We did not implement the serial forwarder. Existing sensor motes as well.
TikiriDB [3] 0.2 release there was a serial forwarder which we
B. Performance Analysis
can directly plug into our NoSQL database abstraction. So we
used that serial forwarder plug-in to sent data packet to the In performance analysis we analyzed the query execution
time of TikiriDB [3] database abstraction and our newly
network.
designed NoSQL database abstraction. We used following
Once data packets arrive at the serial forwarder it assigns a
queries to evaluate the execution time of both database
unique query ID to each and every data packet. It also stores
abstractions. For TikiriDB database abstraction,
the query ID and the client ID mapping in a table which is
located in its' memory. After that it sends the query to the • SELECT temp FROM sensors SAMPLE PERIOD 2
network with the query ID and ID for the serial forwarder. FOR 10;
When data arrives from the sensor network, serial forwarder • SELECT humid FROM sensors SAMPLE PERIOD 2
searches the relevant client 10 from its' table and sends the FOR 10;
relevant data to the base stations. All the implementations of
serial forwarder are done by using C++ language and the For NoSQL database abstraction,
serial forwarder COOJA [9] plug-in which was written by • GET temp SET temp 2 FOR 10;
using Java language. • GET humid SET humid 2 FOR 10;
Routing is another important part in sensor networks. Here
we used inbuilt mesh routing protocol which is in Rime stack We get the execution time of queries by changing the
in Contiki [7] operating system for routing. sample period of both SQL and NoSQL queries. Following
figure shows the query execution time of TikiriDB database
abstraction and NoSQL database abstraction with respect to
D. Implementation of RedisDB Plug-in
sample periods. The X and Y axis represent sample period
We have done the implementation of Redis backend in variation in seconds and response time in milliseconds
iterative manner. RedisDB supports different data structures
respectively.
such as Strings, Hashes, Lists, Sets and Sorted-sets. First we
have implemented our backend data structure using Strings 350000
and evaluate the performances of our NoSQL database 300000
250000
abstractions. After that we done the implementation using
200000
other data structures as well and evaluates them. These 150000 • NoSQL
evaluations are mentioned under section v-co According to 100000
.SQL
those evaluations, finally we implemented with Sorted-sets. 50000
o
Sorted-set implementation of our NoSQL database
abstraction basically works with two values called key and 2FOR 2FOR 2FOR 2FOR 2FOR
10 100 1000 10000 100000
member. In this implementation we mapped sensing field of a
query as key and sensing values as members. According to
Fig. 3: NoSQL against SQL query execution time
these key and member values we can use following NoSQL
queries in our database abstraction. According to the Figure 3, it was observed that for a shorter
time periods, both database abstractions show the same
• ZADD key score member: Add a member to a Sorted
performances, but when the time period increases,
set, or update its score if it already exists.
performances of NoSQL database abstraction get better. That
• ZCARD key: Get the number of members in a Sorted means execution time of NoSQL query in sensor networks get
set. lesser time than execution time of SQL query in sensor
• ZCOUNT key min max: Count the members in a networks. Reason for that performance bottleneck in SQL
Sorted-set with scores within the given values. database abstraction is, processing time of SQL query.
• ZlNCRBY key increment member: Increment the score According to above results we can conclude processing
of a member in a Sorted-set. NoSQL queries are much more efficient than processing SQL
1sl & 2nd September 20 I I The International Conference on Advances in ICT for Emerging Regions - ICTer20 11
5. 82 TAM.C. Thantriwatte, and C.I. Keppetiyagama
queries in sensor networks. This cause saves energy in sensor backend data structure is the suitable approach for NoSQL
motes. database abstraction for wireless ad-hoc networks.
C. Runtime Analysis VI. CONCLUSION
In section IV-D mentioned that we used different data In this paper we have first reviewed the existing database
structures as backend of our NoSQL database abstraction. We abstractions in WSN. Then we discussed about the issues
tested the backend data structures against the time related to existing database abstractions in wireless ad-hoc
complexities of NoSQL queries. We obtained following networks. Then we discussed the importance of using NoSQL
results. as the underlying database abstraction for ad-hoc networks.
• SET query analysis According to the experimental results we conclude that
NoSQL queries perform better when using longer sample
periods. This shows higher scalability for the networks.
TABLE I Secondly we evaluated the query performance with regard to
the time complexity of different data structures, and it showed
that using Sorted-sets we can achieve best results.
O(log(N»
VII. FUTURE WORK
• GET query analysis This research has evolved from relational database model to
NoSQL database model. Therefore query optimization is not
good as the relational model. Query optimization related to
TABLE II this research is to be done in near future. In addition optimized
routing protocols, security layers can also be incorporated to
this model.
O(log(N»
ACKNOWLEDGMENTS
From the above tables it can be observed that Sorted-sets The authors would like to express their sincere thanks to the
show the best time complexity. After analysing Sorted-set people in SCORE lab of University of Colombo School of
furthermore for different NoSQL query operations, we got the Computing. We would also like to thank the support given by
following results. Primal Wijesekara from University of British Columbia, K.C
Hewage from UCSC, Nayanagith M. Laxman from UCSC and
A.P. Sayakkara from UCSC.
TABLE III
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The International Conference on Advances in ICT for Emerging Regions - ICTer2011 15t & 2nd September 2011