On Big Data Management In Internet Of Things

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On Big Data Management In Internet Of Things. Do you want the complete project material? Download the complete project topic and material (chapter 1-5) on Edustore.NG below.See below for the abstract, table of contents, list of figures, list of tables, list of appendices, list of abbreviations and chapter one, Literature Review, Methodology e.t.cPlease Note: This is a complete project work from, abstract, table of contents, chapters 1 to 5 with references and questionnaire you can only view half of the project. But if you need the complete project work Click Here To Place Your OrderAbstract on On Big Data Management In Internet Of ThingsThe Internet of Things (IoT) has generated a large amount of research interest across a widevariety of technical areas. These include the physical devices themselves, communicationsamong them, and relationships between them. One of the effects of ubiquitous sensorsnetworked together into large ecosystems has been an enormous flow of data supporting a widevariety of applications. In this work, we propose a new “IntelliFog-Cloud” approach to IoT BigData Management by leveraging mined historical intelligence from a Big Data platform andcombining it with real-time actionable events from IoT devices at the Fog layer to reduce actionlatency in IoT applications. This approach is demonstrated through an advertisement servicesimulation with VoltDB technology where advertisements are being served on mobile phonesbased on geo-location and highest bids, and displayed from user interests determined by dataanalytics of activities on the web. Results from the demonstration show very low latencyoverhead of processing large hundreds of thousands of transactions. This approach improvesboth action latency and accuracy of real-time decisions in IoT applications.Chapter one on On Big Data Management In Internet Of Things1.0 IntroductionAdvances in sensor technology, communication capabilities and data analytics have resultedin a new world of novel opportunities. With improved technology such as nanotechnology,manufacturers can now make sensors which are not only small enough to fit into anything andeverything but also more intelligent. These sensors can now pass their sensing data effectivelyand in real time due to improvements in communication protocols among devices. There arenow, also, emerging tools for processing these data. These phenomena combined have madethe Internet of Things (IoT) a topic of interest among researchers in recent years. Simply put,the IoT is the ability of people’s “things” to connect with anything, anywhere and at any timeusing any communication medium. “Things” here means connected devices of any form. It isestimated that by 2020 there will be 50 to 100 billion devices connected to the internet [2] .These devices will generate an incredible amount of massively heterogeneous data. These data,due to their size, rate at which they are generated and their heterogeneity are referred to as “BigData”. Big Data can be defined with the famous three characteristics known as the 3Vs:volume, variety, and velocity or sometimes 5Vs, including Value and Veracity [3] , [12] . Thesedata, if well managed, can give us invaluable insights into the behaviour of people and “things”;an insight that can have a wide range of applications.The potentials of incorporating insights from IoT data into aspects of our daily lives arebecoming a reality at a very fast rate. The acceptability and trust level is also growing as people have expressed willingness to apply IoT data analytics results in situations even as delicate asstock market trading [1] . These developments inform the need for efficient approaches tomanage and make use these huge and fast-moving data streams. Distributed processingframeworks such as Hadoop have been developed to manage large data but not data streams.One major limitation of distributed settings such as Hadoop is latency. They are still based onthe traditional Store-Process-and-Forward approach which makes them unsuitable for real-timeprocessing, a contrast with the real-time demands of the current and emerging application areas[4] . Store and forward also will not be able to satisfy the latency requirements of IoT databecause of the velocity and the unstructured nature of the data. Stream processing frameworkslike Apache Storm and Samza are then introduced to solve this problem. In stream processing,data from data sources are continuously processed as they arrive and do not need to be storedfirst. This improves latency, especially in stateless stream processing which processes data asit comes without reference to the current situation of the system.Stream processing frameworks, however, are more general for processing data streams and arenot tailored for the specific needs of IoT data management systems. IoT applications typicallyhave strict latency requirements. IoT applications also involve a great deal of Machine-to-Machine (M2M) communications. The latency requirements of emerging IoT applications, nodoubt, requires a new approach to reduce latency to its barest minimum and make fast andefficient use of “things” data.1.1 Research QuestionCan we develop a generic Big Data management approach to reduce the latency of intelligentreaction to actionable events in IoT applications?1.2 Objective of the ResearchThe aim of this work is to propose and demonstrate a generic, efficient, scalable and robustapproach to Big Data management approach in IoT which extracts real-time value from dataand demonstrate its operation in an application area. Using existing and emerging computingparadigms, we seek to develop an approach to significantly reduce latency in streaming datafrom a network of connected devices and thus capture events that trigger actions in real time.1.3 Implication of ResearchThis work seeks to propose a general latency-reducing approach to IoT data managementindependent of data source, type or communication protocol. Finding this approach willimprove significantly, the speed and responsiveness of current real-time applications and alsobroaden the applications of IoT to new latency critical domains. The approach will reduceresponse time of IoT applications and enable them to react fast enough to suit the requirements of emerging applications. It will also serve as the underlying principle of both open-source andcommercial IoT data management applications.1.4 Scope of workThe scope of this work includes the following:I. To provide a new approach to IoT data management with a view to reduce latency.II. To implement this approach with software tools.III. To apply this implementation to a challenging use case.1.5 OrganizationAn extensive review of literature is contained in the second chapter of this write-up. Thisincludes review of the main concepts, technologies used as well as related work in the researcharea. The third chapter presents and describes the proposed model, how it works and its latencyreducingadvantages. The fourth chapter describes an implementation of the model with resultsand evaluations and the fifth chapter contains the conclusion and future works on the subject.GET THE FULL PROJECT WORK>>How To Get The Complete Project Material PDFDo you need the complete project work titled “On Big Data Management In Internet Of Things – PDF Download” from chapters 1 to 5 with references and questionnaire it cost 3000 Naira e.t.c Click the blue-button belowCLICK HERE TO GET THE COMPLETE PROJECT MATERIAL (FILE)S NOW!>>call us (+234) 08157509410 or whats-app us (+234) 0810793263. Email us – [email protected]

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