Thursday, December 5, 2019
International Research Journal of Multidisciplinary Ã¢â¬ Free Samples
Question: Discuss about the International Research Journal of Multidisciplinary. Answer: Introduction: With the exponential increase in the number of devices connected to the internet, the excessive data consumption from these devices reflect the importance of Big data in IoT devices. The web of devices such as actuators, sensors. Software, embedded electronics, home appliances and vehicles are collectively referred as the Internet of Things or IoT. Each device has its own unique computing system that enables it to communicate with the other devices which are connected to the internet. This direct integration of the devices facilitates accuracy and efficiency of data transfer among the devices. The organizations which are delivering the IoT devices have to analyse and collect data from these devices simultaneously allowing a direct integration of computer systems with the physical worlds (Mulani and Pingle 2016). The data from the IoT devices can range from camera streaming services, biochip transponders to heart monitoring implants. The data from the devices are continuously exchange d among them leading to an amalgamation of software, service, data and hardware. According to a research paper from Gartner, estimates state that the revenue that will be generated from IoT data by the year 2020 will exceed more than 300 billion dolars (Cui 2016). The data that will be generated will be vast and will need well analysis method for accurate representation. This will impact other companies which will be forced to turn to data analysis tools and big data for accommodating this massive influx of user information from these IoT devices. About 73% of the data that are retrieved from IoT devices are used to improve businesses. The data is used for reducing downtime and maintenance, reducing the operational costs, improvement in the decision making and product performance (Wortmann and Flcht 2015). The data are also used to build business cases and strategies and how they are affecting the business from their traditional counterparts. Internet of things data also enables organizations to invest in technical consultation, deployment and implementation of strategies and planning. About Storage and data security in big data The influx of data from the IoT devices have to be handled by the data centres who have built their respective data storage facilities. The data centres need to be capable of handling such a vast amount of unstructured data. Organizations are slowly shifting towards PaaS service models (cloud based models) instead of developing their own storage solutions. Unlike the traditional data storage systems, these models need to be constantly upgraded to handle the continuous overflow of data. The storage of data with big data helps to potentially transform businesses and society across sectors. The advanced analystics that are offered by big data is still unrivalled. Then big data storage technologies are capable of idetofying various trends in the data and has the capability to handle huge amount of data. HDFS or Hadoop File systems are used to store data which are unstructrured in a proper way (John Walker 2014). It is an integral part of the Hadoop Framework and is useful for bulk processing and ingesting data. Another storage technology used for big data is NoSQL databases. It is a model that does not rely on transactional properties such as durability, isolation, consistency and atomicity (Chen et al. 2014). A new relational database known as NewSQL is also used for compare scalability while maintain the transactional features of NoSQL. Some technologies provide more query facades to the existing NoSQL database. It provides a high level interface while achieving a low latency. Other technologies has been developed for increasing the data security of the storage systems. Hive is such a platform that has been developed over HDFS system. By translating the query via Map Reduce it helps to execute the queries at high latency. This is made possible by validating the data every single time during the query time. Other storage facilities used for big data analytics is Cloud storage. This system is used for onlinen backups rather than hardware storages and offers a cheaper alternative to the enterprise environment. Benefits of Using Storage and data security of Big Data with IoT Data The benefits of using big data for storage and data security with IoT Data is immense and is necessary for the two technologies to co-exist. The huge amount of data cannot be handled by traditional databases as they are not built for handling data at a high rate. Big data helps to utilize the edge data analytics to pre-process data before they are put into the data servers for storage. Some data are filtered away and useless data are monitored and removed accordingly. With the growing number of unstructured data, companies delivering IoT products need big data to analyse what data is coming in and going out of the company (Lee et al. 2013). Relevant and vital data are collected from the IoT devices and analysed for their importance accordingly. Parameters are set which rules out any type of unwanted data that are coming from the IoT devices. This helps to reduce the amount of data that are used for storage purposes. Unnecessary data is filtered out to prevent excessive data from accu mulating in the data storages. Another benefit of using big data for storage and security purposes with IoT is the usage of different communication protocols such as WiFi, Bluetooth, Zigbee, MQTT and CoAp by the IoT devices. Understanding all these protocols and analysing the data accordingly is a major challenge for many businesses. IoT interoperability with big data can open up new avenues which can be used by businesses for better efficiency. The data needs to be correlated, received from several domain sources and protocols and analysed with the help of big data to make it structured and usable. The vast amount of data coming from several Iot devices need to be analysed in real so that the data makes sense and stays usable. Big data analytics helps to analyse the data in real time and predict certain trends that may have existed from a long time. Such insights helps the business to make decisions based on actual facts rather than gut feelings (Marz and Warren 2015). The usability of big data with IoT devices for storage has other benefits such as anticipating failures which cannot be tracked by any other analytics. As the embedded sensors in the IoT devices transmit data 24x7 , with real time analysis it can detect real time dangers and help improve customer experience. Limitations to Using Storage and data security of Big Data with IoT Data With several IoT devices in the market, the data can include database data, transactional data, ERM as well as CRM data and other unstructured data such as emails. This massive types of data from several sources can be managed up to an extent with the help of pre-processing but development is needed for efficient services. Another limitation of the data that is stored is based on data security. With the rising levels of cyber-attack sin the recent decade, no information can be termed as 100% secure (Gubbi et al. 2013). Strong user authentication, automatic encryption and intrusion protection are necessary for protecting this data but that area is still being researched. Proper tools need to be deployed for better security controls such as protecting log files and tools used for analytics inside the data platform. Another limitation is the improper encryption of output data from IoT devices. The data that are processed by big data analytics tools are often showed via dashboards, reports and appluications. Although efforts are put in encrypting the input data sources properly, not much work is done to encrypt the output data as well whuch can be used for intrusion. How organisations will find IoT Data useful The IoT data is used for gaining analytics information of consumers and used as a source of modelling. The data is used to make modules for an organisation where new modules are added based on their necessity. New services such as customer care centres can be added by an organisation if the need to become more customer centric arises in the organisation based on the data. Various vehicles use this data to decide their telemetry (Wu et al. 2014). A temperature drop in a certain region or excessive traffic in an area allows the car to navigate through an entirely different path. This rapid analysis of user information is only possible with real life analysis with the help of big data. It can help the organisation to create new time series data which allows the device to create a record of all the real time events. References Chen, M., Mao, S. and Liu, Y., 2014. Big data: A survey.Mobile networks and applications,19(2), pp.171-209. Cui, X., 2016. The internet of things. InEthical Ripples of Creativity and Innovation(pp. 61-68). Palgrave Macmillan, London. Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M., 2013. Internet of Things (IoT): A vision, architectural elements, and future directions.Future generation computer systems,29(7), pp.1645-1660. John Walker, S., 2014. Big data: A revolution that will transform how we live, work, and think. Lee, G.M., Crespi, N., Choi, J.K. and Boussard, M., 2013. Internet of things. InEvolution of Telecommunication Services(pp. 257-282). Springer, Berlin, Heidelberg. Marz, N. and Warren, J., 2015.Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co.. Mulani, T.T. and Pingle, S.V., 2016. Internet of things.International Research Journal of Multidisciplinary Studies,2(3). Wortmann, F. and Flchter, K., 2015. Internet of things.Business Information Systems Engineering,57(3), pp.221-224. Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), pp.97-107.