International Journal of Academic Multidisciplinary Research (IJAMR) ISSN: 2000-006X Vol. 2 Issue 3, March – 2018, Pages: 5-9 www.ijeais.org/ijamr 5 Status of Big Data In Internet of Things: A Comprehensive Overview Peter Alphonce, Lusekelo Kibona School of Electronic Information and Communication Huazhong University of Science and Technology CHINA Abstract: Reports suggests that total amount of data generated everyday reaches 2.5 quintillion bytes [9], annual global IP traffic run rate in 2016 was 1.2 zettabytes and will reach 3.3 zettabytes by 2021 [12]. According to Gartner [25], Internet of Things excluding personal computers, tablets and smartphones will grow to 26 billion units of installed devices in year 2020. This results from penetration of digital applications which highly motivated by smart societies which can be defined as to when a society deploys light and advanced computer technologies to aid provision and or supply chain value of social, cultural, governance and economic utilities for efficiency. Smart society is equipped with mobile, ubiquitous computing facilities, sensors and cyber-physical systems aims at exploring economies of scale; and to large extent it has been made possible with Internet of Things (IoT). This survey paper discusses status of big data in Internet of Things; how IoT generates big data, nature of data generated and dynamics in IoT as influenced by big data. Keywords: Big Data, Internet of Things. 1. INTRODUCTION Internet of Things (IoT) is defined as network of devices enabled with microcontrollers, transceivers, actuators, radio frequency identifiers (RFIDs) communicating to each other and with easy access by human. Internet of Things technology connects cars, mobile phones, detectors, servers, security camera, smart watches, GPS signals, smart televisions, etc. Workings of above few mentioned smart devices creates data with hidden value which namely by professionals as „big data‟ that demands sophisticated analytic frameworks to reveal value from which decisions are made and knowledge is gained. Rise in multi-functioning of general purpose digital devices, increase in processing power of processors, storing systems, internetworking of communication systems and equipments have caused data to grow and transfer easily and exponentially across distributed hosts. Social networks, sensors, search engines, genomics, astronomy and Contents Distribution Networks all have multiplier effects in creating big data. All Internet enabled accessories generate and processes huge volume of data of different formats; audio, video, images, texts, structured data, unstructured data, real time data and non-real time data across hosts and by this dependence big data is affecting Internet of Things and vice versa. This survey paper aims at giving a comprehensive overview of the status of big data in Internet of Things; discusses status of big data in Internet of Things; how Internet of Things generates big data, nature of data generated and dynamics in IoT as influenced by big data. 2. BACKGROUND Big data has been a trending topic among professionals in computer networks industry, in article "The rise of big data on cloud computing: Review and open research issues" [1], it describes big data as a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex and of a massive scale. As per article [6], "Clouds for Scalable Big Data Analytics", researcher had referred big data to massive, heterogeneous, and often unstructured digital content which is difficult to process using traditional data management tools and techniques. Continues to say the term encompasses data complexities, data varieties, real time processing needs and value extraction. In the paper [20] "The real-time city? Big data and smart urbanism", author discussed how authorities have deployed real-time analytics to manage public utilities and the relevance of data produced by network enabled monitors. Article [26] "From Data Mining and Knowledge Discovery to Big Data Analytics and Knowledge Extraction For Applications In Science", big data was defined as extensive, diverse, complex, data sets generated from digital based generating sources both hardware and software. As briefly discussed above (Section I and II) on the two terms, more on the subject is explored in below Sections including methodology used (Section III), big data status in smart networks and its influence (Section IV). 3. METHODOLOGY The methodology adopted by this study was „Internet Search‟. The study visited various sources on the Internet to establish facts about the presented issues. Where necessary the websites of the specific resource were visited, website of some journals which only put materials in html format rather than pdf or documents. The listed articles are mostly available on the Internet and where possible in some areas algorithm were modified to facilitate the discussion. Henceforth, secondary sources were majorly used in a large part to come up to conclusion. International Journal of Academic Multidisciplinary Research (IJAMR) ISSN: 2000-006X Vol. 2 Issue 3, March – 2018, Pages: 5-9 www.ijeais.org/ijamr 6 4. BIG DATA AND IOT INTERDYNAMICS Internet of Things includes mass of Internetconnected devices each of which can communicate to one another. Industry's majorly accepted definition of Internet of Things is to a scenario where network connectivity and computing capability extends to objects, sensors and everyday items not normally considered computers, allowing these devices to generate, exchange and consume data with minimal human intervention [2]. By year 2021 mobile-to-mobile connection expected to reach 51 percent of the total devices and connection [12], its usefulness in data-intensive industrial applications likes of healthcare monitoring (smart chipset in human body), security video surveillance and transportation (e-ticketing, auto-monitoring of systems), etc rose to become a platform of mass data sources. Term „big data‟ across globe is recognized to have features that make its defining attention, not only a collection of huge sizeable data alone but also grow unsteadily with time while hiding significant details; inspite of diverse definitions; big data appeared to have similar features from various studies [9, 28]: see below mentioned: (i) Volume refers to the amount of all types of data generated from different smart sources. The benefit of gathering large amounts of data from smart objects includes the creation of useful and hidden patterns through analysis. (ii) Variety described as distinct forms of data collected in the network of connected smart objects i.e IoT. Such data types include video, image, text, audio, and data logs, in either structured or unstructured format. (iii) Velocity feature concerns speed of big data creation and processing in the digital things networks. Rocket speed digital contents transfer in smart objects change following needy from businesses, data traverse via wireless, circuit switched networks and Bluetooth, example financial stock markets generates terabytes of data everyday, satellites transfer dense feeds in seconds, mining companies provides exploration updates, etc. (iv) Value as driving force behind big data analysis; it refers to the process of discovering hidden values from huge datasets collected [1]. Unknown value inside big data is what makes it for analysis. Figure 1. Pictorial representation of features which define big data. (A) BIG DATA DYNAMICS IN IOT IoT brought the future of which physical objects of everyday life are Internet enabled; configured with microcontrollers, transceivers for digital communication, and suitable protocol stacks that specifies interlinking with one another [3]. From personal IoT devices like wearable fitness and health monitoring devices to non-personal network enabled objects, all massively share electronic content which in turn after analysis evolves our lives. (i) Big Data creation in IoT Internet of Things offer regular data collection through sensing and sense-compute-actuate loops [8], applies automation and interlinking of programmed functionalities prior not existed; and it‟s via cross communication of the devices that this paper learnt diverse habits which enormously contribute to big data creation; from both mobile and stationary devices [6]. (ii) Data volume in IoT Through interconnection of smart objects, data volume created is huge and mulitly rapidly; according to DOMO report "Data Never Sleeps 5.0 2017" [11], in every minute Google conducts 3,607,080 searches, YouTube users watch 4,146,600 videos, Netflix subscribers stream 527,760 videos compared to 86,805 hour of videos in 2016, Twitter users send 456,000 tweets compared to 9,678 in 2016 [10, 11], etc. this amounts from interconnection of smart objects through Internet and cloud service platforms. (iii) Data velocity in IoT Speed of data processing in IoT is another contributor to big data rapid rise. 100Gbps ports in Transport Networks transfer data faster over 10Gbps which was highly deployed before, Transport layer technologies has led broadband IP traffic to record level. Refer IoT application daily basis in pollution level monitoring [16], weather monitoring [14], Global Positioning System [5] and smart grids. Sensor sends timely feeds in the server, analytics software processes uploaded updates to give aggregation and prediction of events. (iv) Data value in IoT Data generated in smart networks has improved digital experience of users, patterns are discovered and analyzed with frameworks from which decisions are made. In connected vehicles, faults detection systems provides online feeds to the manufacturer [15], smart television connects to Internet to transmit viewing information [17]. Before IoT it was not possible to automate checkups of this scale. (v) Ubiquitous Connectivity in IoT Ubiquitous connectivity is provided by number of objects connected to the Internet; by 2021 there will be 27.1 billion networked devices, an increase from 17.1 billion devices in 2016 [12]. Connectivity is everywhere due to low Value Volume Velocity Variety Big data International Journal of Academic Multidisciplinary Research (IJAMR) ISSN: 2000-006X Vol. 2 Issue 3, March – 2018, Pages: 5-9 www.ijeais.org/ijamr 7 cost and high speed pervasive networks. This is to say big data will be everywhere; smart homes, vehicular networks transactions, streaming, social networking, search engines queries [6]. Figure 2. IoT in Vehicular Networks [15] (B) BIG DATA CHANGES IOT Challenges and Opportunities. Big data grow simultaneously with IoT expansion; heterogeneity, scalability and interoperability aspects in softwares and hardwares tag along [18]. I observed several issues out of big data and IoT integration, and are below explained. (i) Data heterogeneity scales IoT Protocols Smart objects creates different types of data; with distinct uniformity; structured, unstructured, real-time and non-real time data. Heterogeneity of these data brought about new routing protocols and architectural model to support interoperability in IoT. Protocol helps to govern communication between distinct devices since billions of things been connected and continue to be connected to the Internet. New protocols are designed to bridge diverse technologies and enable intelligent routing [19]. (ii) Big Data changes IoT Architectures Sophisticated data analytics are produced for understanding, monitoring, regulating and planning smart environments [20, 24]. Rapid growth of big data has resulted into the emergence of versatile stream processing frameworks enabling improved connectivity, control and interaction with applications and across different platforms. Analytics platforms are redesigned to accommodate tremendous data passing by. Apache, Hadoop, MapReduce, AWS IoT [21], IBM Bluemix [22] and Azure IoT Suite [23] are an example of distributed computing resources. [27] Fog; computing environment that brings closer processing, storage, and networking functionalities to users and traditional Cloud Computing Data Centers, it relies on devices on the edge of the network that have more processing power. (iii) Performance Algorithms are researched to accommodate dynamic streaming application needs, application-level data routing over the pipelines to exhibit optimal performance for increasingly one-to-many communication [4]. New algorithms in response to desirable performance on throughput and latency in processing of big data. Refer Data mining cloud App [7], a software framework that enables the execution of large scale parameter, it sweeps data analysis applications on top of cloud computing and storage services, support density of data. It optimizes existing file systems demanded by data mining applications and data to be stored that they can be easily retrieved and migrated between nodes. (iv) Big Data Storage and Access Due to huge generation of big data in smart networks, this huge generation raised the question of storage difficulty and access; scalable platforms to handle huge amount of data generated and its encapsulation [1], as proliferation still continuing in smart things networks, it came need to counter high storage costs, transfer between primary and secondary storage systems, security in storage, etc [5], heterogeneous data need be encapsulated properly to enhance its accessibility. International Journal of Academic Multidisciplinary Research (IJAMR) ISSN: 2000-006X Vol. 2 Issue 3, March – 2018, Pages: 5-9 www.ijeais.org/ijamr 8 (v) New Knowledge and Technology Analysis of data created from Internet of Things opened avenues to improve other technologies. Noisy data are nurtured by developing new inference techniques that converts into useful information. (vi) New Frameworks for Future Integrated IoT Applications. Proposals for framework designs [21, 29] that combines Internet of Things, Semantic Web, and Big Data concepts been put forward to respond to high demands on the device‟s storage and computational capabilities, high network bandwidth, systems consistency, replication, concurrency control. 5. CONCLUSION The Internet of Things is an emerging topic of technical, social, and economic significance. Consumer products, durable goods, cars and trucks, industrial and utility components, sensors, and other everyday objects are being combined with Internet connectivity and analytic capabilities that promise to transform the way we work, live, and play. Big data in IoT has been able to scale technical aspects; architecture, traffic routing metrics, data access and storage. REFERENCES 1. 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