ANALYTICAL STUDY OF DATA MINING FOR INTERNET OF THINGS: A REVIEW

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Available Online at www.ijpret.com 1499

INTERNATIONAL JOURNAL OF PURE AND

APPLIED RESEARCH IN ENGINEERING AND

TECHNOLOGY

A PATH FOR HORIZING YOUR INNOVATIVE WORK

ANALYTICAL STUDY OF DATA MINING FOR INTERNET OF THINGS: A REVIEW

MISS. NIKITA D. DONGARE, PROF. V. T. GAIKWAD, PROF. H. N. DATIR Accepted Date: 05/03/2015; Published Date: 01/05/2015

INTRODUCTION

Abstract:The internet of things (IoT) is a structure in which object, animals, or people are provided with unique identifiers and ability to transfer data over a network without requiring human to human or human to computer interaction. The difference between internet and internet of things is that the internet is the first version in which the data created by people while the next version is about data created by things. The Internet of Things (IoT) is the broad network of physical objects or "things" embedded with electronics, software, sensors and different connectivity to enable it to achieve greater value and service by transforming data with the manufacturer, operator and or other connected devices. Each and every thing or object is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. It sounds like mission impossible to connect everything (IoT) will dramatically change our life in the near future, by making many impossible things possible.

Keywords:Data Mining, Internet of Things, Knowledge Discovery In Databases (KDD).

Corresponding Author: MISS. NIKITA D. DONGARE

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Nikita D. Dongare, IJPRET, 2015; Volume 3 (9): 1499-1505

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The Internet of Things (IoT) refers to the next generation of Internet which will contain trillions of nodes representing various objects from small ubiquitous sensor devices and handhelds to large web servers and supercomputer clusters [1]. It is the next technological revolution after the revolution of computer and Internet IoT is the core of Smart Planet that is proposed by IBM Corporation. Smart objects of the Internet of Things (eg. sensor inputs, actuators etc.) are able to communicate via the internet based on the new technologies of information and communication. The Internet of Things will produce large volumes of data[2].One of the most important questions that arise now is, how do we convert the data generated or captured by IoT into knowledge to provide a more convenient environment to people. This is where knowledge discovery in databases (KDD) and data mining technologies come into play, for these technologies provide possible solutions to find out the information hidden in the data of IoT, which can be used to enhance the performance of the system or to improve the quality of services this new environment can provide.

This paper gives a review of systematic description of data mining and also a discussion about how to connect it to the IoT so as to provide a general introduction focusing on data mining or the IoT to shift to the next generation internet environment. To clarify what the IoT refers to, several good surveys were presented recently each of which view the IoT from a different perspective: challenges, applications, standards, and smartness [3].The Internet of things (IoT) is becoming an increasingly growing topic of conversation both in the workplace and outside of it. It’s a concept that not only has the potential to impact how we live but also how we work. But what exactly is the “Internet of things” and what impact is it going to have on you if any. There are a lot of complexities around the Internet of things but we want to stick to the basics. Lots of technical and policy related conversations are being had but many people are still just trying to grasp the foundation of what the heck these conversations are about. Simply put this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes everything from cell phones, coffee makers, washing machines, headphones, lamps, wearable devices and almost anything else you can think of.

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entities. Therefore, the ability to securely and privately collect, manage, index, query and process large amounts of data is critical.

RFID tags can be used for aide variety of access control applications [4]. For example; RFID sensors can be used for fast access control on highways, instead of manual tollbooths. Similarly, a significant number of library systems have implemented smart check out systems with tags on items. When the collected data is allowed to have network connectivity for further aggregate analysis and processing, over multiple access points, this also enables significant tracking and analysis capabilities for a variety of applications. For example, in a network of connected libraries, automated tracking can provide the insights required to decide which books to acquire for the different locations, based on the aggregate analysis.

II. DATA MINING

Data mining is also known as KDD i.e. Knowledge Discovery in databases [5]. KDD is the automated extraction of novel, understandable and potentially useful patterns implicitly stored in large databases. The data warehouses and other massive information repositories comprising textual, numerical, graphical, spatial data. Data mining the analysis step of the Knowledge Discovery in Databases process or KDD [6].An interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning , statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

III. ARCHITECTURE OF IOT:

Fig.1 The four-layer architecture.

The architecture of IoT system can be classified into four layers: object sensing layer, data exchange layer, information integration layer, and application service layer. The four-layer architecture is shown in Fig.1 [8]. The object-sensing layer handles sensing the physical objects and obtaining data. The data exchange layer handles transparent transmission of data. The information integration layer handles recombination, cleaning and fusion of uncertain

Application Service Layer Information Integration Layer Data Exchange Layer

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information acquired from the networks, and integrates the uncertain information into usable knowledge; the application service layer provides content services for various users.

The traditional Internet generally does not have Sensing ability, and only interconnects the intelligent devices. In contrast, the IoT has the additional sensing layer, which reduces the requirements on the capability of devices, and enables the interconnection among the non-intelligent or weakly non-intelligent devices. Meanwhile, it brings about many new requirements and challenges to data exchange, information integration and services, as well as the complexity of the network architecture.

IV. OBJECTIVES OF IOT:

Compared with the traditional information networks, IoT has three new goals, i.e., more extensive Interconnection, more intensive information perception and more comprehensive intelligent service. They are elaborated as follows [7].

A. More Extensive Interconnection

IoT extends the interconnection among the information equipment’s, such as computer and mobile phone, to the interconnection of all intelligent or non-intelligent physical objects. It has the following outstanding characteristics:

1) Extensiveness in the quantity of devices: The amount of the connected devices will sharply rise from several billions to over hundreds of billions, including a multitude of equipment’s, sensors, actuators, vehicles, and devices attached with RFID.

2) Extensiveness in the type of devices: Networking devices (networking elements) may be powered by the electronic power directly or by batteries; the computation and communication capacity may be greatly different, e.g., some devices even may not have any computational ability.

B. More Intensive Information Perception

IoT extends the paradigm of traditional single sensors that sense the local environment independently to the new paradigm of collaboration of multi-sensors to achieve the global environment awareness. Sensing information from each single sensor may contain uncertainties in the following aspects:

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•Inconsistency - There is inconsistent information due to the distortion of space-time mapping. •Inaccuracy -A range of information inaccuracies are often caused by the variety of sampling methods and different capabilities of the sensors.

•Discontinuities - Intermittent information availability is often caused by the dynamic network transmission capacity.

•Incomprehensiveness -Incomplete sensing of information is caused by the limitations of sensors. For example, measuring the forest pollution relying on Carbon Dioxide information only is clearly inadequate.

•Incompleteness -Partial loss of information is caused by dynamic network environment.

Therefore, it is difficult to use the sensor information directly, and the challenge of effective utilization of the uncertain sensory data in IoT must be met. This kind of uncertain information needs certain representation and further to be integrated in order to fuse them into relatively accurate knowledge. Then, we can understand and control the physical environments in a timely manner. For example, in the coalmine gas-monitoring scenario, it is less enough to judge if there is an explosion based on local information only. The decision should be made based on the distribution of gas density and the situation of gas own after real-time fusion of multi-point information.

V. CURRENT RESEARCH WORK

Data exchange among large-scale heterogeneous network elements. Through modeling the architecture of IoT and the interconnection models of sub-networks, develop the mechanisms of network convergence and autonomy, present the methods of measuring and evaluating the network, and solve the problem of interconnecting large-scale heterogeneous network elements within the context of local dynamic autonomy and highly efficient network convergence [9].

A. Infrastructure Perspective

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B. Data Perspective

In order to deal with the open issues of large-scale data entering the system from different data resources quickly and dynamically, several relevant technologies, such as data preprocessing, information extraction, and information retrieval, are usually employed. Simply speaking, determining what data need to be kept on the sensor and what data need not is a difficult issue for a sensor. The limited memory size is not the only reason for this issue. The other reason is in that we need to filter out redundant data so as to avoid their influence on the performance of the system. That is why several recent studies attempted to provide solutions to handle the big data issue from things, such as dimension reduction, data compression, and data sampling. For the big data issue of IoT, it can be easily found that acquisition, deposition, analysis, and integration involve several vital open issues that may strongly impact the performance of an IoT system. The other important issue is that most data are captured by different sensors, RFID’s, and devices; thus, the data are either heterogeneous or dispersed to different regions, systems or applications.

VI. CONCLUSIONS

In this paper, we can study the IoT, and its objectives and its application. The applications of IoT have been further extended to various areas in order to manage people's production and living more accurately and dynamically and improve the relationship between human and environment. This article introduced the main concepts and features of IoT, as well as analyzed the objectives and scientific challenges of IoT technology.

VII. REFERENCES

1. "Data Mining Curriculum". ACM SIGKDD. 2006-04-30. Retrieved 2014-01-27.

2. Clifton, Christopher (2010). "Encyclopedia Britannica: Definition of Data Mining". Retrieved 2010-12-09.

3. Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07.

4. C. Scheme, V. Vasyutynskyy, and K. Kabitzsch, “Simulation and analysis of buying behavior in supermarkets,” in Proc. IEEE International Conference on Emerging Technologies and Factory Automation, 2010, pp. 1–4.

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6. Global Environment for Networking Innovation (GENI), http://www.geni.net/, Aug. 2011.

7. International Telecommunication Union. ITU Internet Reports 2005: The Internet of Things, November 2005.

8. Bush R, Meyer D. Some Internet architectural guidelines and philosophy. RFC 3429, Dec. 2002, http://www.faqs.org/rfcs/rfcs3439.html.