Data Mining in Internet of Things

 

Abstract

Internet of things (IoT) is a network of objects such as, vehicles, buildings and

other items embedded with sensors, software and network connectivity which assist

them to collect and exchange data. As this network of objects generate massive

amount of data stream, the challenge here is to convert the generated or captured

data by IoT into information by means of data mining to provide ecient, timely

knowledge to aid decision making. Applying traditional Data mining algorithms to

the IoT platform causes several issues such as, handling the distributed nature of

IoT, resource constraints of things etc. Knowledge discovery from collected data

can be handled in two ways. The rst approach is to send the collected data to the

cloud computing platform and perform the data mining operations on the cloud.

But, this methodology increases the latency and response time which might not be

helpful in handling real time applications. So to decrease the latency and increase

the throughput, a new approach could be used to mine the data at IoT level itself,

which can be referred to as Edge Mining or Far Edge Computing. Limited battery,

processing capabilities, memory constraints etc. needs to be taken into account

during Edge Mining to achieve the expected eciency. In this project, we will

survey the existing mining algorithms in the domain of IoT, try to classify them

and, future directions to further improve the eciency of these algorithms. We will

also be reviewing the existing work done associated with the processes involved in

knowledge discovery (Data Collection, Data processing, Data transformation etc.)

and the research involved with these processes in the domain of IoT.

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1 Introduction

IoT (Internet of Things) [1] [2] can be simply described as connecting with Internet,

di erent things that exists around us so that the formed network can collect and exchange

data. A thing can be any item in the real world that might be connected to

Internet to communicate with other things, person, server or machine. The things in

IoT can be said of possessing traits like cheap sensors, low energy communication and

limited processing capabilities. “Things,” in the IoT sense, can refer to a wide variety of

devices such as heart monitoring implants, biochip transponders on farm animals, electric

clams in coastal waters, automobiles with built-in sensors, DNA analysis devices for

environmental/food/pathogen monitoring, eld operation devices that assist re ghters

in search and rescue operations, Radio Frequency Identi cation tags (RFID), Wireless

Sensor Networks (WSN), smartphones or wearable devices [3] [4]. IoT has the potential

to lead the next technological revolution across multiple domains including laying the

foundation for future smart cities, smart industries, health-care, disaster management,

retail, transportation, agriculture to name a few, which can improve the quality of life.

There have been plethora of good surveys presented each of which view IoT from di erent

perspective: challenges [5], applications [6], standards [7] and smartness [8] [9]. Another

study [10] described the overall design of IoT as a generic ve-layer architecture, from

bottom-up these layers are: edge-technology, access gateway, internet, middleware and

application.

It is estimated that, by 2020 there will be 50 billion things connected with Internet

against the population of 7.6 billion people around the globe, which renders the number

of 6.58 connected devices per person [11]. Enormous data generated by things will be an

important source of Big data, creating massive data streams; that will demand immediate

and context aware responses. Currently, many applications rely on data and services

hosted on remote clouds. However, more devices connected to the IoT paradigm will only

increase, augmenting the amount of data going to the cloud for processing. Pushing data

to the cloud is expensive and can cause multiple problems like data explosion. Because

of this, it is necessary to manage this enormous amount of data generated by “things”

and extract the knowledge from it in real time. This is where Knowledge Discovery from

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Data (KDD) and data mining comes into play, as these technologies provides the means

to nd the useful hidden information collected from things, which can be used further to

enhance the performance of system or to provide new useful services this new network

can provide.

There is a great vision that all things can be easily controlled and monitored, can

be identi ed automatically by other things, can communicate with each other through

internet, and can even make decisions by themselves [12]. The IoT has very complex data

types and data generation requirements. So in order to make IoT smarter the mining

algorithms used in the paradigm of IoT also need to deal with 5 Vs of data generated

namely: Volume, Variety, Variability, Velocity and Veracity which all can be described

brie

y as below for the domain of IoT:

 Volume: The sheer size of data generated by di erent things, which is on a scale

never experienced before and to gather this data in a cost-ecient and energye

cient way when network bandwidth and resources may be at premium.

 Variety: Data collected from di erent things have heterogeneous data types, differences

in representations and semantic interpretations. Di erent kind of data

collected by things includes sensor readings, RFID tags, GPS readings, video, images,

social feeds (e.g. Facebook posts, tweets).

 Variability: Things senses and collects data from di erent environments like crowd

sensing data on the streets or movement of objects inside the home to detect the

falling of old persons. This kind of data changes rapidly so along this V we need to

take care of the rate at which data is changing.

 Velocity: The mining algorithms need to perform the task of knowledge discovery

from data in real time where millions of data points are being generated by things

in seconds such as by the sensors to detect fuel leakage in airplanes.

 Veracity: The data generated by things may be noisy, inaccurate or unreliable

and the mining algorithm should take care of this kind of data eciently in order

to discover helpful knowledge. Often in WSN only some of the sensors are selected

to sense the environment and collect data to preserve the lifetime of entire network.

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This project tries to examine the di erent mining algorithm proposed in the domain

of IoT. According to [13] the data mining can be categorized by di erent views

such as: (i) knowledge view or data mining functions view which includes classi cation,

clustering, association analysis, discrimination, time series analysis, characterization and

outlier analysis (ii) utilized techniques view which includes machine learning, statistics,

pattern recognition, big data, support vector machine, rough set, neural networks and

evolutionary algorithms (iii) application view which includes industry, telecommunication,

banking, fraud analysis, biodata mining, stock market analysis, web mining, text

mining, social network and e-commerce.The authors in [14] tries to classify di erent mining

algorithms in IoT for data mining functions view and sub classify it further to examine

whether they are used for infrastructure of IoT(i.e. to enhance the performance of IoT

network) or for the applications of IoT. We will rst try to generalize the existing data

mining models and then will try to divide the di erent mining algorithms developed so

far according to the 5-dimensional metric we created. The rest of the paper is organized

as follows. We present the related work in section 2. Section 3 tries to generalize the

existing data mining models for the IoT and gives di erent categories of it. Section 4

de nes the 5-Dimensional space in which the existing mining algorithms for IoT can be

classi ed. We present our conclusions in Section 5 of the paper.

2 Related Work

There has been signi cant amount of work done to study di erent architectures, models

and algorithms for the IoT. The term Data Mining does not really present all the components

of KDD. Data mining is merely a step in the process of KDD. The entire data

mining process can be divided into di erent parts with major parts consisting of data

preprocessing (data cleaning, data integration, data selection and data transformation),

data mining step itself which discovers interesting knowledge about data in order to yield

fruitful results and data post-processing (pattern evaluation and visualization) to aid

proper decision making. All these steps of data mining is presented in gure 1 below

properly.

Starting with data pre-processing, as “things” in IoT have resource constraints within

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Figure 1: Data mining as a step in knowledge discovery

them and they can be used in real time analysis the work done for this step can be

assessed in terms of energy saving and/or latency improvements. Authors at [15] proposed

an automatic time series modeling based data aggregation scheme in wireless sensor

networks to decrease the number of transmitted data values between sensor nodes and

aggregator by using time series prediction model and to save energy of the WSN. The

big data collection framework for water industry has been suggested in [16] to provide

useful insights consumers to proactively manage their water supplies and to utilities

management to achieve higher levels of sustainability in water supply. Extensible and

exible architecture for integrating data collected from WSN has been proposed using

REST based Web services as an inter-operable application layer that can be directly

integrated into other application domains for remote monitoring such as e-health care

services, smart homes, or even vehicular area networks (VAN) in [17]. Apart from that,

data collection and pre-processing frameworks and architectures has been proposed in

di erent application domains as well such as in medical Alarm-Net [18] or CodeBlue [19]

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for environment monitoring.

Based on the data post-processing step there has been many interesting and innovative

applications built on top of data mining in IoT from the domains ranging from smart

home, smart cities, health-care, agriculture, industry monitoring, environment monitoring,

disaster management, vehicular systems etc. Several possible applications of the IoT

have been presented or are about to be presented in the near future as are mentioned in

several technical reports [20], [21], research papers [6], [10], and books [22], [23], [24] and

by international companies [25], [26].

There has been a number of surveys done to analyze di erent mining algorithms in

the context of IoT, which all view data mining in IoT from data mining functions view,

application view or utilized technique view. Some survey exists to incorporate the new

notion of edge mining or far-edge mining where many of the mining functionalities are

achieved by processing and discovering knowledge at IoT layer itself. But to our best

knowledge there exist no survey so far that gives useful insights for mining algorithms used

in both traditional mining in IoT and edge mining. Through this project we are trying

to integrate emerging edge mining algorithms in the algorithms proposed for traditional

IoT. In [14] authors examine di erent mining algorithms from mining functions view (i.e.

clustering, classi cation, frequent pattern mining). Di erent data mining algorithms and

challenges faced to integrate those algorithms to IoT has been discussed in [13]. A novel

distributed data-mining model to realize the seamless access between cloud computing

and distributed data mining has been proposed in [27]. Authors also analyzed di erent

edge mining algorithms in [28].

3 Data Mining Models for IoT

Data mining is no longer considered as an approach for traditional data analysis and

statistics. It has become an essential tool in Internet of things (IoT). Data mining faces a

number of challenges and technical issues in the rapidly changing real-time environment

of IoT. First of all, because of the real time environment of IoT, Data mining processes

should be really quick and ecient to support the need of real-time data analysis and

decision making. Second, the nature of the data is heterogeneous and it is distributed. It

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is essential that Data mining processes should be capable enough to adopt the existing

distribution and heterogeneity of IoT. Third is the data quality control. It is important

to store and manage data properly to guarantee real time results. Last is the decision

making control. The characteristics such as rules used for decision making etc. need

to be calculated carefully. It is said that, RFID technology used in the supply chain

by supermarket, recording Electronic Product Code (EPC), location and time will be

generating 12.6 GB of data stream in one second and 544 TB of data per day [29]. The

challenges mentioned above can be concisely presented as shown in the gure-2.

Figure 2: Challenges in Internet of Things

Now we will be discussing di erent data mining models that can be objected to IoT.

3.1 Multi-layer data mining model in Internet of Things:

Internet of Things communicate and exchange information through equipment such as

sensors, GPS (global positioning system), radio frequency identi cation (RFID) etc.

Multi-layer data mining model is based on the RFID data mining framework and hierarchical

architecture of IoT and is divided into four layers and can be seen in Figure-3:

 Data collection layer: Data collection layer is the basis on which IoT is formed

and acts like the source of IoT as its main functionality is to recognize things and

collection information. RFID reader, receiver etc. are used as the components to

collect all kinds of data from objects such as GPS data, RFID stream data, location

and sensor data, satellite data, and so on. Collecting information from these

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di erent kind of sources require di erent strategies. The components mentioned

earlier collect the data and transmit it to the upper gateway access points and so

on in the hierarchy. multiple issues such as energy consumption, fault tolerance,

data ltering etc. come into picture during the data collection process and they

must be handled carefully.

 Data management layer: The data collected from the homogeneous and heterogeneous

environments in the data collection layer is managed in this layer. According

to the RFID data framework[reference], an RFID cube has three tables: the

information table, the pause table and the graph table. These table have di erent

functionalities and in data management layer, records from the pause table (used

to store the position information of the data) are obtained to store and manage the

cleansed data.

 Event process layer: An event can be de ned as a combination of multiple factors

such as integration of data, time etc. and it provides a high level of processing

mechanism in Internet of Things. Event ltering is the most important part of this

layer. Contents are collected, organized and analyzed in this layer through various

data mining strategies and passed to the upper layer based on the requirements of

the events.

 Data mining service layer: This layer depends deeply on the functionality of

lower layers. It provides services to the users based on the analyzed and processed

data. It mainly consists of three parts: 1. Data 2. Data Mining and 3. Knowledge.

3.2 Distributed data mining model in Internet of Things:

Unlike the traditional data, data in Internet of things is huge, distributive in nature, time

& location-related etc. These characteristics create several issues for central data mining

and hence, a new mining model known as Distributed data mining model was designed.

Using this model, high-performance requirements can be met reducing the computing

power and high storage capacity required in central data mining process.

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Figure 3: Distributed Data Mining Model

The global control node is the core of the data mining system in this model. It selects

data mining algorithm and organizes mining data sets, then guides these sets to secondary

nodes, which will collect original data from various kinds of smart objects. The original

data will be stored in the local database after preprocess of data lter, abstraction and

compression. A local model is obtained with event lter, complex event detection and

data mining of local node. When needed, the local model will be controlled by the global

control node, and all set of local models will form the global model. Data of objects,

preprocessed data and information can be exchanged among secondary nodes. Multilayer

of agents based on union administrative mechanism controls the whole process.

3.3 Data mining model based on grid in Internet of Things:

Stankovski et al. [30] proposed a data mining grid based on which this model is designed.

Data mining model based on grid in Internet of Things consists of ve layers:

 IoT Resource Layer: Resource layer is formed with the combination of di erent

software and hardware modules.

 IoT Service Layer: Service layer consists of di erent divisions of services provided

by the function modules.

 Grid Middleware Layer: The main functionality of this layer is to solve the

problems generated in the network.

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 Grid Mining Layer: This layer is responsible for data fusion.

 Grid Application Layer: This layer provides the interface services to the users

and consists of 4 modules.

Figure 4: Data Mining Model Based on Grid in Internet of Things.

Grid computing, similar to IoT is receiving a growing attention from research and industrial

communities. The basic idea of Grid is that users can make use of the computation

resources of Grid as the same as power resources. Various computing resources, data

resources and devices resources can be accessed or used conveniently. The basic idea

of IoT is to connect various smart objects via internet. Thus smart objects become intelligent,

context-awareness, and long-range operable. Therefore, we may regard smart

objects of IoT as a kind of resources for Grid computing, and then use data mining services

of Grid to implement the data mining operations for IoT. The di erences between

DataMiningGrid-based data mining model for IoT and DataMiningGrid is the part of

software and hardware resources. IoT provides more types of hardware, e.g., RFID tags,

RFID Readers, WSN, WSAN and Sensor networks etc. It also o ers various software resources,

e.g., event processing algorithms, data warehouse and data mining applications

etc.

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3.4 Data mining model for IoT from multi-technology integra-

tion perspective:

The Internet of Things is one of the most important development directions of the nextgeneration

Internet. At the same time, there are still a number of new directions, e.g.,

trusted network, ubiquitous network, grid computing, cloud computing etc. Therefore,

from the perspective of multi-technology integration, a new data-mining model for IoT

has been proposed by Shen Bin et al. [31]. In this model, data comes from the contextawareness

of individuals, smart objects or the environment. 128-bit IPV6 address is

Figure 5: Data mining model for IoT from multi-technology integration perspective.

adopted, and a variety of ubiquitous ways are provided for accessing to the future Internet,

such as: Intranet/Internet, FTTx/xDSL, sensor devices, RFID, WLAN/WiMAX,

2.5/3/4G mobile access and so on. Trusted control plane is able to ensure credibility and

controllability of data transmission. Data mining tools and algorithms submit gained

knowledge to various service-oriented applications, such as intelligent transportation, intelligent

logistics etc.

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4 Data Mining Algorithms for IoT

In this section we will try to distinguish di erent mining algorithms for the IoT based on

di erent parameters. We consider ve di erent parameters and we call it as 5-dimensional

system to classify a mining algorithm. These ve parameters are:

 Data mining algorithm functionality: Along this dimension we can classify

algorithm according to the data mining function that is used. The di erent mining

functions are clustering, classi cation, frequent pattern mining.

 Goal of algorithm: Along this dimension we can discuss about whether the

mining used is for the purpose of enhancing the performance of infrastructure of

IoT system or to discover useful knowledge for the application built on top of

IoT infrastructure. So the value for this dimension can be infrastructure if mining

algorithm is enhancing the performance of system or service if it discovers knowledge

for the application used.

 Place of mining: This is to distinguish whether the selected mining algorithm

is being processed at the edge or in the Internet based Cloud services (IBC)/data

servers.

 Mining model used: Along this dimension we will distinguish which mining

model is being used from section 3 for the algorithm in focus.

 “Things” involved: This parameter will be used to specify which devices are

considered for algorithm. The devices or things includes RFID, WSN, smartphones,

cars, wearable devices, etc.

We present below the table describing di erent mining algorithms in IoT and where do

they stand according to our 5-D system.

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Functionality Goal Place Model “Things” References

Clustering Infrastructure Cloud

Distributed

Grid

WSN

[32], [33], [34],

[35], [36]

Frequent Pattern-

Mining

Clustering

Service Cloud

Multi-layer

Multi-Technology

WSN [37], [54]

Clustering Service Cloud Multi-layer GPS [38]

Clustering Service Cloud

Multi-layer

Multi-Technology

GPS

Sensors

[39], [40]

Clustering Service Cloud

Distributed

Grid

Multi-layer

GPS

Smart phone

PDA

[41], [42]

Clustering Service Cloud

Multi-layer

Multi-Technology

Smart phone [43]

Classi cation Infrastructure Cloud Multi-layer RFID [44]

Classi cation Service Cloud

Multi-layer

Multi-Technology

GPS

Smart phone

Sensors

[45], [46], [47]

Classi cation Service Edge

Multi-layer

Multi-Technology

Distributed

Infraredsensors

WSN

[48]

Frequent Pattern-

Mining

Service Cloud

Multi-layer

Grid

RFID [49], [50]

Frequent Pattern-

Mining

Service Cloud

Multi-layer

Grid

Multi-Technology

RFID

GPS

[51], [52], [53]

Sequential Mining

Clustering

Service Cloud

Multi-layer

Multi-Technology

RFID

GPS

[55]

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Frequent Pattern-

Mining

Infrastructure Cloud

Multi-layer

Multi-Technology

Distributed

RFID

GPS

Smart phone

[56], [57], [58]

Frequent Pattern-

Mining

Clustering

Classi cation

Service Cloud

Multi-layer

Multi-Technology

GPS

Smart phone

Sensors

[59]

Clustering

Classi cation

Infrastructure Edge Multi-layer WSN [60], [62]

Classi cation Service Edge

Multi-layer

Grid

WSN [61]

Table 1: Classi cation of di erent algorithms according to 5-D system

5 Conclusion

The Internet of Things concept arises from the need to manage, automate, and explore

all devices, instruments, and sensors in the world. In order to make wise decisions both

for people and for the things in IoT, data mining technologies are integrated with IoT

technologies for decision making support and system optimization. Data mining involves

discovering novel, interesting, and potentially useful patterns from data and applying

algorithms to the extraction of hidden information. In this paper, aiming at the characteristic

of massive data in Internet of Things, we discuss about the di erent data mining

models for Internet of Things, which when considered during the real time implementation

will be helpful for the applications working on IoT and setting up the environment

as needed. During this survey we tried to include more parameters while examining the

existing mining algorithms in IoT. We also tried to include the notion of Edge Computing

[63], [64] and Fog computing [65] which can be used to reduce the bandwidth

consumption, latency by mining the collected data at the edge of the network. There

also exist several challenges to focus on for the domain of data mining in IoT such as:

capturing and processing the massive amount of data streams generated by things. Converting

the traditional mining algorithms to t the distributed nature of IoT and that

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too generating massive amount of data (Big Data) is a big challenge. Secondly, data collected

from things contain very personal and private data like daily activity of a person,

medical-records, banking transactions to name a few. The solutions provided for mining

these data should also focus on privacy and security of data and results.

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