ON BIG DATA MANAGEMENT IN INTERNET OF THINGS

ABSTRACT

The Internet of Things (IoT) has generated a large amount of research interest across a wide variety of technical areas. These include the physical devices themselves, communications among them, and relationships between them. One of the effects of ubiquitous sensors networked together into large ecosystems has been an enormous flow of data supporting a wide variety of applications. In this work, we propose a new “IntelliFog-Cloud” approach to IoT Big Data Management by leveraging mined historical intelligence from a Big Data platform and combining it with real-time actionable events from IoT devices at the Fog layer to reduce action latency in IoT applications. This approach is demonstrated through an advertisement service simulation with VoltDB technology where advertisements are being served on mobile phones based on geo-location and highest bids, and displayed from user interests determined by data analytics of activities on the web. Results from the demonstration show very low latency overhead of processing large hundreds of thousands of transactions. This approach improves both action latency and accuracy of real-time decisions in IoT applications.

TABLE OF CONTENTS

ABSTRACT

LIST OF FIGURES

CHAPTER ONE

INTRODUCTION

1.0       Introduction

1.1       Research Question

1.2       Objective of the Research

1.3       Implication of Research

1.4       Scope of work

1.5       Organization

CHAPTER TWO

LITERATURE REVIEW

2.0       Internet of Things (IoT)

2.0.1    Why Internet of Things?

2.0.2    Applications of IoT

2.0.3    Challenges of IoT

2.1       Big Data

2.1.1    Big Data Management

2.1.2    Big Data and Internet of Things

2.2       Data Streams

2.2.1    Data Stream Processing

2.2.2    Stream Processing Models

2.3       Real-Time Data Stream Processing

2.3.1 Requirements of Real-time Data Stream Processing

2.4       Stream Processing Applications

2.4.1    Aurora

2.4.2    Borealis

2.4.3    Apache Storm

2.4.4    Apache S4

2.4.5    Apache Samza

2.4.6    VoltDb

2.5       Related Work

2.5.1    Towards Cloud-Based Big Data Analytics for Smart Future Cities

2.5.2    A Data-Centric Framework for Development and Deployment of Internet of Things Applications in Clouds

2.5.3    A CIM-Based Framework for Utility Big Data Analytics

2.5.4    Data  Management for the Internet of Things: Design Primitives and Solution

2.5.5    An Architecture to Support the Collection of Big Data in the Internet of Things

2.5.6    Lambda Architecture

2.6       Chapter Summary

CHAPTER THREE

ANALYSIS

3.0       Latency

3.0.1    Types of Latency

3.1       Fog Computing

3.2       Multi-Tier Fog-Cloud Architecture

3.3       Analysis of the Existing/Traditional Approach

3.3.1    From Devices to Cloud

3.3.2    Existing Fog Approach

3.4       The Proposed Latency-Reducing Intelli-Fog Approach

3.4.1    The intelli-Fog Layer

3.4.2    The Cloud Layer

3.5 Chapter Summary

CHAPTER FOUR

USE CASES AND IMPLEMENTATION

4.0       Introduction

4.1       Use Case Scenarios

4.1.1    Intelligent Patient Monitoring System

4.1.2    Smart Cities (Intelligent Traffic Light)

4.1.3    Geo-Location and User Interest Based Mobile Advertisement display

4.2       Use Case Implementation (Intelli-Fog Advertisement Display Based on Location and User Interest)

4. 2.1   Technology and Tools

4.2.2    The Developed Advertisement Display Simulation

4.4       Chapter Summary

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATION

5.0       Summary

5.1       Conclusion

5.2       Recommendations

References

Appendix

CHAPTER ONE

INTRODUCTION

1.0         Introduction

Advances in sensor technology, communication capabilities and data analytics have resulted in 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 and everything but also more intelligent. These sensors can now pass their sensing data effectively and in real time due to improvements in communication protocols among devices. There are now, also, emerging tools for processing these data. These phenomena combined have made the 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 conn ect with anything, anywhere and at any time using any communication medium. “Things” here means connected devices of any form. It is estimated 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 “Big Data”. Big Data can be defined with the famous thre e characteristics known as the 3Vs: volume, variety, and velocity or sometimes 5Vs, including Value and Veracity [3], [12]. These data, 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 are becoming 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 as stock market trading [1]. These developments inform the need for efficient approaches to manage and make use these huge and fast-moving data streams. Distributed processing frameworks 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 on the traditional Store-Process-and-Forward approach which makes them unsuitable for real-time processing, a contrast with the real-time demands of the current and emerging application areas

Store and forward also will not be able to satisfy the latency requirements of IoT data because of the velocity and the unstructured nature of the data. Stream processing frameworks like 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 stored first. This improves latency, especially in stateless stream processing...

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Item Type: Postgraduate Material  |  Attribute: 80 pages  |  Chapters: 1-5

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