Blending IOT and Big Data Analytics

Blending IOT and Big Data Analytics

Tulasi

Christ University Bangalore

Abstract–Internet is continuously evolving and changing. Internet of Things (IoT) can be considered as the future of Internet applications which involves machine to machine learning (M2M). The actionable intelligence can be derived through fusion of Big Data and real time analytics with IoT. Big Data and IoT can be viewed as two sides of a coin. With the connection between Big Data and the objects on Internet benefits of IoT can be easily reaped. The applications of IoT spread across various domains like health care, retail management and disaster management. Despite the benefits associated with the synthesis of Big Data analytics and IoT there are inherent complexities and challenges involved which needs to be catered. This paper focuses on the combination of Big Data analytics and IoT.

Keywords: Internet of Things (IoT), Big Data, Real time analytics, Machine to machine learning (M2M), Data intensive.

INTRODUCTION:

Internet applications are now closely associated to human life. Ranging from e-mails to e-learning, many applications are in vogue which has simplified otherwise complex processes. Technology is being refined to integrate itself seamlessly into the routines of human world. The digital space has provided the platform to implement various applications which can facilitate and ease the complicated processes in various domains.

This has resulted in deployment of various types of sensors, computing devices in huge number. Devices which are interconnected share and create data being termed as Internet of Things (IoT) [2]. Instead of most data on the Internet being produced and consumed by people (text, audio, video), more and more information would be produced and consumed by machines, communicating between themselves to improve the quality of our live. It has been seen that IoT has augmented people in various fields like healthcare, natural disaster management [5]. The integration of IoT into the health care system could prove to be incredibly beneficial for both an individual and a society [7]. A chip could be implemented into each

individual, allowing for hospitals to monitor the vital signs of the patient. By tracking their vital signs, it could help indicate whether or not serious assessment is necessary.

IoT can also function as a tool that can conserve energy within households. Home appliances communicating and operating would lead to efficient energy usage. IOT allows for the communication between devices, commonly referred to as Machine-to-Machine (M2M) communication. With this being possible, physical devices would be able to communicate to people letting them know their condition and where it is located. Devices such as trucks or ships allow for the maximum capacity to be filled by communication amongst devices and then relaying that information to a person to capitalize on the data supplied. Another advantage of IoT is the ability to track individual consumers and targeting these consumers based on the information supplied by the devices. In a way, it provides a more personalized system that could potentially increase business sales and increases their demographic. Utilities due to optimal utilization of IoT are many. The various advantages of IoT has been visualized in fig 1. IoT comprises of not only the computing devices but also humans who can sense, communicate and compute. Thus along with the advantages IoT comes with it inherent complexities and challenges. The major concerns related with IoT are complexity of the system, space, size, security and privacy. Due to huge number of interconnections there is a great possibility of increase in complexity of the system. The Internet of objects would encode 50 to 100 trillion objects and would be able to follow the movement of those objects [9]. The size of IoT would be a major concern. Direct collection of sensitive personal information, such as precise geo location, financial account numbers, or health information may create privacy risk. The data intensive nature of IoT can be channeled with Big Data as a part of the solution to the challenges faced by IoT.

Fig 1 Utilities of IoT

The deployment of various devices is also contributing to the deluge of data or now more predominantly known as Big Data. IoT are one of the major sources for Big Data. With the count of interconnected devices increasing the data associated with them is mounting to a humongous one. IoT intersects with Big Data and it is evident that the two trends would fit one another. IoT and Big Data are connected by the definition billions of internet-connected 'things' . The size of the digital universe and the number of interconnected things is being amplified. This is not being complimented by the actionable data [9] . The Big Data analytics would provide a platform to enhance and obtain actionable data for the humongous data being collected. This paper deals with the relationship between IoT and Big Data and its significance.

Data Intensive Iot

Data is usually perceived in modular or transactional form, like a sale, data on a product. But IoT will be creating streams of data similar to social networking. The concept of IoT can be visualized in a smart warehouse where data is being stored regarding the opening of the door of warehouse like duration, temperature, time and date, frequency per hour, per day, per week so on. This is a continuous stream of data which is being captured by various sensors deployed. Similarly in a use case of smart home issues like roof damage, water and gas leakage, power consumption can be effectively handled with the help of the sensors and computing devices which would be streaming data at a very regular interval of time. These scenarios indicate huge growth of data in implementation of IoT. Understanding, analyzing the data to produce actionable data would be an essential exercise. Big Data and IoT infuse at this junction.

Iot And Big Data Analytics

Data though collected by the devices need to be filtered to make it relevant and useful. The redundancy in the data being collected is predominant due to the sheer nature of the framework of IoT. The data is continuous hence the extraction of valuable information is not simple. This requires a good mechanism of protocols and software to ensure that the data is secured and also significant. [17] Data is generally collected by the sensor devices in which these devices collect and transmit data to a centralized server. Similarly the data is distributed back to the devices also. These activities require performance efficiency of the network to be optimum. IoT involves a number of heterogeneous networks like wireless Sensor Networks (WSN), Wireless mesh networks, Wireless LAN. These networks would help in transmission of data and also involve various types of quality issues ranging from performance to energy efficiency.

Big Data and IoT are complimentary to each other and are two dimensions of a perception. Managing the data and extracting information from it is a very vital task associated with IoT. An appropriate analytical platform is required to enable to derive knowledge from IoT data. IoT devices generate continuous streams of data in a scalable way. It is essential to handle the high volume of stream data and exploit the data. In a normal scenario Big Data, the data might not be stream data, but the actions are. While in IoT data, it is continuous flow. Applying real time analytics is the need in IoT environment. The advantages of IoT can be seen only when real time analytics is applied on the data stored. Real tim Big Data analytics and IoT equates to value creation which is depicted in fig 2.

Real time Big Data Analytics

Value

Internet of Things

Fig2. Value Creation

Though real time analytics provides a greater avenue to generate actionable data implementing to the fullest is a huge task. Implementing real-time analytics in an IoT environment is challenging due to following reasons

• The large number of IOT devices and volume of data generated

• The need for processing and analyzing data at low latencies

• The need for specialized visualization and reporting

• Non-standardized stack techniques and solutions

Advanced analytics on IoT data would help in analyzing the data collected over a large period of time, and in turn gain a better insight into systems and their behavior .To create models to forecast future outcomes and to optimize the same [18]. Collect information to estimate factors that would not be directly measured by sensors, by determining the relationship between different system parameters, and their impact on each other.

TECHNOLOGY AND TECHNIQUES IN BIG DATA

Technology and techniques in big data are large, varied fast growing extracting relevant information. Technologies and analytical techniques employed, attacking a big data project being analyzing and researching. Several software technology products are available. Hadoop is key technology used to handle big data. Apache Hadoop is an open source frame work that deals with distributed computing of large data sets across clusters of computers using simple programming models. Its software library is a frame work Hadoop is designed to scale up from single servers to thousands of machine. Major advantages that Hadoop offers are we can use inexpensive hardware. Hadoop distributed file system provides high-throughput access to application data stores large amounts of data. HBase is a scalable and distributed database supports structured data storage for large tables provides for transactional kind of capabilities by allowing updates inserts deletions etc; HBase allows for random check. Pig a high level data flow language and execution frame work

parallel computations. Apache Pig is a scripting language, Map reduce transformations including summarizing. Hive a data warehouse infrastructure that provides data summarization software tool used for managing and analyzing large datasets. Hive QL is SQL like language. SQL is traditional languages. Sqoop software tool designed to transfer bulk data. Sqoop is used to import data from external data bases into HDFS or HBASE. Zookeepers a high performance co-ordination service for distributed applications. It is a centralized service used for maintaining configuration information named registry. Avro is a data serializations system. Cassandra a scalable data base with no points of failure. Apache Cassandra is a high availability, capability with multiple servers. Tez a generalized data flow programming frame work, Tez is being adopted by Hive, Pig and other framework in the Hadoop eco-system. Apache spark is a fast data analytics. Flume is a reliable distributed service for efficiently collecting aggregating and moving large amount of Big Data. Hadoop frameworks base characteristics provide deep information on the various components perceptive.

Challenges of IoT

IoT can change the way of working of Internet but also has challenges which need to be addressed. The following are few key challenges:

• Naming and Identity Management: Identification of the huge number of devices connected is to be done in a unique and dynamic way.

• Interoperability and Standardization: Standardization of the devices to have interoperability among the devices is major requirement.

• Information Privacy: The data being captured can be vital and privacy of it has to be considered.

• Objects safety and security: The devices security and safety is a concern as there can be physical damage done to distributed devices.

• Data confidentiality and encryption: Data being transmitted needs to be encrypted to ensure the data is not misused.

• Spectrum: The spectrum on which the devices would transmit information needs to be efficient one.

• Green IoT: The energy consumption by the devices would be large if efforts to minimize the consumption is not done.

  CONCLUSION

  This paper focuses on the fusion of IoT and Big Data and the role of real time analytics in IoT. IoT is a emerging technology which can drive a new wave of application of analytics into the regular routines of humans. The scalability of IoT would lead to smarter applications in various domain ranging from health care to smart and secure homes. The actionable intelligence obtained by application of real time analytics on the data or Big Data of IoT is one of the main benefit of IoT. To tap in the advantages of IoT, Big Data analytics is needed.

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