## 4 Framework Components
### Big Data  
Gartner's IT glossary states that \"Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.\" [27].  
In the current day's \"always-on\" and \"connected\" world, information is generated at high speeds (velocity) and in huge volumes. In addition to this, the data come in a variety of data formats. Figure 3 depicts the 5V paradigm of big data.  
* Variety Sensors use different technologies to measure the physical characteristics of various real-world parameters. Thus, invariably, the data generated either in terms of format or structure varies from sensor to sensor. It also means that the processor of sensors' data should be prepared to process a variety of data.
* Volume Sensors generate data and are often configured to generate the data continually, and  
Figure 2: Schematic representation of basic blockchain.  
thus, any IoT system data will be generated in large numbers when compared to conventional methods.
* Velocity Sensors are not only generated in large volumes from multiple sensors but also in a fleeting period. Since the speed of data generation is fast, the data processors should consume data in real time before the expiry of data currency.
* Veracity Since the IoT datasets are characterized by high volume, velocity, and variety, the data processors should have a built-in capability to check whether the data are consistent or have no missing values, so that the outcome of processed data is reliable.
* Value The real value from the 4 Vs (volume, velocity, variety, and veracity) can only be extracted when real-time analytics are applied. Big data infrastructure is the enabler that is crucial to deriving the value of IoT systems' data. However, not all data have value. It requires a bit of acumen to make a judgment on identifying useful and actionable data.  
Conventional data handling techniques like traditional RDBMS are not only inefficient but also incapable of handling big data. A special file system (ex. Hadoop) is required for big data analytics and is usually hosted on a cloud service provider's IT infrastructure with sufficient storage capacity and network bandwidth to handle the volume and velocity. [PERSON] et al. have explored the use of IoT data in conjunction with big data analytics and AI/ML [28] and potential impacts on productivity and higher quality. IoT systems generate data continuously with great velocity, but the data are of little use if not consumed in real time to draw meaningful conclusions. While big data systems host high-volume and high-velocity sensor-generated data, AI/ML algorithms consume such data and provide continuous feedback on the quality.  
In the proposed framework context, big data is a key component as the IoT sensors gather and generate huge volumes of information in a variety of data formats. Also, the framework proposes to aggregate the data from sensors (located at innumerable locations) onto a cloud provider's (ex. AWS, Azure, etc.) infrastructure to address any scalability issues.  
Figure 3: The 5 Vs of the big data paradigm.