Exploit IoT from the cloud border and vice versa
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According to Pinakin Patel, MapR's senior director of solutions engineering, many use cases require the collection of sensor data from edge devices that are sent over a network connection to a centralized application for distribution to be analyzed before performing a task, then often come back at the border.
This classic input, processing, and output method could be well understood, but any IoT (Internet of Things) environment faces a data management challenge due to the large amount of data generated and inherent latency in global distribution.
Large amount of IoT data
The challenges of aggregating data from consumer-oriented devices, such as wearable technology and smart thermostats, are also well understood. For those types of devices, the large amount of data is due to the large number of devices and each individual device does not generate much data.
However, there are new challenges for IoT devices that produce megabytes or gigabytes of data per second. For example, real-time analysis of video, audio and LIDAR (light detection and ranging - a survey method measures the distance to a target by illuminating the target with an ambient laser and measuring reflected pulses by a sensor) are all areas in which information flows can overwhelm traditional data storage architectures.
Certainly, the infrastructure will have to change, because that amount of data will be able to overwhelm the available bandwidth to aggregate data into a central repository. Vehicles, medical devices and oil rigs are perfect examples of data sources that need a much larger architecture than consumer-oriented devices. And as these IoT data streams approach centralized clouds to handle it, artificial intelligence and machine learning will gradually help find knowledge and create further actions.
An example of health care
Each IoT use case will have different drivers and requirements. Early detection and treatment of chronic diseases - such as heart disease - can save lives and reduce healthcare costs. Two of the biggest issues are coordinating care and helping people with chronic illnesses from hospitalization. Some trials are using sensors that can monitor vital signs of patients and send this data along with ECG results over the cellular network as a normal flow for applications on the cloud.
These diagnostic and monitoring applications will analyze each patient's data and ECG readings while reviewing historical data from medical records. Data streaming into the system include real-time streams, historical data, patient data, and standard data generated by aggregating large volumes of data from previous scans of other patients.
Researchers have built a platform that uses common elements to handle both stream data and batch data in common data so that it can help process all data in the same way, control access, update data, apply intelligence to improve performance and enhance capabilities.
An example of transportation
Mojio is an IoT-connected vehicle aimed to create an ecosystem that allows the automotive, insurance and telecommunications industries to grow together. Mojio plans to connect 500,000 vehicles to its cloud platform in the first phase that will provide access to different types of behavioral, diagnostic, and contextual data that vary by user needs.
For example, the behavioral data in Mojio's telecommunications equipment gathers information about speed, direction and braking data to determine driver fatigue and alert the incident. Long-term driving behavior data can also be used to help users adopt more fuel-efficient driving solutions and calculate insurance companies' risks.
As IoT data moves from the edge to the cloud and vice versa, businesses will need to forget the monolithic architectures of the past and consider convergence as a starting point to provide the large-scale that is necessary for creation.
By: Jimmy Saunders