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Automation

Making the Connected Car ‘Real-time Data Processing’ Dream a Reality

By | Analytics, Automation, AWS, Blogs | No Comments

Written by Jeremiah Peter, Solution specialist-Advanced Services Group, Contributor: Ravi Bharati, Tech Lead and Ajay Muralidhar,  Sr. Manager-Project Management at Powerupcloud Technologies

Connected car landscape

Imagine driving your car on a busy dirt road in the monsoon, dodging unscrupulous bikers, jaywalking pedestrians and menacing potholes. Suddenly, a fellow driver makes wild gestures to inform you that the rear door is unlocked, averting an imminent disaster.

In a connected car system, these events are tracked in near real-time and pushed to the driver’s cell phone within seconds. Although the business relevance of real-time car notifications is apparent, the conception of the underlying technology and infrastructure hardly is. The blog attempts to demystify the inner workings of handling data at scale for an Indian automobile behemoth and equips you with a baseline understanding of storing and processing vast troves of data for IoT enabled vehicles.

The paradigm of shared, electric and connected mobility, which seemed a distant reality a few years ago, is made possible through IoT sensors. Laced with tiny data transmitting devices, vehicles can send valuable information such as Battery Percentage, Distance to Empty (DTE), AC On/Off, Door Locked/Unlocked, etc. to the OEM. The service providers use this information to send near real-time alerts to consumers, weaving an intelligent and connected car experience. Timely analysis and availability of data, thus, becomes the most critical success component in the connected car ecosystem.

Before reaching the OEM’s notification system, data is churned through various phases such as data collection, data transformation, data labeling, and data aggregation. With the goal of making data consumable, manufacturers often struggle to set up a robust data pipeline that can process, orchestrate and analyze information at scale.

The data conundrum

According to Industry Consortium 5GAA, connected vehicles ecosystem can generate up to 100 terabytes of data each day. The interplay of certain key factors in the data transmission process will help you foster a deeper understanding of the mechanics behind IoT-enabled cars. As IoT sensors send data to a TCP/IP server, parsers embedded within the servers push all the time series data to a database. The parsing activity converts machine data (hexadecimal) into a human-readable format (Json) and subsequently triggers a call to a notification service. The service enables OEM’s to send key notifications over the app or through SMS to the end-consumer.

Given the scale and frequency of data exchange, the OEM’s earlier set up was constrained by the slow TCP/IP data transfer rate (Sensor data size: TCP/IP- 360 bytes; MQTT- 440 bytes). The slow transfer rate has far-reaching implications over the user experience, delaying notifications by 6-7 minutes. As part of a solution-driven approach, Powerup experts replaced the existing TCP/IP servers with MQTT servers to enhance the data transfer rate. The change affected a significant drop in notification send-time, which is presently calibrated at around 32-40 seconds.

Furthermore, the OEM’s infrastructure presented another unique challenge in that only 8 out of 21 services were containerized. The rest of the services ran on plain Azure VM’s. To optimize costs, automate scalability and reduce operational overhead, all services are deployed on Docker Containers. Containers provide a comprehensive runtime environment that includes dependencies, libraries, framework and configuration files for applications to run. However, containers require extensive orchestration activities to aid scalability and optimal resource management. AWS Fargate is leveraged to rid the OEM’s infrastructure management team of routine container maintenance chores such as provisioning, patching, cluster and capacity management

Moreover, MQTT and TCP IP brokers were also containerized and deployed on Fargate to ensure that all IoT sensor data is sent to the AWS environment. Once inside the AWS environment, sensor data is pushed to Kinesis Stream and Lambda to identify critical data and to call the AWS notification service-SNS. However, the AWS solution could not be readily implemented since the first generation of electric vehicles operated on 2G sim cards, which did not allow change of IP whitelisting configuration. To overcome the IP whitelisting impediment, we set up an MQTT bridge and configured TCP port forwarding to proxy the request from Azure to AWS. Once the first generation vehicles are called back, the new firmware will be updated over-the-air, enabling whitelisting of new AWS IP addresses. The back-handed approach will help the OEM to fully cut-over to the AWS environment without downtime or loss of sensor data.

On the Database front, the OEM’s new infrastructure hinges on the dynamic capabilities of Cassandra DB and PostgreSQL. Cassandra is used for storing Time Series data from IoT sensors. PostgreSQL database contains customer profile/vehicle data and is mostly used by the Payment Microservice. Transactional data is stored in PostgreSQL, which is frequently called upon by various services. While PostgreSQL holds a modest volume of 150 MB Total, the database size of Cassandra is close to 120 GB.

Reaping the benefits

While consumers will deeply benefit from the IoT led service notifications, fleet management operators can also adopt innovative measures to reduce operational inefficiencies and enhance cost savings. Most fleet management services today spend a significant proportion on administrative activities such as maintaining oversight on route optimization, tracking driver and vehicle safety, monitoring fuel utilization, etc. A modern fleet management system empowers operators to automate most of these tasks.

Additionally, preventive maintenance can help operators augment vehicle lifecycle by enabling fleet providers to pro-actively service vehicles based on vehicular telemetry data such as battery consumption, coolant temperature, tire pressure, engine performance and idling status (vehicle kept idle). For instance, if a truck were to break-down due to engine failure, the fleet operator could raise a ticket and notify the nearest service station before the event occurred, cutting down idle time.

Conclusion

With 7000 cars in its current fleet, the OEM’s infrastructure is well-poised to meet a surge of more than 50,000 cars in the near future. Although the connected car and autonomous driving segment still goes through its nascent stages of adoption, it will continue to heavily draw upon the OEM’s data ingestion capabilities to deliver a seamless experience, especially when the connected car domain transcends from a single-vehicle application to a more inclusive car-to-car communication mode. Buzzwords such as two-way data/telematic exchanges, proximity-based communications and real-time feedback are likely to become part of common parlance in mobility and fleet management solutions.

As the concept of the Intelligent Transport System gathers steam, technology partners will need to look at innovative avenues to handle high volume/velocity of data and build solutions that are future-ready. To know more about how you can transform your organization’s data ingestion capability, you can consult our solution experts here.

Transforming Invoice Processing through Automation

By | AI, Automation, Blogs, Image Processing | One Comment

Written by Jeremiah Peter, Solution specialist-Advanced Services Group, Contributor: Amita PM, Associate Tech Lead at Powerupcloud Technologies.

Automation Myth

According to a recent survey by a US-based consultancy firm, organizations spend anywhere between $12 to $20 from the time they receive an invoice until they reconcile it. The statistic is a stark reminder of how organizations, in pursuit of grand cost-cutting measures, often overlook gaping loopholes in their RPA adoption policy- All or nothing!

This blog makes a compelling case for implementing RPA incrementally in strategic processes to yield satisfactory results. Streamlining the invoice management process is, undoubtedly, a judicious leap in that direction.

Unstructured invoice dilemma

In a real-world scenario, data in invoices are not standardized and the quality of submission is often diverse and unpredictable. Under these circumstances, conventional data extraction tools lack the sophistication to parse necessary parameters and, often, present organizations the short end of the stick. 

Consequently, most invoice processing solutions available today fail to reconcile the format variance within the invoices. The Powerup Invoice Processing Application is a simple Web Application (written in HTML and Python) that leverages cloud OCR (Optical Character Recognition) services, to extract text from myriad invoice formats. Powered by an intelligent algorithm, the solution uses the pattern-matching feature to extract data (e.g. Date MM-DD-YYYY) and breaks free from the limitations of traditional data extraction solutions.

A high-level peek into the solution


Picture by Google.com

Driven by a highly user-friendly interface, the Powerup Invoice Processing Application enables users to upload invoices (png, jpg) from their local workstations. The action invokes a seamless API call to Google OCR service, which returns a long string object as API response. A sample of the string is presented below:

Subsequently, the string is converted to a human-readable format through a script, which uses a Python-based Regex library to identify desirable parameters in the invoice such as date, invoice number, order number, unit price, etc. The extracted parameters are passed back to the web application after successful validation. The entire process lasts not more than 10 seconds. The video below demonstrates how Powerup has successfully deployed the complete process:

Another noteworthy feature of the solution is that it seamlessly integrates with popular ERP systems such as SAP, QuickBooks, Sage, Microsoft Dynamics, etc. Given that ERP systems stash critical accounts payable documents (purchase orders, invoices, shipping receipts), a versatile solution requires integration with the organization’s ERP software to complete the automation cycle. 

A brief look at the advantages offered by invoice processing automation can help you assess the value delivered by the solution. 

The Silver-lining

Picture by Google.com

The adoption of Powerup Invoice Processing Application helps organizations reap the following benefits:

  • Deeply optimized invoice processing TAT resulting in quicker payment cycles
  • Up to 40% cost savings in procurement and invoice processing
  • Highly scalable solution that can process multiple invoices in a few minutes
  • Fewer errors and elimination of human data-entry errors
  • Free-form parameter pattern-matching 
  • Easy integration with ERP software
  • Readily implementable solution; no change required from vendor’s end 

Conclusion 

While procurement teams in various organizations struggle to strike a trade-off between low funds dispensation and high-cost savings, measures that enable them to cut expenses and improve efficiencies in the invoicing process are a welcome respite. 

Tools such as the Powerup Invoice Processing Application can help organizations infuse automation and agility into its processes, as well as, knockdown process complexities into manageable parts. Moreover, the time and cost efficiencies achieved in these undertakings can be passed on to other functions that can significantly bolster the organization’s service offerings. To find out how your organization can be positively impacted, sign up for a free demo session here.