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Connected & Autonomous Vehicle: Trip Analysis

Posted by Jai on January 25, 2017

In this post, we will explore to make sense of connected vehicle trip related data and how the vehicle driving information can further be used using data analytics techniques which can be useful for consumer and business also.

Trip Data

We have already have covered what kind of data can be available from connected vehicle in the previous post, IOT Data: Connected & Autonomous Vehicle, Making sense of data. Based on that data we take into account the vehicle driving information available to us.

The information in real scenarios can come from multiple sources. In case of connected and autonomous vehicle, it will come from vehicle itself. In case the vehicle is not connected to such devices, user phone information can be used to identify a trip movement information.

Vehicle Trip Definition

For the connected or autonomous vehicles, the data information we retrieve from the vehicle is the ignition on/off. Now we need to turn this information into meaningful business value trip information.

The functional start/stop information may not be directly mapped to a consumer or business ready definition of a trip. End consumer may switch on/off vehicle multiple times in a span of short period, you can stop your vehicle on a signal, you can also take a break in between your journey etc.

Vehicle trip is logical derivation on vehicle start/stop information. Taking into account the time between consecutive start/stop and small stops during the journey a trip is defined.

Vehicle Trip Analysis

Based on trip information, we will explore what all information can be driven with the data and how it can add business value.

The analysis can be categorised in different buckets,

  • Per consumer trip analysis : How each consumer is doing in terms of driving mileage and time for vehicle.
  • All consumers travel time analysis: What are the most frequent travel time for consumers
  • Total travel behaviour and movement information: How vehicle movement happens

Per Consumer Trip Analysis

In case of connected car solution, from end consumer perspective, lot of analytics information can be provided to the end consumer. Detailed analysis of driving distance/time information and effective travel time can be shared.

Daily Distance Travelled

Detailed analysis of daily distance travelled by a consumer,


As shown above, daily distance travelled by the consumer for a month. Based on the consumer mileage information, consumer can see average distance travelled and on what days vehicle is mostly driven.

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IOT Data: Connected & Autonomous Vehicle, Making sense of data

Posted by Jai on January 19, 2017

In this post, we will explore the possibilities of connected & autonomous vehicle driving data availability, and making sense of this IOT data to deliver stack of services for all the parties involved like customers, manufacturers, governance bodies and dealers etc. and creating host of service for complete ecosystem.

Connected/Autonomous Vehicle

The trend is in coming years by 2025-2030 all the vehicles will be connected to internet in one or other way. As per the report,

Connected car report 2016: Opportunities, risk, and turmoil on the road to autonomous vehicles

Scenario for value shifts in the auto industry, 2015–30

Estimated connected car revenues (and market share) by product package, 2015–22

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IOT Security: Attack surface areas, Vulnerabilities & Considerations

Posted by Jai on January 5, 2017

In this post we will discuss the security aspects around connected devices solution. As typically goes in M2M information flow, from connected devices hardware till the information is available to end-user on dashboard or mobile gadget.

There are varieties of connected devices getting in market day by day. The size and processing power of each of such devices put extra emphasis on the security aspects around these devices. Here we will discuss the information flow in case of connected devices, how data flows from devices to end users of those devices. On top of that what are different attack surface areas, what common vulnerabilities those expose and what are the common ways to eliminate those threats for connected devices.

In the previous posts, we discussed about availability of different type of IOT platforms in market, typical characteristics of such a platform and the technical challenges in building your own IOT platform.

IOT Platform: Typical Characteristics
Building IOT platform, Technical challenges

Below diagram explains the typical IOT data flow, possible vulnerabilities is such system and the considerations to build strong and secure security architecture model for such solution.


Information Flow Networks

In a typical IOT scenario, the device captures the data, either using local gateway or telecom the data is transmitted to cloud server for further processing. The processed data is further made available to end consumer or respective events are triggered or information shared further with ecosystem parties.

Commonly below different data flow networks are used in the system,

  1. Device Network
  2. Device Communication Network
  3. Telecom Network
  4. Internet
  5. Hosting/Cloud environment Network
  6. IOT Platform
  7. Mobile applications/Gadgets network
  8. Data Privacy

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Building IOT platform, Technical challenges

Posted by Jai on January 2, 2017

As discussed in the last post regarding the typical characteristics of IOT platforms, there are different categories of available IOT platform these days and typical feature and functional support provided by each. Device connectivity, device agnostic, domain agnostic and extensibility etc. drive to come up with few required technical aspects of such a platform. In this post, we will discuss different technical aspects to be taken into account to build such IOT platform.

Typical characteristics of any IOT platform are as covered in last post.

In general, we can add below typical technical aspects in the architecture of such IOT platform


  1. Device Connectivity
  2. Device repository and management
  3. Data Processing of Events/Rules Management
  4. Alerting, Monitoring & Notification support
  5. Complex Event Processing support
  6. Scalable & Extensible Data storage layer
  7. Real time Data access support
  8. Data Analytics
  9. Data Visualisation support
  10. External Ecosystem system Integration support
  11. IOT Security, Across all layers
  12. Development/Testing Support
  13. Testing/POC/Time to Market support
  14. Device Bi directional support
  15. Device Firmware management support
  16. Data reporting/export/publish/share support

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IOT Platform: Typical Characteristics

Posted by Jai on June 29, 2016

There are common characteristics for iot platforms but it is indeed very difficult to benchmark iot platform solution as many of these try to solve fundamentally different problems. In this post we will cover some of the typical characteristics which such a solution should have.

IOT Platform Categorisation

IOT Platforms

IOT Platforms

There are lot of IOT platform providers now a days and numbers are growing every year. Each having some very common features and some having quite different features. On high level, we can categories these IOT platforms in different categories,

Connectivity / M2M platforms

To provide connectivity to connected devices with cloud.

IaaS backends

Infrastructure-as-a-service backends provide hosting space and processing power for applications and services.

Hardware-specific software platforms

Domain specific platform supporting only those connected devices.

Consumer/Enterprise software extensions

Existing enterprise software packages and operating systems allowing the integration of IoT devices.
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