A Knowledge Graph Driven Approach for Edge Analytics
Messaging Backbone and Application Services
The messaging backbone ( shown in Figure 7 ) allows for applications and data streams to independently interact with each other without having to previously determine which applications and sensor streams are present on each device .
We utilize NSQ 7 as the default message broker because it is fault-tolerant , dynamically scalable , has simplified configuration , no single points of failure and is primarily in-memory ( bounded volatile memory with disk overflow ) so that the applications have a lower latency to access sensor data .
Integral to the operations of the messaging backbone are the protocol converters which support it . These translators are dynamically instantiated to convert device specific data protocols ( MQTT , Serial , etc .) to the consuming NSQ format applications . These converters or containers reside on the edge appliance . They are automatically created by the orchestration layer when the knowledge graph reflects that a new device has been onboarded and connected to the platform .
The application services component provides a software development kit ( SDK )
for the application engineer to parameterize components of the application which vary across device instances . For example , the sensor stream topic names , IP addresses of the message brokers , etc . will all vary . The application engineer needs only to define a parameterized query and the application services will fill in the specifics ( e . g ., an accelerometer stream from the right front wheel well of a vehicle ).
Figure 7 : Edge Appliance Architecture
Containerization
Containerization , providing near native runtime specs 8 , allows for the application engineer to encapsulate nearly all software dependencies of the target device . This allows the platform to orchestrate the
7
“ A realtime distributed messaging platform ”, 2017 . [ Online ]. Available : http :// nsq . io /
8
W . Felter and F . Alexandre , " An updated performance comparison of virtual machines and Linux containers ," in IEEE Performance Analysis of Systems and Software ( ISPASS ), 2015 .
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