A customer is deploying sensors with Cisco IR829 routers in moving trucks to continuously monitor the health of engines using a cloud application. Which data extraction and processing strategy is best

Question

Options

• ANo need to store data locally, upload in real time to the cloud for processing.
• BGenerate local alerts and create reports at the edge, and upload to the cloud at the end of the
• CUse the store and forward mechanism to upload the information at the earliest to cloud.
• DEnsure that data is stored for a longer duration locally and upload to the cloud every week.

• A
  29% (10)
• B
  50% (17)
• C
  6% (2)
• D
  15% (5)

Explanation

Option B is correct because trucks in motion experience intermittent or unreliable connectivity, making real-time cloud uploads impractical - processing data locally at the edge (on the IR829 router) ensures immediate alerting and reporting without depending on a constant WAN link, then uploads to the cloud when connectivity allows.

• Option A fails because moving trucks cannot guarantee continuous internet connectivity; relying solely on real-time uploads means data loss or processing gaps whenever the truck is in a dead zone.
• Option C (store-and-forward) is close but insufficient - it prioritizes getting data to the cloud as fast as possible but skips local processing, meaning alerts and analysis are delayed until upload occurs, which is unacceptable for real-time engine health monitoring.
• Option D fails because a weekly upload cycle is far too infrequent for health monitoring - engine faults require timely detection, not a week-old report.

Memory tip: Think "Edge first, cloud second" - whenever you see mobile/vehicle deployments with unreliable connectivity, the answer favors local edge processing for immediacy and cloud upload for aggregation, not raw real-time streaming or delayed batch uploads.

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