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To remedy this issue, we can use the relevant verified truths, which are some already known best recommended routes near the query locations, to evaluate these routes.
We develop a truth reusing component to reuse the verified truths and known best routes near the query locations to evaluate the routes and quickly return to users, which notably reduces the response time.
Each node in the routes of interest rebroadcasts the query at most once and keeps the reverse path entry to its immediate query sender.
Now, it has to recognize that the speaker said "Alexa" at the beginning of the query, then route it to the Alexa service.
A solution to the localization problem can specify a set of sensor nodes on a path that gather and combine data as they route the result back to the querying node.
Similarly, the query on the route that has the most non-on-time events over a period of time can be easily implemented.
The pursuer periodically informs the network of its position by picking a node in its proximity to route a query to the landmark.
Note that the source node can receive multiple QR messages for each query it has initiated because the query traverses multiple routes to the destination.
Thus in any route path, any node before it is covered by the query, is considered unlucky node.
Thus, instead of selecting a fixed plan, AMR dynamically routes batches of tuples to operators in the query network based on up-to-date system statistics.
A node nearest to the destination that receives the query message returns the query reply containing road traffic information of the forward route back to the source.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com