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As Fig. 3a shows, we select a set of CHs that will be included in the itinerary with minimum cost, then we plan itinerary among those CHs.
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Many of the BigApps developers have created services that rate local places or plan itineraries and then let you tell your friends about them.
This is due to the strategy of planning itineraries just among CHs and not all the SNs and by using MST to plan itineraries for MAs.
Another algorithm has been proposed in [29] to plan itineraries for MAs.
In this paper, we propose multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks (MAEF) to plan itineraries for MAs.
To generate the itinerary, we use Algorithm 2. We plan itineraries for mobile agents among CHs by using a minimum spanning tree (MST).
Jetsetter is leveraging its network of more than 200 travel writers to help members plan itineraries for vacations.
Herein, we propose multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks (MAEF) to plan itineraries for MAs among CHs, and then plan alternative itineraries in case of the failure of the first one.
In our proposed approach, we use a clustering method to group SNs in clusters and select a CH for each cluster for data aggregation, then plan itineraries only among CHs using a minimum spanning tree.
The output of this algorithm is a group of CHs V CH that will be used in Algorithm 2 to plan itineraries and the SNs in each cluster Vgroup.
Therefore, to plan itineraries for MAs, it is essential to consider the following three key elements : SNs grouping is a set of SNs that should be visited by each MA.
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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