Analysis of the efficiency of data transmission in a communication network group of unmanned aerial vehicles
Communication
Аuthors
*, **, ***Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
*e-mail: doc_bor1@mail.ru
**e-mail: nio4@mai.ru
***e-mail: vs@mai.ru
Abstract
Currently, for many tasks it is proposed to use a group (Swarm) of unmanned aerial vehicles (UAV) [1,2,3]. The grouping efficiency is greatly improved when using a communication network, the nodes of which are aircraft included in the grouping.
The movement of the UAV relative to each other causes in the General case, the change of their relative position, resulting in the structure of the network also changes. This leads to the need for each node to periodically update information about the current network configuration.
The basis for the construction of the route is the connectivity matrix of the network, which contains information about the characteristics of the communication channels between the adjacent (i.e., having a direct channel of communication) nodes.
Update the adjacency matrix requires additional exchanges of information between nodes. Because the speed of information transmission is limited, the update of the matrix will be performed for the final time.
The work deals with the problem of estimating the time required to update the adjacency matrices using different algorithms access.
Considered two mechanisms for network access during the upgrade process matrix — cyclic access and random access.
A comparative analysis of access methods showed that in the absence of distortion (or in the case of a small amount of distortion) cyclic access provides higher efficiency compared with random access.
Random access is useful when a relatively high probability of data corruption (of the order of 0.1 and above), a large number of nodes (more than 40) and low network connectivity.
Keywords:
unmanned aerial vehicles, data network, random access, cyclic access, routing, network adjacency matrixReferences
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