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      Copy Adaptive Routing Algorithm Based on Network Connectivity in Flying Ad Hoc Networks

      1 , 1 , 2 , 1 , 1
      Mobile Information Systems
      Hindawi Limited

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          Abstract

          The Flying Ad Hoc Network (FANET) is a kind of mobile network which is composed of flying UAVs (Unmanned Aerial Vehicles) as network nodes. With the high node mobility and dynamic topology, data transmission in FANETs is faced with severe challenges, such as unstable communication links and limited resources in the network. Due to the uncertainty of node encounters, data transmission is usually carried out by opportunistic transmission. However, the opportunistic delivery of data using multicopy routing not only consumes a large amount of network resources but also causes too much transmission overhead and low delivery quality. Thus, this paper designs a connectivity-based copy adaptive transmit routing algorithm (CCAT). Considering the network interruptions caused by high-speed node movement, CCAT makes packet forwarding decisions by estimating network connectivity and uses node transmit prediction value as forwarding utility value to control the number of copies of packets. Simulation results show that the delivery rate of CCAT can be improved by about 30% at the peak compared with traditional multicopy routing strategies and the transmission latency of CCAT is minimal.

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          Most cited references19

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          Survey of Important Issues in UAV Communication Networks

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            A delay-tolerant network architecture for challenged internets

            Kevin Fall (2003)
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              FCNS: A Fuzzy Routing-Forwarding Algorithm Exploiting Comprehensive Node Similarity in Opportunistic Social Networks

              At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neighbors. However, when evaluating the similarity degree between a pair of nodes, most existing algorithms in opportunistic social networks pay attention to only a few similar factors, and even ignore the importance of mobile similarity in the data transmission process. To improve the transmission environment, this study establishes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the mobile and social similarities) in opportunistic social networks. In our proposed scheme, the transmission preference of the node is determined through the fuzzy evaluation of mobile and social similarities. The suitable message delivery decision is made by collecting and comparing the transmission preference of nodes, and the sustainable and stable data transmission process is performed through the feedback mechanism. Through simulations and the comparison of social network algorithms, the delivery ratio in the proposed algorithm is 0.85 on average, and the routing delay and network overhead of this algorithm are always the lowest.
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                Author and article information

                Contributors
                Journal
                Mobile Information Systems
                Mobile Information Systems
                Hindawi Limited
                1875-905X
                1574-017X
                October 4 2021
                October 4 2021
                : 2021
                : 1-11
                Affiliations
                [1 ]School of Information Engineering, Henan University of Science and Technology, Luoyang, China
                [2 ]School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
                Article
                10.1155/2021/8580795
                47d97e67-b181-4b90-b8c1-5318a389e787
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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