# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks  
[PERSON]  
1 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; 1 [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (X.Y.) 1  
[PERSON]  
[PERSON]  
1 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; 1 [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (X.Y.) 1  
[PERSON]  
1 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; 1 [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (X.Y.) 1  
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enhances network life and reduces network load and end-to-end delay. Ref. [9] proposed a multipath routing algorithm based on distance and energy consumption constraints in wireless sensor networks, which selects the neighbor node with the largest weight to transmit data. However, in a dynamic topology environment, it is complicated to accurately calculate the probability of being selected as a relay node determined by multiple performance metrics (e.g., delay and stability). Therefore, in this paper, we propose fuzzy logic to comprehensively deal with the factors that are not precise in delay, stability, and residual energy and screen out high-reliability and low-delay routing paths. Fuzzy logic is a well-known artificial intelligence tool that can combine human experience and digital information to solve the decision making process in a dynamic and constantly changing system. In [10], fuzzy logic was used to infer the weights of different objective functions according to the different type of service required by users to solve a multiobjective resource optimization problem in VANETs. The authors of [11] proposed to extend the life of the network based on fuzzy logic by considering energy efficiency and energy consumption balancing of sensor nodes simultaneously. Based on fuzzy logic, many improved routes have also been proposed. Considering different contradictory indicators, a fuzzy logic framework was proposed in [12] to select the node with a output value of fuzzy logic system greater than the threshold in route request packet (RREQ) as relay node during the route discovery process. Similarly, in [13; 14], the most suitable next-hop node was selected among the candidate forwarders for packet forwarding based on geographic routing. The authors of [15] proposed to use fuzzy logic only to compromise with flight autonomy, mobility level, and received signal strength indicator of nodes to establish communication routing with a longer active period in FANET. In order to reduce the number of hops caused by the optimal fuzzy logic output value node as the relay node, the authors of [16] proposed to combine the reinforcement learning and fuzzy logic into FANET to achieve fewer hops by considering the suboptimal nodes. The authors of [17] made use of fuzzy logic to determine the optimal route, and took into consideration multiple parameters to select routing relay nodes, but fuzzy logic was only used in the destination node. Improving the energy efficiency of routing algorithms through fuzzy logic controller was proposed in [18]. However, these works mainly focused on using fuzzy logic to determine the optimal node without considering reliability and hops simultaneously, which increases the probability of link interruption and delay.