## 1 Introduction
The proposed scheme also employs a Q-learning algorithm [7] to learn the best next-hop forwarder by using control messages. Fuzzy logic can analyze different metrics even if they are imprecise, uncertain, or conflicting with each other. The proposed routing scheme employs fuzzy logic to evaluate wireless link status and find a trade-off between the performance metrics FQ-AGO considers without deriving a mathematical model. Based on the fuzzy logic evaluation, the Q-learning algorithm chooses a potential next-hop forwarder with a highly reliable and stable link. FQ-AGO is different from existing OR routing schemes for MANETs. In every step of FQ-AGO, we infuse a stable strategy; in the candidate selection phase, the FQ-AGO forwarder node evaluates the direct wireless links in its vicinity, and it makes the selection of the next-hop forwarder on-the-fly by employing the Q-learning algorithm as a means to improve network connectivity and routing performance, and the routing decision does not rely on global network information. The proposed routing scheme is scalable since there are no overwhelming control packets required to move the data packets towards the desired destination. By modifying the fuzzy logic membership functions and rules, various MANETs scenarios such as vehicular ad hoc networks (VANETs), or wireless sensor networks (WSNs) can apply FQ-AGO seamlessly. In addition, our approach addresses another challenge in MANETs, namely the high cost of distributing network global routing information. Although the capacity of wireless networks has been studied extensively, little attention has been paid to how practical routing protocols may be best implemented in MANETs. It is impossible to ignore network dynamics in large-scale networks: nodes joining, leaving, or moving, and channels fading. However, the distribution of global routing information incurs high costs. In a network of n nodes, the overhead of a routing protocol, such as the link-state protocol, scales as \(n^{2}\), whereas the total network capacity scales as \(n/log_{n}\)[3; 4], which may be overwhelmed by the routing overhead as the number of nodes increases. We demonstrate that our distributed opportunistic routing scheme is independent of network global routing information and rely on the local routing information to make routing decisions using our approach. In this paper, we make the following contributions:  
1. We propose FQ-AGO fuzzy Q-learning opportunistic routing scheme that learns and makes the route decisions on-the-fly by considering one-hop performance. 2. The proposed scheme takes into account the available throughput, distance progress, asymmetric link, and link quality in the candidate selection process. 3. The proposed protocol is flexible because we can easily tune the protocol to work for different MANETs networks by modifying the fuzzy membership functions and fuzzy rules. 4. We implemented and tested the proposed routing scheme in the ns-3 simulator. 5. We evaluated the performance of the proposed routing scheme and compared it with three state-of-the-art MANETs routing protocols: destination sequenced distance vector routing (DSDV) [8], ad hoc on-demand distance vector (AODV) [9], and geographic opportunistic routing protocol (GOR) [10]. The remainder of this paper is organized as follows. In Section 2, we review the related literature, focusing mostly on the opportunistic routing on MANETs. In Section 3, we describe the system model and the assumptions. A detailed description of the proposed scheme and its objective function and features is in Section 4. In Section 5, we discuss the routing scheme implementation as well as the simulation setup and performance evaluation. Finally, Section 6 concludes the paper.