Irrespective of whether the environment is wired or wireless, routing is an important challenge in networks. Since mobile ad hoc networks (MANETs) are flexible and decentralized wireless networks, routing is very difficult. Furthermore, malicious nodes existing in the MANET can damage the routing performance of the network. Recently, reinforcement learning has been proposed to address these problems. Being a reinforcement learning algorithm, the Q-learning mechanism is suitable for an opportunistic routing approach because it not only adapts to changing networks, but also mitigates the effect of malicious nodes on packet transmission. In this study, we propose a new reinforcement learning routing protocol for MANETs called reputation opportunistic routing based on Q-learning (RORQ). Using this protocol, which works based on game theory, a reputation system can detect and exclude malicious nodes in a network for efficient routing. Thus, our method can find a routing path more effectively in an environment attacked by malicious nodes. The simulation results showed that the proposed method could achieve superior routing performance compared with other state-of-the-art routing protocols. In addition, compared to other algorithms, the proposed method demonstrated gains of up to 55% in terms of packet loss, up to 82% in terms of average end-to-end delay, and up to 28% in terms of energy efficiency in the blackhole attack scenario and up to 73% in terms of packet loss, up to 35% in terms of average end-to-end delay, and up to 12% in terms of energy efficiency in the gray hole attack scenario.
Computation offloading is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we propose a new computation offloading model for the 5G networks and beyond. Based on the edge computing platform, intensive computing tasks can be partially offloaded from local devices to edge clouds to supplement the computation capability of resource-limited devices. This approach leverages the edge server's idle computing power to assist individual devices in model training. To implement control decision algorithms for the distributed computing process, we adopt the concepts of different bargaining solutions for the dynamic offloading services. According to the cooperative game theory, the proposed method can maximize the full synergy that gives mutual advantages for devices and edge clouds while improving the system efficiency. Therefore, we can take various benefits to reach a fair-efficient consensus under the edge-assisted distributed computing system environment. Finally, experimental results demonstrate the effectiveness of our bargaining based computation offloading scheme by comparing with the existing state-of-the-art distributed computing protocols; we can accelerate training process thanks to our efficient bargaining approach.
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