Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.
Abstract-This paper addresses the challenge of developing techniques for the effective teaching of computer network design. It reports on the experience of using the technique of problem-based learning as a key pedagogical method for teaching practical network design within the context of a Master's program module in data telecommunications and networks at the University of Salford, Salford, Greater Manchester, U.K. A two-threaded approach was adopted that comprised a problem-based learning thread and a conventional lecture thread. The problem-based learning thread within the module comprised sessions designed to place the students in the position of network design consultants who are introduced to scenarios that have a high degree of realism in which a client has specific business requirements that can be met through the adoption of a network solution. In this way, the problem-based learning thread allows the students to develop their design skills, while the lecture thread uses traditional teaching methods to allow students to develop their understanding of key network components and architectures. A formal evaluation of this approach has been carried out and demonstrated a very effective and realistic learning experience for the students. Therefore, the authors propose that problem-based learning is an ideal pedagogical tool for the teaching of computer network design.Index Terms-Computer network design, M.Sc. degree, problem-based learning.
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