Research Area:  Internet of Things

Abstract:

The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing protocol. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The simulation outputs revealed that our scheme performs more efficiently in load balancing, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, control packet overhead and Remaining Useful Lifetime in designed scenarios compared to other methods. Moreover, the simulation results show an out-performance of rival schemes in terms of remaining energy and network lifetime.

Keywords:  

Author(s) Name:  Mahmoud Alilou, Amin Babazadeh Sangar, Kambiz Majidzadeh & Mohammad Masdari

Publisher name:  Springer

DOI:  10.1007/s11235-023-01075-5

Volume Information:  Volume 85, pages 289-312, (2024)

Paper Link:   https://link.springer.com/article/10.1007/s11235-023-01075-5