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Proposing an Energy-Aware Objective Function Based on Reinforcement Learning for Mobile IoT Networks

Rezagholi Lalani, Sahar | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55248 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Ejlali, Alireza
  7. Abstract:
  8. The presence of IoT has been increased in different aspects of human life. Due to the increasing trend in the number of smart connected devices, routing has become a major challenge in loT systems. Furthermore, due to the energy-constrained embedded devices in the network, which are usually battery-powered, and considering that %97 of the node's energy consumption is due to activities associated with the transceiver module, the energy-aware routing is significantly important. In this regard, the IPv6 routing protocol for low-power and lossy networks (RPL) was standardized to be adopted in IoT infrastructure. Due to the increasing interest in mobile IoT applications in recent years and regarding that RPL was designed for static networks and it can't adapt itself to the dynamic situations of mobile networks, the adaptation of this protocol to these networks has gained significant attention. Packet loss, packet retransmissions, and consequently increment of the node power consumption are inevitable in the mobile networks because of frequent changes in the network topology and disconnections. Therefore, it is important to employ an efficient routing policy by the objective function in this protocol to select the optimal path. On the other hand, the utilization of learning methods in the mobile networks, due to the dynamics of these networks, can provide suitable predictive models for accurate and timely decision-making for such networks. Using machine learning to estimate network conditions, such as device mobility, quality of communication links, and packet transmission with optimal energy consumption can be an effective step to reach an energy-aware routing mechanism in mobile networks. In this research, an energy-aware objective function based on the Q-learning method has been introduced. To perform the simulations of this study, we used the Cooja network simulator in the Contiki operating system.
  9. Keywords:
  10. Internet of Things ; Routing ; Objective Function ; Machine Learning ; Reinforcement Learning ; Energy Consumption

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