Abstract
Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.
Highlights
Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions
Reinforcement learning (RL) problems are formalized as Markov decision processes (MDP) with a tuple (S, A, P, R), where S represents a set of states an agent can be in at a given time t; A is a set of possible actions an agent can take. e transition probability that an agent at a given time t, and from a given state s(t) which performs an action a(t) to enter in state s(t + 1), is denoted as P, and R is the reward obtained by the agent for the action performed [18]
We proposed a cluster-based energy-efficient routing protocol for IoT using Reinforcement Learning, named EER-RL. e objective of this work was to optimize energy consumption and prolong the network lifetime by finding an optimal route for data transmission
Summary
RL problems are formalized as Markov decision processes (MDP) with a tuple (S, A, P, R), where S represents a set of states an agent can be in at a given time t; A is a set of possible actions an agent can take. e transition probability that an agent at a given time t, and from a given state s(t) which performs an action a(t) to enter in state s(t + 1), is denoted as P, and R is the reward obtained by the agent for the action performed [18]. Applying RL to routing protocols requires defining the main components of an RL model, such as agent and environment, state and action, and reward. E state space for an agent is the available routing information from all available neighbouring devices. Us, the action space represents the set of all available routes through neighbours at a given time. E following definitions are considered for the implementation of the proposed protocol: (1) every device in the network is considered an agent, and (2) for each device, the set of available routes through its neighbouring devices to the base station is the state space. (3) e set of all available neighbours through which packets can be sent to the base station is denoted as the action space.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
