In IoT networks, RL can be used to deal with problem such as network topology change due to mobility of devices, energy level, and other transmission parameters such as distance, signal strength, and bandwidth, which can change over time and influence the network performance. This approach has brought dynamism in data routing and adaptation capability in network communication compared to static routing approaches. In other words, the agent interacts with the environment by performing actions and gets rewards, which can be either positive when the action performed was right or negative otherwise. Reinforcement learning is a subfield of machine learning that solves the problem of an agent that takes actions in an unknown environment and improves over time through a sequence of trial-and-error interactions with the environment. Hence, energy-efficient routing protocols are known to manage the consumption of devices’ available energy and extend the lifetime of the network. Energy efficiency is crucially important to maintain a fully operational network for the most prolonged time possible, especially for devices deployed in a harsh environment where recharging and replacing the battery are impossible. However, to accommodate a large number of devices in an IoT, several requirements are needed including energy efficiency, scalability, interoperability, security, and flexibility. Logically, the power unit does not consume any energy but supplies energy to other modules, the sensing module and processing module also consume negligible energy, whereas the communication module is the most energy-consuming. Finally, the power unit consists of a small battery that supplies power to the remaining three modules. The communication unit is in charge of sending packets across the network. The sensing unit is responsible for sensing data from the surrounding environment, whereas the processing unit carries out the computation tasks. Generally, a sensor device comprises four units, namely, power unit, sensing unit, processing unit, and communication unit. IoT consists of the interconnection of heterogeneous wireless devices including smartphones, wireless sensors, actuators, identification by radio frequency (RFID) tags, and real-world things with sensing capabilities. Due to its implications to various fields, IoT has recently received much attention, and it has been applied to a wide range of applications such as smart cities, smart healthcare systems, smart homes, object tracking, disaster management, and environmental monitoring. Through wireless communication, these objects can interact with each other and enable the system to be remotely controlled via Internet connection. Initially, IoT has been targeted to the network of RFID tags and later it has been broadly extended to various devices and applications with the goal to first make objects capable of learning and understanding their environment and interact with it.
IoT allows real-world things and people to be connected and be part of the virtual world of the Internet through wireless communication. The latter has become the backbone for ubiquitous computing while enabling the environment to be smart through recognition, identification of objects, data generation, transmission, and retrieval. The emergence of wireless technologies and information systems and mobile technologies has opened up a new era for the Internet of things (IoT). 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. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. 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.
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.
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