Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs

Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.

Automated summary

This article proposes an intelligent routing scheme based on deep reinforcement learning (DRL) for IoT-enabled Wireless Sensor Networks (WSNs), aiming to increase network lifetime and reduce communication delay. Experimental results demonstrate that the proposed scheme is more efficient compared to state-of-the-art algorithms in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.