A Novel Chain Formation Scheme for Balanced Energy Consumption in WSN-based IoT Network

In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environmental phenomena in the targeted region. The energy consumption imbalance of sensors becomes the cause of the network performance decrement, as sensor nodes have limited energy available for operation after being randomly deployed. Therefore, more research is necessary for the design of energy-efficient routing algorithms in energy-constrained WSNs. This paper focuses on the chain-based routing algorithm, which is a popular algorithm for achieving energy efficiency in WSN-based IoT network. Chain-based routing algorithms offer numerous advantages for WSNs, such as energy conservation and extended lifetime of WSNs. However, they face challenges due to the issue of internal communication imbalance. The objective of our study is to design a novel chain formation scheme that improves the energy consumption imbalance caused by internal communication in WSN-based IoT network. The proposed scheme is categorized in three phases (initial communication phase, chain formation phase, and data collection phase). In the first phase, the sink acquires their location information from sensors deployed in the sensing region. Then the sensing region is separated into sub -regions and with the number of sensor nodes is balanced employing the concept of the k-dimensional binary tree (K-D-B-tree). The sub -regions are organized into a binary tree structure, which is then formed into a chain. Lastly, data is collected along the chain, and the selected representative sensor transmits the collected data to the sink. We utilized the OMNET++ simulator and demonstrated effective simulation results in terms of network lifetime and average residual energy. In the simulation results, a novel chain formation scheme outperforms the power-efficient gathering in sensor information systems (PEGASIS) and the concentric clustering scheme for efficient energy consumption in the PEGASIS (CCS).