Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods

Wireless Sensor Networks (WSNs) are the main data collection tools used by Internet of Things (IoT) devices. The WSN-based IoT is a collection of several small, geographically dispersed, battery-powered sensors that are devoted to carrying out a certain activity in a collaborative manner. In a dense WSN-based IoT network, numerous sensors that are near to one another simultaneously collect the same data about the occurrence. Even though WSN-based IoT has opened up previously unimaginable possibilities in a variety of application areas, they are still susceptible to resource limitations. The energy of nodes, which is needed to run well for extended periods of time in many activities, is the most crucial resource in a given WSN-based IoT. Increasing the lifetime of the network is a major focus of research in the field of WSN-based IoT because it is impossible to replace or recharge batteries in remote, harsh or dangerous environments. In this article, an energy-efficient fuzzy-based unequal clustering with a sleep scheduling (EFUCSS) protocol for IoT that uses WSN is proposed. This protocol makes the network last longer and uses less energy. It does this by using clustering, scheduling, and data transmission. Unequal clusters based on Fuzzy C-Means are formed using this protocol to balance the energy used via reducing the distance that data travels. The selection of the cluster head is carried out using fuzzy logic system. The gateway’s (GW) distance, remaining energy, and centrality are input variables. The output fuzzy variable is chance. Fuzzy inference is performed using the Mamdani technique. The sleep scheduling strategy is used between the coupled nodes to reduce the number of transmitted nodes. Extensive Python-based simulation experiments are run in order to evaluate the performance of the proposed EFUCSS protocol. While taking into account different WSN-based IoT scenarios and several criteria, such as network stability, network lifetime, and energy efficiency, a comparison is made between the proposed EFUCSS protocol and other well-known conventional protocols. The results show that the proposed EFUCSS improves remaining energy by 26.92%–213.4% and network lifespan by 39.58%–408.13%. The suggested EFUCSS also results in a greater improvement in network lifespan compared to other comparable algorithms.