Abstract

Background: Nowadays, Wireless Sensor Network (WSN) plays an important role in various fields. The limited power capability of the sensor nodes in the WSN brings constraints on the performance of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) is a promising protocol for WSN that suffers from higher energy consumption.

Objective: The primary objective of this study is to give an alternate harvesting resource power to sensor nodes in the LEACH algorithm which can be equally capable of providing the same or sometimes better results.

Methods: This study is based on real-time meteorological data. A real-time wind speed data is taken for the starting of a day to the end of the day on an hourly basis from the weather forecast. Now to convert this rotational energy into electrical energy, we used two types of wind turbines. For the proposed methodology, a micro wind turbine generator and 300watt wind turbine are used. Then this converted electrical energy is given to sensor nodes. For the clustering, the wind power operated nodes are given maximum preference to be elected as the cluster heads based on realtime wind meteorological data. We consider 10 wind-powered sensor nodes. As we increase the number of wind-powered sensor nodes in the network, the performance is increased in terms of a lifetime but then increases the complexity of the network. These wind-powered nodes remain alive in the network. Since the deployment of the sensor nodes is random, each simulation runs for 5 times and the average of first node dead, half node dead and last node dead is considered.

Results: The experimental results for the micro wind turbine generator are compared based on with and without the MPPT controller. MPPT controller gives the maximum power by using the tip speed ratio control, power signal feedback control, and hill climb search control method. Therefore, the network lifetime should be higher for the MPPT based wind generator. Network lifetime and Energy consumption are compared for a micro wind turbine generator and 300watt wind turbine. Finally, the performance of the proposed system is compared with the modified solar LEACH implemented using real-time meteorological data.

Conclusion: This paper has investigated the wind-based LEACH which uses the real-time meteorological data for the selection of the cluster head. Two types of wind generators are considered for the implementation and it is found that the performance of the commercial 300W wind turbine and the micro wind turbine with and without MPPT is almost similar since the data from both wind turbines are given on hourly basis. The performance of the wLEACH is compared with the sLEACH which shows that the network lifespan of the wLEACH is also nearly the same compared to the sLEACH. However, it was found that wind power generation is cheaper and efficient than solar power generation. Therefore, it is inferred that this proposed wLEACH provides a costefficient solution.

Keywords: Low energy adaptive clustering hierarchy, wind LEACH, wireless sensor network, energy consumption, wind turbine model, MPPT.

Graphical Abstract