A method for preserving battery life in wireless sensor modes for lora based IOT flood monitoring using fuzzy logic-based adaptive sampling algorithm

Abstract

A common challenge in the implementation of Internet of Things (IoT) Wireless Sensor Networks (WSN) is that the sensor nodes are known to be power hungry devices. The energy stored in the batteries which power up these sensor nodes deplete quickly especially when more data is transmitted to the cloud or when multiple sensors are attached to a single sensor node. In the context of flood and environmental monitoring, increasing the number of sensor nodes in a Wireless Sensor Network is desirable to increase the spatial resolution of the data and hence achieve better representation about rising water levels and overall water quality in a particular city. However, having more sensor nodes in a Wireless Sensor Network results in more challenges for the power supply management of the overall Wireless Sensor Network. A drawback of the sensor nodes which are usually powered by Lithium-ion batteries is that there is a limited number of cycles in which a battery can be charged and discharged before the battery is fully degraded and therefore methods which can lengthen the duration of each charging and discharging cycle will be useful to increase the overall battery longevity. In this thesis, a method which combines solar energy harvesting together with a fuzzy logic-based algorithm for adaptive sampling is proposed to achieve a continuous source of energy for the sensor nodes and increase the duration between each charging and discharging cycle resulting in batteries which can last for a longer duration. The sensor nodes will also be using LoRa module for communications as this technology meets the requirement of having low power configurations. A comparison of the sensor node deployed for flood monitoring with and without the proposed algorithm is then presented in the results chapter. Keywords: Internet of Things (IoT), Wireless Sensor Network, Logic-based Algorithm.