The details of the seminar is as follows:
Speaker: Dr Alex Weddell
Title: Energy Harvesting for Real Applications
Abstract: Energy harvesting offers the potential to free the Internet of Things from dependence on batteries. In an ideal world, devices would be powered indefinitely from environmental energy, with no need for maintenance or connection to the power grid. However, in the real world, environmental energy resources are highly variable and components may not perform as expected (the energy storage device is known for being particularly problematic). In his talk, Alex will describe the work he has carried out in application-focussed energy harvesting system design, and will also introduce the concept of “intermittent computing” which removes most of the need for energy storage, but requires a new approach to the functionality of devices. He will also introduce Mansour Alzahrani, his PhD student.
Mansour Alzahrani (PhD student)
Title: Using Environmental Data for IoT Device Energy Harvesting Prediction
Abstract: There has been significant innovation in the domain of Internet of Things (IoT) as nowadays wireless data transmission is playing an essential role in various organizations like agriculture, defence, transportation, etc. Batteries are the most common option to power wireless devices. However, using batteries to power IoT devices has drawbacks including the cost and disruption of frequent battery replacement, and environmental concerns about battery disposal. Solar energy harvesting is a promising solution for long-term operation applications. However, solar energy harvesting varies drastically over location and time. Due to fluctuating weather conditions and the environmental effects on PV surface condition, output could be reduced and become insufficient. Environmental conditions including temperature, wind, solar irradiance, humidity, tilt angle and the dust accumulated over time on the photovoltaic (PV) module surface affects the amount of energy harvested. To address this issue, a novel solution is required to autonomously predict the harvested energy and plan the IoT device tasks accordingly, to enhance its performance and lifetime. Using Machine Learning (ML) algorithms could make it possible to predict how much energy can be harvested using weather forecast data. This research is ongoing, and aims to apply ML algorithms on historical weather data including environmental factors to generate solar energy predictions for IoT device energy budget planning.