Retrofitting analogue meters with smart devices : A feasibility study of local OCR processes on an energy critical driven system

Abstract: Internet of Things (IoT) are becoming increasingly popular replacements for their analogue counterparts. However, there is still demand to keep analogue equipment that is already installed, while also having automated monitoring of the equipment, such as analogue water meters. A proposed solution for this problem is to install a battery powered add-on component that can optically read meter values using Optical Character Recognition (OCR) and transmit the readings wirelessly. Two ways to do this could be to either offload the OCR process to a server, or to do the OCR processing locally on the add-on component. Since water meters are often located where reception is weak and the add-on component is battery powered, a suitable technology for data transmission could be Long Range (LoRa) because of its low-power and long-range capabilities. Since LoRa has low transfer rate there is a need to keep data transfers small in size, which could make offloading a less favorable alternative compared to local OCR processing. The purpose of this thesis is therefore to research the feasibility, in terms of energy efficiency, of doing local OCR processing on the add-on component. The feasibility condition of this study is defined as being able to continually read an analogue meter for a 10-year lifespan, while consuming under 2600 milliampere hours (mAh) of energy. The two OCR algorithms developed for this study are a specialized OCR algorithm that utilizes pattern matching principles, and a Sum of Absolute Differences (SAD) OCR algorithm. These two algorithms have been compared against each other, to determine which one is more suitable for the system. This comparison yielded that the SAD algorithm was more suitable, and was then studied further by using different image resolutions and settings to determine if it was possible to further reduce energy consumption. The results showed that it was possible to significantly reduce energy consumption by reducing the image resolution. The study also researched the possibility of reducing energy consumption further by not reading all digits on the tested water meter, depending on the measuring frequency and water flow. The study concluded that OCR processing is feasible on an energy critical driven system when reading analouge meters, depending on the measuring frequency.