Abstract
The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.
Highlights
The cold storage market size was valued at $29.08 billion in 2016, and it is expected to expand at a compound annual growth rate (CAGR) of 9.9% from 2017 to 2025 [1]
The film heater works for 130 s once it turns on, as we found through repeated tests on our platform that this prevents frost-forming, except for the first 10 s when the mobile terminal is moved from the cold warehouse to room temperature
Thanks to the progress of artificial intelligence (AI) technology, deep neural networks for such specific tasks can be developed in small sizes to fit the memory and computing capacity of industrial mobile terminals
Summary
The cold storage market size was valued at $29.08 billion in 2016, and it is expected to expand at a compound annual growth rate (CAGR) of 9.9% from 2017 to 2025 [1]. What the trend tells us is that more industrial mobile terminals will be used to scan products in a low-temperature environment such as cold-storage warehouses, and the demand will grow further. These industrial mobile terminals are typically equipped with a film heater in the scanner window. This is to remove the frost that the scanner window may accrue when the terminal comes out of the cold storage to the room temperature outside. The moisture in the outside air condenses on the cold surface of the window when the terminal-carrying worker exits the warehouse. When the worker returns to the cold warehouse before the frost melts, the terminal cannot function properly because the frost blocks the view of the scanner
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