Edge Processing: A LoRa-Based LCDT System for Smart Building With Energy and Delay Constraints

A smart building is an emerging technology that has the potential to be used in a variety of ubiquitous computing applications. The majority of existing work for smart building monitoring consumes a significant amount of energy to communicate the sensory data from the building to the end users (EUs). This work presents a low-cost data transmission (LCDT) system for a smart building in the context of a noisy environment. The system uses the long-range (LoRa) communication protocol to conserve energy and enable long-distance communication. The smart building sensors generate data in the form of a multivariate time series (MTS). The system compresses such an MTS before transmission by utilizing deep learning (DL) techniques. A channel to reduce the transmission noise of sensory data is also designed using the DL method. The system decompresses the received data at the receiver end and obtains the original MTS. Additionally, we also conducted experiments to demonstrate the utility of the system. The experimental results demonstrate that selecting a finite number of distinct edge device (ED) types aids in developing an LCDT system subject to energy and latency constraints.

Automated summary

A low-cost data transmission system for smart buildings is developed using the long-range communication protocol and deep learning techniques. The system compresses and transmits the multivariate time series data generated by smart building sensors, while also reducing transmission noise. Experimental results demonstrate the effectiveness of the system, especially when a finite number of distinct edge device types are selected.