Effective Lossless Compression of Sensor Information in Manufacturing Industry

Abstract

Abstract-In recent years, many visions for hitherto considered-futuristic computing applications gained momentum. The vision of smart factories is one example for these trends, which all share a common requirement: the prolific dissemination of sensor information. As wireless communication in smart factories needs to cope with harsh environments, the amount of sensor information produced by sources will likely surpass the communication channel's available capacity. This discrepancy calls for efficient communication and filtering protocols, as well as compression mechanisms, as a foundation for dependable applications. We propose such a compression algorithm that is lossless and tailored towards the requirements of the manufacturing industry. Our algorithm employs a two-step stochastic model that uses lossy compression to extract an approximation from the signal and a separate noise model to accommodate the remaining error. Evaluation results validate that our algorithm achieves better compression rates than existing approaches for several types of real world sensor data from the industry.

Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review

“…Data compression techniques aim to reduce the data packet size by the node itself or by an aggregator node. By applying a data compression algorithm, such as Kalman filter, lossless compression and adaptive model selection [ 127 , 128 , 129 , 130 ], the data packet size can be significantly reduced. However, depending on the adopted algorithm, the data may be not accurately recovered, which results in a loss of precision.…”