Abstract-As smart factory trends gain momentum, there is a growing need for robust information transmission protocols that make available sensor information gathered by individual machines and enable their algorithmic exploitation. Wireless transmission enables often-called-for flexibility, yet it poses challenges for reliable and timely transmission of information. This paper proposes a wireless transmission scheme for sensor information of production cycles in industrial environments. We include a preview functionality based on a discrete cosine transform that allows for rapid detection of problematic characteristics. The transmitted information's precision is improved using incremental updates as wireless capacity permits. Further, we include compact meta data that allows receivers to bound the received information's error. Evaluation results show that, even with high packet loss, characteristic features of sensor information are available early, and that error bounds closely follow the actual error.

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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.