The protocol evaluation is an integral part of network protocol design. From the perspective of experimental design, discrete event simulations constitute a middle ground between analytical protocol evaluation and testbeds. They allow precise control of otherwise external influences while supporting more detailed protocol models than analytical evaluations. Compared to testbeds, a major restriction is that existing protocols require a separate implementation in the discrete event model. Creating this implementation model may cause differences between the protocol's simulator-based model and the native implementation, invalidating simulation results. We propose a novel architecture to evaluate unmodified, binary protocol implementations in the state-of-the-art discrete event simulators by utilizing the operating system's system call barrier. Notably, our approach does not affect discrete simulation properties, such as repeatability, and it does not require the native protocol implementation's source code. The evaluation results using existing network protocols show the feasibility of our approach in combination with the ns-3 simulator core. We show that our approach more closely resembles realistic protocol performance when compared to simulator-based protocol models. Moreover, our approach performs better than existing solutions for more realistic protocol simulations.

The trend towards smart factories necessitates protocols for wireless transmission of production process information. Protocols must transfer process information timely even with temporary wireless interference on the factory floor and regardless of machines' location in the network topology. TANDEM is a topology-independent wireless multi-hop network protocol that implements in-network prioritization to provide a useful, early approximation of process information. Tailored to often required time series data transmission, TANDEM applies a time-frequency transformation to prioritize more valuable information parts, decouples multi-hop communication steps to improve robustness, and uses a periodic acknowledgment overhearing mechanism to save transmissions. We analytically discuss reliability and fairness aspects and show using simulations that TANDEM's prioritization is beneficial to obtain an early preview of sensor information even in large factories. In addition, we implement TANDEM on industrial grade hardware and provide a performance evaluation.

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.

Random linear network coding simplifies routing decisions, improves throughput, and increases tolerance against packet loss. A substantial limitation, however, is delay: decoding requires as many independent linear combinations as data blocks. Hierarchical network coding purportedly solves this delay problem. It introduces layers to decode prioritized data blocks early, which may benefit video streaming applications or applications for sensor information collection. While hierarchical network coding reduces decoding delays, it introduces significant space complexity and additional decoding time. We propose a decoding algorithm that manages all prioritization layers in a joint decoder matrix. Analytical evaluation and performance measurements show that we maintain prioritization benefits without increased space complexity and improve decoding performance. With memory requirements independent of the number of layers, our algorithm facilitates more fine-grained prioritization layers to further the benefits of hierarchical network coding.