{'fraction_non_alphanumeric': 0.0231023102310231, 'fraction_numerical': 0.0027502750275027505, 'mean_word_length': 5.543165467625899, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
QoS-Aware Cross-Layer Configuration for Industrial Wireless Sensor Networks

In many applications of industrial sensor networks, stringent reliability and maximum delay constraints paired with priority demands on a sensor-basis are present. These quality of service (QoS) requirements pose tough challenges for industrial wireless sensor networks that are deployed to an ever larger extent due to their flexibility and extendibility. In this paper, we introduce an integrated cross-layer framework, SchedEx-GA, spanning medium access control (MAC) layer and network layer. SchedEx-GA attempts to identify a network configuration that fulfills all application-specific process requirements over a topology including the sensor publish rates, maximum acceptable delay, service differentiation, and event transport reliabilities. The network configuration comprises the decision for routing, as well as scheduling. For many of the evaluated topologies it is not possible to find a valid configuration due to the physical conditions of the environment. We therefore introduce a converging algorithm on top of the framework which configures a given topology by additional sink positioning in order to build a backbone with the gateway that guarantees the application specific constraints. The results show that, in order to guarantee a high end-to-end reliability of 99.999% for all flows in a network containing emergency, control loop, and monitoring traffic, a backbone with multiple sinks is often required for the tested topologies. Additional features, such as multichannel utilization and aggregation, though, can substantially reduce the demand for required sinks. In its present version, the framework is used for centralized control, but with the potential to be extended for decentralized control in future work.