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Innovative possibilities for data collection, networking, and evaluation are unleashing previously untapped potential for industrial production. However, harnessing this potential also requires a change in the way we work. In addition to expanded automation, human-machine cooperation is becoming more important: The machine achieves a reduction in complexity for humans through artificial intelligence. In fractions of a second large amounts of data of high decision quality are analyzed and suggestions are offered. The human being, for this part, usually makes the ultimate decision. He validates the machine’s suggestions and, if necessary, (physically) executes them.
Both entities are highly dependent on each other to accomplish the task in the best possible way. Therefore, it seems particularly important to understand to what extent such cooperation can be effective. Current developments in the field of artificial intelligence show that research in this area is particularly focused on neural network approaches. These are considered to be highly powerful but have the disadvantage of lacking transparency. Their inherent computational processes and the respective result reasoning remain opaque to humans. Some researchers assume that human users might therefore reject the system’s suggestions. The research domain of explainable artificial intelligence (XAI) addresses this problem and tries to develop methods to realize systems that are highly efficient and explainable.
This work is intended to provide further insights relevant to the defined goal of XAI. For this purpose, artifacts are developed that represent research achievements regarding the systematization, perception, and adoption of artificially intelligent decision support systems from a user perspective. The focus is on socio-technical insights with the aim to better understand which factors are important for effective human-machine cooperation. The elaborations predominantly represent extended grounded research. Thus, the artifacts imply an extension of knowledge in order to develop and/ or test effective XAI methods and techniques based on this knowledge. Industry 4.0, with a focus on maintenance, is used as the context for this development.
The application of Wireless Sensor Networks (WSNs) with a large number of tiny, cost-efficient, battery-powered sensor nodes that are able to communicate directly with each other poses many challenges.
Due to the large number of communicating objects and despite a used CSMA/CA MAC protocol, there may be many signal collisions.
In addition, WSNs frequently operate under harsh conditions and nodes are often prone to failure, for example, due to a depleted battery or unreliable components.
Thus, nodes or even large parts of the network can fail.
These aspects lead to reliable data dissemination and data storage being a key issue.
Therefore, these issues are addressed herein while keeping latency low, throughput high, and energy consumption reduced.
Furthermore, simplicity as well as robustness to changes in conditions are essential here.
In order to achieve these aims, a certain amount of redundancy has to be included.
This can be realized, for example, by using network coding.
Existing approaches, however, often only perform well under certain conditions or for a specific scenario, have to perform a time-consuming initialization, require complex calculations, or do not provide the possibility of early decoding.
Therefore, we developed a network coding procedure called Broadcast Growth Codes (BCGC) for reliable data dissemination, which performs well under a broad range of diverse conditions.
These can be a high probability of signal collisions, any degree of nodes' mobility, a large number of nodes, or occurring node failures, for example.
BCGC do not require complex initialization and only use simple XOR operations for encoding and decoding.
Furthermore, decoding can be started as soon as a first packet/codeword has been received.
Evaluations by using an in-house implemented network simulator as well as a real-world testbed showed that BCGC enhance reliability and enable to retrieve data dependably despite an unreliable network.
In terms of latency, throughput, and energy consumption, depending on the conditions and the procedure being compared, BCGC can achieve the same performance or even outperform existing procedures significantly while being robust to changes in conditions and allowing low complexity of the nodes as well as early decoding.