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1. Introduction

Hence, the user is guided during the orchestration process. Their matching procedure could also be applied to the task of sensor orchestration, but it contains drawbacks compared to the orchestration concept elaborated here. Similar to [ 30 ], the system relies on a complex ontology.

EEG Feature Extraction

An orchestration system does not have to include complex knowledge representations. The required functionalities can even be fulfilled by more effective and lightweight concepts. As mentioned before, ontology engineering is a time-consuming process that requires high-skilled operators. Therefore, a rule-based system is easier to handle and maintain. Furthermore, as the formulation of rules is simple for human operators, the system is extensible offhand.

Another concept is proposed in [ 34 ]. Its field of work is sensor networks. The aim is to assign specific roles to sensors in order to improve aspects such as energy consumption, life-time, or coverage.

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Instead of an accurate description of sensors, the assignment relies on user-defined roles that are available at sensor nodes. The latter is comparable to intelligent sensors, i. A role assignment algorithm is available at every sensor node and used to infer the particular role of this node. The algorithm relies on formulations of predicate logic and evaluates rules for role assignment. Although this is similar to the orchestration procedure of this contribution, their concept is different.

The authors specify roles externally and propagate these to available sensor nodes that implement the logic for role assignment.

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Here, intelligent sensors are incorporated for orchestration and available knowledge is collected in a shared knowledge base. With respect to information fusion systems, this is a superior concept since knowledge about the overall system is available from a central repository.


These are easier to handle and overview in a central knowledge base instead of a distributed network. Hence, a central knowledge base simplifies fusion system generation and maintenance. Besides the functionality of the orchestration concept and its advantages compared to related approaches, its complexity is of interest. The orchestration engine relies on a rule-based system that infers knowledge from given rules and facts using the forward chaining algorithm cf. Rules defined for orchestration are independent of each other, i.

Radar target identification using HRRP-based features and Extreme Learning Machines

Thus, the forward chaining algorithm is required to iterate over the rule base only once. The computational steps necessary for the orchestration grow linearly in N. The number of rules depends on the number of available elementary sensors and algorithms. Rules are added to the rule base R for the assignment of features and attributes. For feature assignment, the rule base contains rules for all possible combinations of algorithms A and elementary sensors S.

Thus, the amount of rules added to R for the assignment of features is. Furthermore, R contains rules for the assignment of attributes. Hence, the amount of rules added for the assignment of attributes is. Computational complexities are evaluated in a worst case scenario. The complexity states that an algorithm does not perform worse than a comparison function. Here, a scenario is considered as worst case if a maximum of rules is generated in an orchestration.

Features are created by applying an extraction algorithm to a sensor signal. As detailed in Section 3. In the worst case, all sensors match to all available algorithms. Thus, the maximum amount of features is equal to the amount of elementary sensors times the amount of available algorithms:. The maximum amount of attributes relies on the maximum number of features.

Signal and information processing for sensing systems

In the following, only module and physical attributes are considered. Since functional attributes are designed manually, their number cannot be estimated. Quality attributes are treated by the orchestration as a special case of a module attribute. A sensor monitors either a module or the fabricated product. With regard to Table 5 , a sensor contributing to a quality attribute has the associated object product. Each feature monitors only one single module or the product.

It also captures only one single physical phenomenon. Consequently, it can only be part of two attributes, either a module or quality attribute and one single physical attribute. Considering that at least two features are required to form an attribute as specified in Definition 3, an orchestration results in the maximum amount of attributes if all features pair up once for module and once for physical attributes.

A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition

The maximum amount of attributes, again without considering functional attributes, is then formalised as follows:. The number of rules is independent of the amount of implemented intelligent sensors. Intelligent sensors, i. A system manager detects available intelligent sensors. Semantic data is exchanged by a middleware. An orchestration procedure that relies on a knowledge-based system in the form of a rule-based system is introduced. The system designer remains the final decision maker in this process.

Furthermore, the underlying real-time communication system for process data exchange is configured automatically with respect to the actual design of the fusion system. The current implementation of the proposed automated fusion system design relies on several centralised entities and repositories. First of all, the system manager represents a central unit. It is divided into an orchestration engine and a knowledge base. The knowledge base consists of a central rule base and various repositories sensor, feature, attribute, and algorithm repositories.

Furthermore, the communication of process data is centrally organised as well. It relies on a central Profinet controller, which manages the communication process. Merging the proposed automated fusion system design with concepts of MASs and self-organising distributed systems is an open research topic for future work. In such an approach, the orchestration and the information fusion system themselves become decentralised. The aim is to increase the fault tolerance, scalability, and accessibility of the automated design system.

A self-organising distributed fusion system without a central control unit requires autonomously acting sensor nodes. Intelligent sensors, as introduced in Definition 5, provide local processing and communication capabilities, are self-aware and provide self-configuration. Because of these capabilities, an intelligent sensor is well-suited to function as an embodied agent cf. Section 2.