Abstract
In this paper, we propose a decentralized semantic reasoning approach for modeling vague spatial objects from sensor network data describing vague shape phenomena, such as forest fire, air pollution, traffic noise, etc. This is a challenging problem as it necessitates appropriate aggregation of sensor data and their update with respect to the evolution of the state of the phenomena to be represented. Sensor data are generally poorly provided in terms of semantic information. Hence, the proposed approach starts with building a knowledge base integrating sensor and domain ontologies and then uses fuzzy rules to extract three-valued spatial qualitative information expressing the relative position of each sensor with respect to the monitored phenomenon’s extent. The observed phenomena are modeled using a fuzzy-crisp type spatial object made of a kernel and a conjecture part, which is a more realistic spatial representation for such vague shape environmental phenomena. The second step of our approach uses decentralized computing techniques to infer boundary detection and vertices for the kernel and conjecture parts of spatial objects using fuzzy IF-THEN rules. Finally, we present a case study for urban noise pollution monitoring by a sensor network, which is implemented in Netlogo to illustrate the validity of the proposed approach.
Highlights
Based on Molenaar [10] works, we identify three conceptual uncertainty levels for spatial objects extracted from sensor data as follows: The existential uncertainty expressing how sure we are that a given phenomenon really exists at a particular position in space and time from recorded sensor data, The extensional uncertainty expressing how the area covered by the monitored phenomenon can only be determined, The geometric uncertainty refers to the precision with which the boundary of the object representing the monitored phenomenon can be detected
From the bottom level where sensor data are collected and translated into spatial information, to the top level where the fuzzy spatial object representing a monitored phenomenon at a given date is built, there are some challenges in preparing the reasoning engine of sensing agents. These include: (1) defining the appropriate membership function used by sensors to change collected data into fuzzy sets, (2) ensuring meaningful integration and communication among sensors by deriving semantic rules from ontologies to translate sensor data into geospatial information, and (3) handling geometric uncertainties in inferring vertices from sensor collaboration to define the geometry of the spatial object representing a sensed phenomenon
We have proposed a new approach to extract vague spatial objects from sensor data
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We need collaboration among nodes to infer and aggregate knowledge describing the complete spatial extent of the phenomena This process that implicates real-time assessment of a large amount of widely varying sensor data with inherent uncertainties for modeling field-based dynamic phenomenon is still a challenging problem [14]. If such a field-based phenomenon with vague boundaries is represented using a crisp spatial object, subsequent spatial computation (e.g., topological analysis) may result in poor decision-making process.
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