Network management activities, such as fault analysis and configuration management, are eventually related to changes in network measurements. Some measurement event might be either a trigger or objective of a management activity. We argue that sharing the semantics of performance data across networks provides a basis for more advanced automation. This paper presents an ontology-based system for sharing information about network measurements across network domains. The represented information contains correlations and human-defined mappings between network measurements and the system is based on semantic reasoning that identifies dependencies which arise by combining local and shared information. We demonstrate the usage of the system in a Long Term Evolution (LTE) network domain. Our experiments from an LTE simulator and LTE test network show that a combination of correlations, humandefined mappings, and ontological reasoning produces useful cross-domain information that can be accessed with ontology queries.
Advanced automation is needed in future mobile networks to provide adequate service quality economically and with high reliability. In this paper, a system is presented that takes into account the network context, analyses uncertain information, and infers network configurations by means of probabilistic reasoning. The system introduced in this paper is an experimental platform integrating a mobile network simulator, a Markov Logic Network (MLN) model, and an OWL 2 ontology into a runtime environment that can be monitored via a Resource Description Framework (RDF)-based user interface. In this approach, the OWL ontology contains a semantic representation of the relevant concepts, and the MLN model evaluates elements of uncertain information. Experiments based on a prototype implementation demonstrate the value of semantic modelling and probabilistic reasoning in network status characterization, optimization, and visualization.
In this study, we introduced and tested a new approach to characterize residential magnetic field (MF) exposure. Short-term 20-min MF measurements were obtained by a person who carried out instantaneous spot measurements in residences. Compared to spot measurements, the 20-min measurement could potentially improve exposure assessment, because it contains information of temporal variations of MF, which have been suggested as biologically important characteristics of MF exposure. We have used this new exposure assessment method on a study of maternal MF exposure and reproductive outcomes. To validate the new method, the exposure of 30 subjects was measured with a more accurate ''gold standard'' method (24 h personal exposure measurements). The measures of validity used were the Spearman correlation coefficient (r), sensitivity, and specificity. We evaluated the validity of the 20-min measurements for estimating several different exposure metrics for the entire 24 h measurement period or for the hours spent at home: arithmetic mean, median, percentage of time above 0.15 mT, standard deviation, rate of change metric, standardized rate of change metric, constant field metric, and three metrics for the occurrence of high-peak exposures. The 20-min measurement was modestly associated with standard deviation and the rate of change metric, but gave very little information of other metrics of temporal variation. The 20-min measurement can also be used for assessing exposure metrics such as arithmetic mean and median, but it does not seem to offer any advantages compared to traditional 'spot' measurements. The 20-min measurement was not useful for assessing occurrence of high-peak exposures. We conclude that the 20-min measurement is useful for estimating some aspects of MF temporal variability.
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