Multi-sensor management for information fusion: issues and approaches

Xiong, Naixue; Svensson, Per

doi:10.1016/s1566-2535(02)00055-6

Cited by 251 publications

(125 citation statements)

References 13 publications

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“…Deterministic resource planning for WSN requires explicit assignment of the responsibility to each sensor, such as sensor selection, multi-target tracking and sensor allocation for classification, negotiation and cooperation between sensors in WSN. Similar requirements can be demanded for sensor scheduling and sensor control (Xiong and Svensson, 2002). Although the redundant deployment of sensors allows for sampling of a signal at finer resolution and provides high fault tolerance and noise immunity, there is an inherent trade-off between QoS and resource constraints which must be addressed from an uncertainty-aware WSN framework.…”

Section: Discussionmentioning

confidence: 98%

Uncertainty-aware Wireless Sensor Networks

Mal-Sarkar

Sikder

et al. 2009

IJMC

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Abstract:The characterisation of uncertainty and the management of Quality of Service are important issues in mobile communications. In a Wireless Sensor Network, there is a high probability of redundancy, correlation and noise in the sensor features since data is often collected from a large array of densely deployed neighbouring sensors. This article proposes a soft computing approach to manage uncertainty by reasoning over inconsistent, incomplete, and fragmentary information using classical rough set and dominance-based rough set theories. A methodological and computational basis is provided and is illustrated in a real world sensor network application of aquatic biodiversity mapping under uncertainty.

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Section: Discussionmentioning

confidence: 98%

Uncertainty-aware Wireless Sensor Networks

Mal-Sarkar

Sikder

et al. 2009

IJMC

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“…However it is important to note that the distributed architecture exhibits many attractive properties such as being scalable in structure without being constrained by centralized computational bottlenecks, or modular in the implementation of fusion nodes [60,61].…”

Section: Discussionmentioning

confidence: 99%

Fusing actigraphy signals for outpatient monitoring

Fuster–Garcia¹,

Bresó

Martı́nez-Miranda

et al. 2015

Information Fusion

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ElsevierFuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015) AbstractActigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a * good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.

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“…Often multiple sensors (sensor network) are used to give a more complete overview about the environment than a single sensor can give [40,15]. This increases the diagnosis ability (failure detection and localization [5]) of a system and makes sensor optimization critical for failure diagnosis.…”

Section: Sensor Optimizationmentioning

confidence: 99%

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Gerdes

Galar

Scholz

2016

EiN

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IntroductionThis paper presents a condition monitoring system with sensor optimization capabilities to prevent unscheduled delays in the aircraft industry. Unscheduled delays cost airlines a great deal of money but can be prevented by condition monitoring [11]. The aim was to develop a simple condition monitoring system that can be understood by humans and modified by experts to incorporate knowledge that is not in the learning data set, using decision trees as the main tool. Decision Gerdes M, GAlAr d, scholz d. decision trees and the effects of feature extraction parameters for robust sensor network design. eksploatacja i Niezawodnosc - Maintenance and reliability 2017; 19 (1): 31-42, http://dx.doi.org/10.17531/ein.2017.1.5. trees satisfy the requirements and provide a ranking of data sources for condition monitoring.The first section of the paper gives the motivation for developing a condition monitoring system with sensor optimization capabilities and explains the basic concepts of the proposed method. The second section explains the method in detail. Section three discusses the experiments validating it. The results of the validation experiments are given in section four. The paper concludes with a discussion of the results. New and better monitoring approaches are required for condition monitoring, because systems are becoming more complex and more difficult to monitor [32]. Condition monitoring requires reliable sensors. To obtain enough sensing data, special attention should be given to optimizing sensor allocation to ensure system diagnosability, lower sensing cost and reduce time to diagnosis [37]. Sensors can be used to determine the system health of control systems; a failed sensor can lead to a loss of process control [18]. The information about a system is incomplete, if a sensor fails. Complex systems are often monitored by multiple sensors. An advantage of a multi sensor system is that a single failed sensor shows its effects in multiple sensors [18].This means the system condition is defined by all information from the sensors. However, the system's health status becomes uncertain when a sensor fails or sends wrong data. This could trigger incorrect maintenance, including maintenance on a part with no failure, as well as long maintenance times to find the correct fault or not noticing the fault at all.The Safety Integrity Level (SIL) defines the probability that the system safety function for a Safety Instrumented System (SIS) can be executed. There are four SILs; level four is the level with the highest probability that an SIS can be performed. Sensor failure detection (sensor validation) is a critical part of the safety function of a system. When a failure is detected SIS is put into a safe state to avoid risk and damage to humans and machines [14,13].Redundancy is used to reduce the risk of model uncertainty [5]. One way to create sensor redundancy is hardware redundancy; another is analytical redundancy [5]. Analytical redundancy assumes multiple sensors deliver the same information, and,...

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Multi-sensor management for information fusion: issues and approaches

Cited by 251 publications

References 13 publications

Uncertainty-aware Wireless Sensor Networks

Uncertainty-aware Wireless Sensor Networks

Fusing actigraphy signals for outpatient monitoring

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

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