Multi-sensor fusion: an Evolutionary algorithm approach

Maslov, Igor V.; Gertner, I.

doi:10.1016/j.inffus.2005.01.001

Cited by 38 publications

(20 citation statements)

References 53 publications

(83 reference statements)

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“…2) Evolutionary Algorithms: Article [111] gives an overview of basic and advanced concepts, models, and variants of GA in various applications in information fusion. …”

Section: B Data Aggregation and Sensor Fusionmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

611

270

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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areasand publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

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“…2) Evolutionary Algorithms: Article [111] gives an overview of basic and advanced concepts, models, and variants of GA in various applications in information fusion. …”

Section: B Data Aggregation and Sensor Fusionmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

611

270

View full text Add to dashboard Cite

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“…1 We must mention that the word "fusion," which appears often in the aforementioned references, usually refers to the "combination" of classifiers for improving classifier performance using a single training data set and not necessarily to "data fusion" (combining information coming from different data sources). Commonly used methods for "data fusion" are generally based on Bayesian theory [32], [33], state estimation with Kalman or particle filtering [34]- [39], evidence theory (DS) [40], [41] and its variations [10], [42]- [45], information theoretic framework [46], neural networks [47], and evolutionary algorithms [48], [49]. The traditional application area for data fusion has long been target detection and tracking [38], [39], [50]- [54].…”

Section: B Ensemble Approaches and Data Fusionmentioning

confidence: 99%

An Ensemble-Based Incremental Learning Approach to Data Fusion

Parikh

Polikar

2007

IEEE Trans. Syst., Man, Cybern. B

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Abstract-This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data.Index Terms-Data fusion, incremental learning, Learn++, multiple classifier/ensemble systems.

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“…EAs have been widely applied in the last few years in computationally expensive applications and proved to be a strong optimization method in many types of combinatorial problems. Those successful applications of EAs involve scheduling [6], knowledge discovery [7], information fusion [8], etc.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Genetic-Algorithm-Based Fusion System Optimization for 3D Image Interpretation

Valet

Lima

Evsukoff

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Information fusion systems are complex systems with many parameters that must be adjusted to obtain interesting results. Generally applied in specialized domains such as military, medical and industrial areas, these systems must work in collaboration with the experts of the domains. As these end-users are not specialists in information fusion, the parameters adjustment becomes a difficult task. In addition, to find a good set of those parameters is a hard and time consuming process as the search space is very large. In order to overcome this issue a genetic algorithm is applied to automatically search the best parameter set. The results show that the proposed approach produces accurate levels of the global performance of the fusion system.

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Multi-sensor fusion: an Evolutionary algorithm approach

Cited by 38 publications

References 53 publications

Computational Intelligence in Wireless Sensor Networks: A Survey

Computational Intelligence in Wireless Sensor Networks: A Survey

An Ensemble-Based Incremental Learning Approach to Data Fusion

A Genetic-Algorithm-Based Fusion System Optimization for 3D Image Interpretation

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