Self-Organizing Architecture for Information Fusion in Distributed Sensor Networks

Bajo, Javier; Paz, Juan F. De; Villarrubia, Gabriel; Corchado, Juan M.

doi:10.1155/2015/231073

Cited by 22 publications

(21 citation statements)

References 47 publications

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“…Then, we introduced the combination of Bayes classifier and Multi-layer Perceptron (MLP) in order to increase the processing speed without losing the accuracy. Similar study was reported by Javier [3]. He proposed the method using fusion classifier.…”

Section: Proposed Methodssupporting

confidence: 76%

Preliminary Study of Classifier Fusion Based Indoor Positioning Method

Miyashita

Oura

Paz

et al. 2016

Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)

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Indoor positioning technology is commercially available now, however, the positioning accuracy is not sufficient in the current technologies. Currently available indoor positioning technologies differ in terms of accuracy, costs and effort, but have improved quickly in the last couple of years. It has been actively conducted research for estimating indoor location using RSSI (Received Signal Strength Indicator) level of Wi-Fi access points or BLE (Bluetooth Low Energy) tags. WiFi signal is commonly used for the indoor positioning technology. However, It requires an external power source, more setup costs and expensive. BLE is inexpensive, small, have a long battery life and do not require an external energy source. Therefore, by adding some BLE tags we might be able to enhance the accuracy inexpensive way. In this paper, we propose a new type of indoor positioning method based on WiFi-BLE fusion with Fingerprinting method. WiFi RSSI and BLE RSSI are separately processed each one by a Naive Bayes Classifier. Then, Multilayer Perceptron(MLP) is used as the fusion classifier. Preliminary experimental result shows 2.55m error in case of the MLP output. Since the result is not as good as the ones using conventional method, further test and investigation needs to be performed.

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Section: Proposed Methodssupporting

confidence: 76%

Preliminary Study of Classifier Fusion Based Indoor Positioning Method

Miyashita

Oura

Paz

et al. 2016

Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)

Self Cite

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“…This is like having a balance of $10 in a bank account and spending it twice by sending $10 to two different recipients. When it comes to measurements in WSNs (say energy consumption data) avoiding these inconsistencies is imperative [31]. If a miner attempted to pack these transactions along with ⪯ 2 into a block, he will reach an inconsistency.…”

Section: Why Order Matters: Sequential Diffusionmentioning

confidence: 99%

Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty

2017

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This work presents a theoretical and numerical analysis of the conditions under which distributed sequential consensus is possible when the state of a portion of nodes in a network is perturbed. Specifically, it examines the consensus level of partially connected blockchains under failure/attack events. To this end, we developed stochastic models for both verification probability once an error is detected and network breakdown when consensus is not possible. Through a mean field approximation for network degree we derive analytical solutions for the average network consensus in the large graph size thermodynamic limit. The resulting expressions allow us to derive connectivity thresholds above which networks can tolerate an attack.

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“…The capabilities of a MAS exceed those of each individual agent. Bajo et al propose a concept in which fusion of sensor data is organised using software agents [49]. The authors focus on an agent-based implementation of information fusion techniques.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Automated Fusion System Design and Adaptation

Fritze

Mönks

Holst

et al. 2017

Sensors

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Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

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Self-Organizing Architecture for Information Fusion in Distributed Sensor Networks

Cited by 22 publications

References 47 publications

Preliminary Study of Classifier Fusion Based Indoor Positioning Method

Preliminary Study of Classifier Fusion Based Indoor Positioning Method

Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty

An Approach to Automated Fusion System Design and Adaptation

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