A Data Fusion Method in Wireless Sensor Networks

Izadi, Davood; Abawajy, Jemal; Ghanavati, Sara; Herawan, Tutut

doi:10.3390/s150202964

Cited by 103 publications

(59 citation statements)

References 32 publications

(31 reference statements)

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“…In addition, important research can be found in adopting FL in aggregation mechanisms. In [29], a fuzzy-based data fusion approach for WSNs is proposed. The model aims to increase the QoS while reducing the energy consumption of nodes.…”

Section: E Information Aggregation Under Uncertaintymentioning

confidence: 99%

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

Kolomvatsos

Anagnostopoulos

Hadjiefthymiades

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying phenomena e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism, which based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a Type-2 fuzzy inference system to derive highly accurate identification of events. The Type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with Type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability.

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Section: E Information Aggregation Under Uncertaintymentioning

confidence: 99%

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

Kolomvatsos

Anagnostopoulos

Hadjiefthymiades

2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…Demin et al [58] introduce an algorithm for maximum data collection rate in rechargeable wireless sensor networks with multiple sinks. In [59], the authors present a fuzzy-based data fusion approach for increasing the QoS whilst improving the data collection rate and reducing the energy consumption of the sensor network.…”

Section: Related Workmentioning

confidence: 99%

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

Lin

Dan

Gao

et al. 2016

Sensors

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In Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network’s performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks.

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“…Moreover, this technique needs to be applicable in different application environments and more importantly minimizes the aforementioned delay factor in WSNs. In the literature, data fusion and aggregation mechanisms are centered on how to efficaciously use the on‐board batteries of sensor nodes to enhance the WSNs' lifetime . These approaches utilize different mechanisms proposed in the literature such as fuzzy set theory, sampling techniques, theory of probability, a hybrid of fuzzy set and probability, and evidence theory proposed by Dempster‐Shafer .…”

Section: Introductionmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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A Data Fusion Method in Wireless Sensor Networks

Cited by 103 publications

References 32 publications

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

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