A Fuzzy Data Fusion Solution to Enhance the QoS and the Energy Consumption in Wireless Sensor Networks

Collotta, Mario; Pau, Giovanni; Bobovich, Alexander V.

doi:10.1155/2017/3418284

Cited by 25 publications

(8 citation statements)

References 32 publications

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“…On the one hand, the increase in the number of sensor nodes increases the number of faults, and, when a sensor node fails, it will contaminate the final fusion result: in terms of its requirements, the state space model of the system is strict, and the accuracy of the model directly affects the data fusion effect [22,23]. Collotta et al [24] adopted a data fusion scheme based on fuzzy aggregation theory. Although fusion precision is improved, the method can only aggregate accurate data and cannot process complete data.…”

Section: Related Workmentioning

confidence: 99%

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Sun¹,

Zhang²,

Zheng³

et al. 2019

Sensors

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Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

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Section: Related Workmentioning

confidence: 99%

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Sun¹,

Zhang²,

Zheng³

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The DSS involves a rule-based decision approach based on referenced mathematical models applied to the information gathered by a sensor network. 22 A fuzzy-based model (FBM) for data fusion has been proposed to solve the quality-of-service (QoS) issue in WSNs. In this model, a fuzzy logic controller is employed within each sensor node.…”

Section: Related Workmentioning

confidence: 99%

“…A Decision Support System (DSS) has been proposed, which is based on multisensor data fusion for greenhouse application. The DSS involves a rule‐based decision approach based on referenced mathematical models applied to the information gathered by a sensor network . A fuzzy‐based model (FBM) for data fusion has been proposed to solve the quality‐of‐service (QoS) issue in WSNs.…”

Section: Related Workmentioning

confidence: 99%

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Kumar

Chaurasiya

2018

Concurrency and Computation

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To give the complete description of an environment or to take a robust decision, a number of observations are collected and combined from multiple sensor nodes. The process of combining and analyzing the observations is called multisensor data fusion. The fusion is used to produce more consistent, accurate, and useful information rather than provided by any individual sensor node. Data fusion finds wide application in many areas of such as object recognition, wireless sensor network, image processing, environment mapping, and localization. Nowadays, the Internet of Things (IoT) utilizes wireless sensor network (WSN) as a necessary platform for data sensing and communication. For efficiency, data fusion is performed on the sensed sample collected by the sensor nodes. However, fusion of the network parameters is also essential to select an appropriate sensor node for the forwarding of data. Therefore, in this paper, we propose a multisensor data fusion (MDF) strategy that performs fusion of the collected network parameters like bandwidth and centrality for the selection of an appropriate path. Extensive simulations were made, and related results showed that our proposed scheme has a better performance compared with other schemes in our simulated scenarios. KEYWORDS data fusion, Internet-of-Things, performance analysis, wireless sensor network INTRODUCTIONA wireless sensor network (WSN) is a type of network that contains large number of nodes having sensing capability such that they can easily detect any changes in the surrounding real-world environment. The nodes in WSN are used to carry sensed information from one to another desired location for further processing. 1,2 Technological advances are being developed daily, and a WSN plays a rapid contribution for providing smart communication with the use of smart devices. These smart devices perform communication at the different locations by providing a level of transparency among users and maintained an interconnected smart network. A number of sensor nodes are constituted by the smart network, which is used to send sensed information and termed as the Internet of Things (IoT). 3 In this paper, we called this network as an IoT oriented WSN. In this network, the users communicate with each other by exchanging of sensed data, monitoring of events, and surrounding and reacting autonomously. 4 The difference between the WSN and IoT oriented WSN is only the few sensors are deployed in WSN rather than along with smart devices for applications.However, the IoT oriented WSN consists of devices having sensors with full network connectivity. The network of IoT oriented WSN is concerned with the loss of packets and performance of the links for achieving high throughput in less delay.The services of IoT oriented WSN are provided by using a standard interface such that users can create a query, retrieve information, and change their states accordingly. An Internet link is used to provide the standard interface between users and IoT devices. The hardware components of a sensor no...

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“…The scheme proposed in [19] is proved to have better energy conserving performance than T1FL, LEACH-SH and LEACH-MH. In [20], Authors propose a fuzzy logic based data fusion technique to improve the QoS and the energy conservation in wireless sensor networks. The authors maintain that the fuzzy-based solution introduced in [20] is highly suited for applications that do not require strict real-time constraints and most useful in soft real-time contexts.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed method achieves low energy consumption and low computation complexity and provides high classification accuracy contrary to that of traditional fuzzy fusion and fuzzy aggregation algorithm. However, The proposed schemes in [19][20][21] are solely related to data aggregation and fusion in wireless sensor network using sophisticated fuzzy logic based techniques, where as we propose a fuzzy logic based source authentication technique which helps to very data reports at the intermediate nodes. Our proposed scheme enables the adaptive selection of a number of such verification nodes aimed at saving computational energy at low false positive report rate and increased security at higher false positive report insertion rate.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficiency Enhancement of TICK –based Fuzzy Logic for Selecting Forwarding Nodes in WSNs

2018

KSII TIIS

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Communication cost is the most important factor in Wireless Sensor Networks (WSNs), as exchanging control keying messages consumes a large amount of energy from the constituent sensor nodes. Time-based Dynamic Keying and En-Route Filtering (TICK) can reduce the communication costs by utilizing local time values of the en-route nodes to generate one-time dynamic keys that are used to encrypt reports in a manner that further avoids the regular keying or re-keying of messages. Although TICK is more energy efficient, it employs no re-encryption operation strategy that cannot determine whether a healthy report might be considered as malicious if the clock drift between the source node and the forwarding node is too large. Secure SOurce-BAsed Loose Synchronization (SOBAS) employs a selective encryption en-route in which fixed nodes are selected to re-encrypt the data. Therefore, the selection of encryption nodes is non-adaptive, and the dynamic network conditions (i.e., The residual energy of en-route nodes, hop count, and false positive rate) are also not focused in SOBAS. We propose an energy efficient selection of re-encryption nodes based on fuzzy logic. Simulation results indicate that the proposed method achieves better energy conservation at the en-route nodes along the path when compared to TICK and SOBAS.

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A Fuzzy Data Fusion Solution to Enhance the QoS and the Energy Consumption in Wireless Sensor Networks

Cited by 25 publications

References 32 publications

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Energy Efficiency Enhancement of TICK –based Fuzzy Logic for Selecting Forwarding Nodes in WSNs

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