A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

Cao, Li; Cai, Yong; Yue, Yinggao; Cai, Shaotang; Hang, Bo

doi:10.1109/access.2020.2967118

Cited by 28 publications

(17 citation statements)

References 32 publications

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“…ML algorithms should learn the correlation between traffic inputs and link conditions to predict or determine a path for the incoming traffic. Recent studies that attempt to improve routing decisions in a network are mostly NN based, such as in [22], [25], [101]- [103], followed by works that adopt RL, such as in [35], [102], [104]. Those recent studies that exploit other ML algorithms are further elaborated in this section.…”

Section: ML For Improving Routing Decisions In Communication Netmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Section: ML For Improving Routing Decisions In Communication Netmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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“…Figure 3 shows the comparison results of Network Lifetime of BAT-ELM [15] and proposed protocol SDAFPS and numerical results are depicted in Table 3. The BAT-ELM protocol network structure is not adoptable to reconnect the (10) x j = Data Received fitness value( j ) network when new node enters into the network, whereas SDAFPS protocol is adaptable to connect new node to the efficient parent node based on fuzzy judgment matrix algorithm. In the graph, linear changes of both the protocol remain same till 8000 s due to data reduction process.…”

Section: Performance Metricsmentioning

confidence: 99%

“…An efficient data fusion process is presented in [10] to minimize the data size and forward to the sink node. The author addresses long delay issue while using extreme machine learning technique for data aggregation and later proposed BAT-ELM algorithm with an extreme machine learning to reduce long delay for training the sensor nodes in a network for data fusion.…”

Section: Introductionmentioning

confidence: 99%

SDAFPS: Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution

Reshma

Shaila²,

Venugopal

2021

SN COMPUT. SCI.

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Secure data aggregation intends to reduce redundant data transmission and malicious node interference in the network. Therefore, designing secure data aggregation protocol is a crucial task in WSNs. In this paper, we have proposed a Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution (SDAFPS) protocol. The SDAFPS protocol involves three main phases. In the first phase, the protocol controls the topology with the selection of efficient aggregator node in every interval. The second phase uses category pattern code generation and utilization concept to reduce data size and to aggregate data. Finally, in third phase, the aggregated data are encrypted using partial equation of SHAP contribution and decrypted with SHAP contribution equation. The decrypted data are verified with dataset preserved at the sink node. The SDAFPS protocol is implemented using NS2 Simulator tool and performance of proposed protocol is compared with existing protocol and validated 18% improvement in network lifetime, 10% minimized End-to-End Delay and 14% improvement on Packet Delivery Ratio over protocol.

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“…The purpose of the hidden layer is to process and identify the variables. Subsequently, the information is passed on the output layer [31].…”

Section: Extreme Learningmentioning

confidence: 99%

Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

2020

Self Cite

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Data fusion can reduce the data communication time between sensor nodes, reduce energy consumption, and prolong the lifetime of the network, making it an important research focus in the field of heterogeneous wireless sensor networks (HWSNs). Normal sensor nodes are susceptible to external environmental interferences, which affect the measurement results. In addition, raw data contain redundant information. The transmission of redundant information consumes excess energy, thereby reducing the lifetime of the network. We propose a data fusion method based on an extreme learning machine optimized by particle swarm optimization for HWSNs. The spatiotemporal correlation between the data of the HWSNs is determined, and the extreme learning machine method is used to process the data collected by the sensor nodes in the hierarchical routing structure of the HWSN. The particle swarm optimization algorithm is used to optimize the input weight matrix and the hidden layer bias of the extreme learning machine. An output weight matrix is created to reduce the number of hidden layer nodes and improve the generalization ability of the model. The data fusion model fuses the original data collected by the sensor nodes. The simulation results show that the proposed algorithm reduces network energy consumption and improves the lifetime of the network, the efficiency of data fusion, and the reliability of data transmission compared with other data fusion methods.

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A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

Cited by 28 publications

References 32 publications

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

SDAFPS: Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution

Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

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