BIM2RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks

Li, Hongbing; Chen, Qiang; Ran, Yong; Niu, Xiaowei; Chen, Liwan; Qin, Huafeng

doi:10.1016/j.swevo.2017.11.005

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…There is also some work (e g., Gaussian model [ 12 ]) to model the correlation between different sequences; however, its computational complexity is very large and computationally unaffordable when dealing with thousands of temporal data. Therefore, the author would like to find a method that can both model different events, with different sampling frequencies, and capture the correlation between different events and can be relatively easily extended for application to high-dimensional medical case data [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

Gao

et al. 2022

Journal of Healthcare Engineering

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This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its superiority over other typical approaches on real data sets. A controlled study is performed according to computerized randomization, with 38 patients in each of the two groups. The study group has a higher resuscitation success rate and patient satisfaction than the conventional group (P < 0.05), and the time from the first consultation to the completion of the first ECG, the time from the completion of the ECG to the activation of the path lab, and the time from the emergency admission to the balloon dilation were significantly shorter in the study group than in the conventional group (P < 0.05). The emergency care process reengineering intervention helps patients with acute myocardial infarction to be treated quickly and effectively, thus improving their resuscitation success rate and satisfaction rate, and is worthy to be caused in the clinic and widely applied.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

Gao

et al. 2022

Journal of Healthcare Engineering

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“…They also introduced a scalable and distributed passive clustering algorithm based on smart delay strategy by using the energy levels and the distances in order to sort out the entire network into different clusters. Hongbing et.al [14] presented an immune mechanism based multipath reliable transmission called BWAS with fault tolerance in wireless sensor networks. Preetha and Paul [15] suggested a Cross Layer-Low Energy Adaptive Clustering Hierarchy model (CL-LEACH), which is an effective RP to improve the battery lifetime.…”

Section: Related Workmentioning

confidence: 99%

Routing by HDF based Optimal Path Selection in Multipath WSNs

2019

IJRTE

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Multipath routing (MPR) is an effectual method for routing data on Wireless Sensor Networks (WSNs) since it offers security, reliability as well as load balancing (LB) that are particularly serious in the resource-constrained scheme like WSNs. This paper proposed a selecting optimal routing path in MPR using QoS for WSN. In the First phase, the network nodes are initialized. Next, the nodes are formed as a cluster which is known as cluster formation utilizing K-Medoid clustering algorithm. In the cluster formation, the cluster heads (CH) are chosen from each cluster using Grey wolf Optimization (GWO) algorithm. In the next stage, routing operation is performed, which is bifurcated into 2 sections as, multipath route selection, and optimal path selection (OPS). For multipath route selection, AOMDV protocol is used. Using this protocol, efficient multipath routes are chosen in the network. After several transmissions, a route might lose the quality of the link. So an optimal path is chosen from the existing routes in the network using Hybrid Dragon Fly (HDF) optimization. Performance metrics of the proposed work is compared with that of existing optimal path routings techniques. Results illustrate that our model exhibited better energy efficiency along with Network Lifetime when compared to the existing routing models

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“…It provides a balance between the consumption of energy and transmission delay in WSN. Hence, multipath routeing is an efficient technique, which should be considered for improving the transmission and tolerating faults in WSN [8]. Multipath routeing can eliminate the requirement of updating lines, load balancing, maximise data transmission rate and improve the reserved energy of SN.…”

Section: Introductionmentioning

confidence: 99%

Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation

Kulkarni

Jesudason

2019

IET Communications

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BIM2RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks

Cited by 17 publications

References 22 publications

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

Routing by HDF based Optimal Path Selection in Multipath WSNs

Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation

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