REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs

Al-Zubaidie, Mishall; Zhang, Zhongwei; Zhang, Ji

doi:10.3390/app10062007

Cited by 25 publications

(19 citation statements)

References 77 publications

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“…In this section, the NS2.34 network simulator is used to evaluate the proposed HDMCL methodology 41,42 in H‐WSN. Different experimental evaluations are to be carried out in this section.…”

Section: Resultsmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

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Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

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Section: Resultsmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

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“…Al-Zubaidie et al [17] created a framework called Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH). Healthcare wireless sensor network (HWSN) provides a high throughput and optimized results for an integration of data.…”

Section: Related Workmentioning

confidence: 99%

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

2022

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Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.INDEX TERMS Industrial Internet of Things, clustering, bald eagle search algorithm, trust aware protocols, security.

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“…Saleh N et al proposed the energy-effective dynamic clustering algorithm (EE-DCA) for the wireless sensor networks that enhances the bandwidth utilization via altering the action of the wireless scheme of the sensor node, remaining energy computation as well as the transition of state of the sensor nodes [9][10][11][12]. The dynamic clustering technique was utilized in enhancing the network lifetime as the clustering head's residual energy was lower than the threshold.…”

Section: Literature Reviewmentioning

confidence: 99%

A Firebug Optimal Cluster based Data Aggregation for Healthcare Application

Ranjani

Ananth²,

Reddy

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Majority of Wireless Sensor Networks (WSNs) are made up of huge sensor nodes with minimum cost, processing, communication capability and limited sensing. Because sensor nodes have limited resources, it’s critical to enhance bandwidth utilization, network lifetime and minimize the data transmission. The term data aggregation defines the method of integrating the sensor data to minimize the data transmission. However, WSN-based healthcare applications face privacy, and security concerns. In this paper design of a Fire Bug Swarm Optimal Cluster-Based Data Aggregation (FSOCDA) in WSN has been presented which is a useful method for WSN to save energy and extend network lifetime. The efficient clustering is carried out using FSO, which is utilized to choose the CH from among various nodes in the cluster that serve as data aggregation nodes. By altering the network scale, the computational competence of the FSOCDA technique is compared to that of the non-aggregate approach. Further, the data aggregation method is helpful in healthcare applications in terms of data transferring from host hospital to other.

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REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs

Cited by 25 publications

References 77 publications

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

A Firebug Optimal Cluster based Data Aggregation for Healthcare Application

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