HRDSS-WMSN: A Multi-objective Function for Optimal Routing Protocol in Wireless Multimedia Sensor Networks using Hybrid Red Deer Salp Swarm algorithm

Ambareesh, S.; Madheswari, A. Neela

doi:10.1007/s11277-021-08201-z

Cited by 27 publications

(23 citation statements)

References 41 publications

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“…An efficient routing protocol must be able to allocate resources with a low minimum rate of packet transmission loss. In Ambareesh and Madheswari ( 2021 ), a Hybrid Red Deer Salp Swarm (HRDSS) was proposed for routing in WSN. HRDSS is a hybrid of RDA and the salp swarm optimization algorithm.…”

Section: Applications Of Rd Algorithmsmentioning

confidence: 99%

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Review and analysis for the Red Deer Algorithm

Zitar

Abualigah

Al-Dmour

2021

J Ambient Intell Human Comput

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Section: Applications Of Rd Algorithmsmentioning

confidence: 99%

“…HRDSS is a hybrid of RDA and the salp swarm optimization algorithm. The experiments conducted in Ambareesh and Madheswari ( 2021 ) showed that DRDSS exhibited the best optimal routing path in comparison to other Routing Protocols. For WSN, RDA was also used in Nguyen et al.…”

Section: Applications Of Rd Algorithmsmentioning

confidence: 99%

Review and analysis for the Red Deer Algorithm

Zitar

Abualigah

Al-Dmour

2021

J Ambient Intell Human Comput

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“…In the clustering process, a cluster is constructed with a specially designated CH and finite number of sensor member nodes. 30 In the cluster, the CH is responsible for establishing coordination with the sensor member nodes of its cluster and CH of the other clusters that controls their individual cluster sensor member nodes. This coordination of CH is mainly for achieving the objective that facilitates reliable aggregated data transmission to the BS or the sink node.…”

Section: Motivationmentioning

confidence: 99%

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Balamurugan

Janakiraman

Priya

2021

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) are capable of offering data dissemination among the nodes such that the exploration of a network's potential could be performed based on the frequency range. It is highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issues of network lifetime, coverage, scheduling and data aggregation. Prolonging network lifetime confirms energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation.Clustering schemes are highly suitable for effectively utilizing the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. This problem of energy-aware optimization included in the clustering process is an NP optimization problem. At this juncture, metaheuristic optimization algorithms are potential candidates for optimizing energy that attributes towards predominant sustenance in network lifetime. In this article, a modified African buffalo and group teaching optimization algorithm (MABGTOA) is proposed for achieving energy stability and maintaining network lifetime by efficient cluster head (CH) selection during the process of clustering. This MABGTOA scheme is developed for sustaining the tradeoff existing between the rate of exploitation and exploration that aids in efficient selection of CHs, thus maintaining lifetime and energy stability in the network. The simulation experiments of the proposed MABGTOA confirm its predominance by offering increased throughput by 18.21% and sustenance in residual energy by 15.48% when compared to the benchmarked schemes taken for comparative investigation.

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“…In some healthcare applications, disease prediction or diagnosis is unable to provide real-time deployment. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] This work evolves into the healthcare domain, and cloud computing is almost always the most cost-effective option for clients' healthcare businesses because it is often less expensive than having multiple computers in different medical rooms, each of which requires proper hardware, updated software, and network access to upload, store, and retrieve patient or other medical data. Healthcare businesses may be certain that they will not lose control of vital patient data now that cloud computing platforms, carriers, and service providers have enhanced security and safeguards in place.…”

Section: Introductionmentioning

confidence: 99%

“…In some healthcare applications, disease prediction or diagnosis is unable to provide real‐time deployment 5–20 . This work evolves into the healthcare domain, and cloud computing is almost always the most cost‐effective option for clients' healthcare businesses because it is often less expensive than having multiple computers in different medical rooms, each of which requires proper hardware, updated software, and network access to upload, store, and retrieve patient or other medical data.…”

Section: Introductionmentioning

confidence: 99%

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

Aswini¹,

Yamini

Jatothu³

et al. 2021

Concurrency and Computation

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Cloud computing is the on-demand access to computer resources such as applications, servers, data storage, development tools, networking capabilities, and other resources that are held in a remote data center maintained by a cloud services provider(CSP) and accessible over the internet. The limited resources and higher time consumption are the major issues faced by cloud users. The appropriate selection of virtual machines (VMs) is used to maximize cloud resource usage and reduce task execution time for the clients (patients, physicians, etc.). In this article, we have introduced an efficient cloud-based healthcare services paradigm (HCS). The opposition-based Laplacian equilibrium optimizer (O-LEO) algorithm is used to select optimal VMs in which the maximization of resource utilization and minimization of task execution time is performed. Additionally, the boosted support vector machine (SVM) effectively predicts chronic kidney disease (CKD) thereby ensuring better prediction results. The CloudSim platform is used as the implementation platform of the proposed method. The overall time taken by the O-LEO-based CloudSim is less than the standard Cloud Sim model to create three cloudlets which improve the system efficiency by 6%. When compared with the existing techniques, both the O-LEO and boosted SVM classifier outperforms superior performances.

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HRDSS-WMSN: A Multi-objective Function for Optimal Routing Protocol in Wireless Multimedia Sensor Networks using Hybrid Red Deer Salp Swarm algorithm

Cited by 27 publications

References 41 publications

Review and analysis for the Red Deer Algorithm

Review and analysis for the Red Deer Algorithm

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

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