A modified rider optimization algorithm for multihop routing in <scp>WSN</scp>

Augustine, Susan; Ananth, J. P.

doi:10.1002/jnm.2764

Cited by 14 publications

(12 citation statements)

References 53 publications

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“…For optimal server selection process, in this paper rider optimization algorithm [21] is utilized (ROA). The rider optimization algorithm has several applications such as document clustering, enhanced video super-resolution, webpage re-raking and resource allocation [22,23]. The proposed methodology can be implemented for NPTEL and also proposed methodology diagram is exposed in Fig.…”

Section: Rider Optimization Algorithm Based Cloud Server Selectionmentioning

confidence: 99%

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Pandian¹,

Columbus²

2023

Computer Systems Science and Engineering

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Currently, e-learning is one of the most prevalent educational methods because of its need in today's world. Virtual classrooms and web-based learning are becoming the new method of teaching remotely. The students experience a lack of access to resources commonly the educational material. In remote locations, educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations. The objective of this study is to demonstrate an optimization and queueing technique for allocating optimal servers and slots for users to access cloud-based elearning applications. The proposed method provides the optimization and queueing algorithm for multi-server and multi-city constraints and considers where to locate the best servers. For optimal server selection, the Rider Optimization Algorithm (ROA) is utilized. A performance analysis based on time, memory and delay was carried out for the proposed methodology in comparison with the existing techniques. The proposed Rider Optimization Algorithm is compared to Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithm (FFA), the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests. Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.

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Section: Rider Optimization Algorithm Based Cloud Server Selectionmentioning

confidence: 99%

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Pandian¹,

Columbus²

2023

Computer Systems Science and Engineering

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“…But, clustering process yields some problem in the work. In Augustine and Ananth, 24 a multipath routing algorithm is presented using optimization algorithm. In the work, cluster head selection is performed using kernel Fuzzy C Means (FCM) algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Energy‐aware redundancy‐aware clustering in wireless sensor networks using Spined Loach Searching Optimization

Sasikala

Sangaiah

2022

Int J Communication

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Wireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient-based technique needs to be implemented in data transmission. The most widely used methods are clustering-based data transmission and energy efficient routing detection.In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bioinspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced.

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“…The performance of FL-PSO obtains better results than PSO-C and PSO-WZ, where 1 and 2 are 0.7 and 0.3, respectively. The objective function is given in (18).…”

Section: Objective Functionmentioning

confidence: 99%

“…Moreover, several of the applications such as temperature and environment monitoring transfers the redundant data packets to the BS. Therefore, clustering is a good option to increase the network lifetime to prevent redundant data transmission [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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A modified rider optimization algorithm for multihop routing in WSN

Cited by 14 publications

References 53 publications

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Energy‐aware redundancy‐aware clustering in wireless sensor networks using Spined Loach Searching Optimization

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

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