Optimal cluster head selection using modified rider assisted clustering for IoT

Poluru, Ravi Kumar; Ramasamy, Lokesh Kumar

doi:10.1049/iet-com.2020.0236

Cited by 19 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed RDSAOA-EECP protocol and the benchmarked GAPSO-CS, EHOCS and GAMFO approaches are evaluated using MATLAB 2014b version installed over Intel Core i5 processor with 8GB RAM and 2.30 GHz CPU [27][28][29][30][31]. The simulation environment comprises of 500 sensor nodes deployed randomly in a two-dimensional network area of 1500x1500 square meters with one sink node placed at the position of (500m,500m) in the network.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

Rajeswarappa

Vasundra

2021

Wireless Pers Commun

View full text Add to dashboard Cite

Energy stability is considered as the core challenge of Wireless Sensor Networks (WSNs) as it is the main factor that contributes towards improved network lifetime expectancy. Clustering with maximized energy efficiency is determined to be the well documented NP hard optimization solution that has the possibility of resulting in prolonged network lifetime. Diversified number of computational approaches that includes nature inspired metaheuristic algorithms, reinforcement schemes and evolutionary algorithms were used for attaining efficient clustering process in WSNs. In this paper, Red Deer and Simulation Annealing Optimization Algorithm-based Energy Efficient Clustering Protocol (RDSAOA-EECP) is proposed for improving lifetime expectancy and energy stability in WSNs. This clustering protocol is proposed with the global optimization capability of the red deer optimization algorithm and local optimization potential of simulation annealing. In specific, RDA is used for diversification and Simulated Annealing (SA) for intensification in order to establish balance between them during the clustering process in order to sustain energy stability. This RDSAOA-EECP protocol is proposed with SA as the search tool that prevents the phases of fighting and roaring phases of RDA involved in the intensification process. Moreover, this clustering protocol inherits the potentialities of the proposed metaheuristic properties in order to achieve optimal cluster heads together with the optimal base station location for the objective of enhancing energy efficiency. The simulation results of the proposed RDSAOA-EECP protocol guaranteed its potentiality over the baseline clustering approaches in improving network lifetime, throughput, packet deliver rate with minimized network latency and energy consumptions independent to the homogeneous and heterogeneous sensor network setup considered for implementation.

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…It is also responsible for restarting the search potential of the SA algorithm. This process of temperature reinitialization is attained based on Equation (28).…”

Section: Process Of Reheatingmentioning

confidence: 99%

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

Rajeswarappa

Vasundra

2021

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…To avoid traffic, several mirror servers can be placed. To establish their judgment, the ROA evaluates a few riding groups that travel to the same location to win the race, to formulate their opinion [27,28]. The overall riders is divided into four sets, with certain riders in the respective group is chosen equal to the overall riders.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…The mean rate of transforming a non-operating node into an operating node is shown in Equation (20).…”

Section: Estimation Of Markov Process-based Opportunistic Trust Factomentioning

confidence: 99%

“…The flowchart shows the steps of the propounded MPOTFEM ( Figure 2). [20][21][22] and propounded schemes are shown in Table 2.…”

Section: Flowchart Illustrating the Steps In The Proposed Mpotfemmentioning

confidence: 99%

A Markov Process-based Opportunistic Trust Factor Estimation Mechanism for Efficient Cluster Head Selection and Extending the Lifetime of Wireless Sensor Networks

Janakiraman¹,

Priya²,

Devi³

et al. 2018

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

Optimal cluster head selection using modified rider assisted clustering for IoT

Cited by 19 publications

References 44 publications

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

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

A Markov Process-based Opportunistic Trust Factor Estimation Mechanism for Efficient Cluster Head Selection and Extending the Lifetime of Wireless Sensor Networks

Contact Info

Product

Resources

About