Optimized Energy Aware Routing Based on Suitable Based Antlion Group with Advanced Algorithm (SA-AOA) in Wireless Sensor Network

Prakash, N.; Rajalakshmi, M.; Nedunchezhian, R.

doi:10.1007/s11277-020-07178-5

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…The method achieved higher normalized network energy, but the method failed to consider another optimization algorithm for better data delivery. Prakash et al 16 developed Suitable Based Antlion Group with Advanced Optimization Algorithm (SA‐AOA) for routing based on the energy. Here, the network lifetime is extended, but still, the method is poor nearby pursuit capacity.…”

Section: Literature Surveymentioning

confidence: 99%

“…The limitations faced by conventional black hole attack detection mechanisms are portrayed below as follows: In Reference 18, the LPO algorithm and fuzzy logic are introduced for energy‐aware clustering in WSN. However, it is more difficult to recharge the sensor node batteries, so that it is considered as the main challenging issue, which should be considered. In Reference 16, SA‐AOA is proposed for energy‐aware routing in WSN. However, several seminars are involved in the WSN network with computing, communicating, and sensing capabilities.…”

Section: Literature Surveymentioning

confidence: 99%

“…where, the term H 𝜏 best is selected randomly from the solution pool, the terms H r1 and H r2 is the two candidate solutions that are randomly chosen from the current swarm. Substitute Equation (16) in Equation ( 15),…”

Section: Algorithmic Steps Of the Proposed Fractional Artificial Lion...mentioning

confidence: 99%

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Safe and secured routing using multi‐objective fractional artificial lion algorithm in WSN

Prakash

Kavitha

Reddy

2022

Concurrency and Computation

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The routing process in WSN needs to be more secure and safe, thus a secure routing process is carried out in this research, where the source node transmits packet simultaneously to destination. WSN nodes are simulated initially, and then the optimal cluster head (CH) from the entire nodes are selected. Here, CH selection is done with the developed Fractional Artificial Lion algorithm (FAL) method.The proposed FAL approach is introduced by combining fractional Calculus, Lion Optimization Algorithm (LOA), and the Artificial Bee Colony (ABC). Moreover, the fitness parameters, like delay, energy, distance, and link lifetime (LLT) where the minimal distance is chosen as the best path. Consequently, route maintenance is done after the secure routing. Moreover, the performance of devised FAL is compared with various performance metrics, like several alive nodes, energy, distance, and throughput. The developed FAL approach obtains a maximal alive node of 45, a minimal distance of 9.29 m, maximal energy of 0.14 J, and maximal throughput of 92.25%.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Safe and secured routing using multi‐objective fractional artificial lion algorithm in WSN

Prakash

Kavitha

Reddy

2022

Concurrency and Computation

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“…These operators are used to generate new candidate solutions during the search process. The effectiveness of these operators can greatly impact the performance of the algorithm and the quality of the solutions obtained [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Saad

Jaafar

Chellappan

2023

Sensors

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Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

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Design of Interior Design Database Based on Ant Colony Algorithm

Zhang

2022

Cyber Security Intelligence and Analytics

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Optimized Energy Aware Routing Based on Suitable Based Antlion Group with Advanced Algorithm (SA-AOA) in Wireless Sensor Network

Cited by 14 publications

References 23 publications

Safe and secured routing using multi‐objective fractional artificial lion algorithm in WSN

Safe and secured routing using multi‐objective fractional artificial lion algorithm in WSN

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Design of Interior Design Database Based on Ant Colony Algorithm

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