Wireless Sensor Networks Localization by Improved Whale Optimization Algorithm

Bačanin, Nebojša; Antonijevic, Milos; Bezdan, Timea; Živković, Miodrag; Rashid, Tarik A.

doi:10.1007/978-981-16-6332-1_62

Cited by 26 publications

(13 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FAMACROW [87] 2014 SETUP_MSG ACO algorithm 33. OPEN [4] 2017 Path routing algorithm Timer system 34. NCRO [88] 2016 Energy function Delay of nodes 35.…”

Section: Deterministic (Unequal)mentioning

confidence: 99%

“…Many clustering protocols have been proposed by researchers with various types of optimization techniques that are used to obtain the maximum lifetime of the sensor node. Firefly [3], Grey Wolf [2], Whale Optimization [4], Bat Algorithm [1], and Dragonfly Algorithm [5] are some of the important optimizations. This optimization algorithm optimized the selection process of cluster heads.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Prasad

Sasikumar

2023

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Research on popular themes today is mainly concentrated on cutting-edge home applications made up of Internet of Things gadgets. As its principal means of sensing, wireless sensor networks are a component of the Internet of Things. Tracking and monitoring applications benefit from the use of sensor nodes. Every step in the data collection, processing, and transmission processes carried out by wireless sensor nodes takes energy. Small capacity batteries on the sensor nodes in the networks make charging them frequently impractical. Energy optimization is required for sensor nodes since there is no other option but to replace the nodes. Clustering is a well-known and effective solution to increase the energy efficiency of the sensor nodes among the various routing techniques. The closest route between the cluster head node and the base station is thus determined using routing techniques in order to manage energy.

show abstract

“…FAMACROW [87] 2014 SETUP_MSG ACO algorithm 33. OPEN [4] 2017 Path routing algorithm Timer system 34. NCRO [88] 2016 Energy function Delay of nodes 35.…”

Section: Deterministic (Unequal)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Prasad

Sasikumar

2023

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Therefore, the scientists have experimented with wide range of optimization algorithms on a variety of practical problems. Some of the most promising domains include medical diagnostics support [14,20,24,33,43], wireless sensor network tuning [4,9,12,48,57,66], stock price estimations [16], as well as intrusion detection and security domain [1,31,41,45,55,56,60,65] and plant classifying task [17].…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Gajevic¹,

Milutinovic²,

Krstovic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

Self Cite

View full text Add to dashboard Cite

This paper explores classification of datasets for Healthcare 4.0 using artificial neural networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes include internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, and other industry 4.0 procedures. Health issues identification are critical since prompt treatment improves the quality of life for individuals affected. One of the most difficult challenges for artificial intelligence (AI) is selecting control parameters that are appropriate for the situation at hand. This paper presents a metaheuristics-based method for training the artificial neural network, by utilizing the SCA.

show abstract

“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Section: Swarm Intelligence Applications In Machine Learningmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

Self Cite

View full text Add to dashboard Cite

Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

show abstract

Wireless Sensor Networks Localization by Improved Whale Optimization Algorithm

Cited by 26 publications

References 39 publications

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of Things-Survey

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Contact Info

Product

Resources

About