An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks
J Amutha, S Sharma, SK Sharma - Expert Systems with Applications, 2022 - Elsevier
In wireless sensor networks (WSNs), energy efficiency is a significant design challenge that
can be resolved by clustering and routing approaches. They are considered as Non …
can be resolved by clustering and routing approaches. They are considered as Non …
Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview
Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and
accurate localization of sensor nodes is vital for their effective operation. In recent years …
accurate localization of sensor nodes is vital for their effective operation. In recent years …
A machine learning approach to predict the k-coverage probability of wireless multihop networks considering boundary and shadowing effects
Network coverage is a pivotal performance metric of wireless multihop networks (WMNs)
determining the quality of service rendered by the network. Earlier, a few studies have …
determining the quality of service rendered by the network. Earlier, a few studies have …
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
Abstract Wireless Sensor Networks (WSNs) is a promising technology with enormous
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
[HTML][HTML] LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the k-Barriers for Intrusion Detection Using …
The dramatic increase in the computational facilities integrated with the explainable
machine learning algorithms allows us to do fast intrusion detection and prevention at …
machine learning algorithms allows us to do fast intrusion detection and prevention at …
Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment
S Nematzadeh, M Torkamanian-Afshar… - Neural Computing and …, 2023 - Springer
The node deployment problem is a non-deterministic polynomial time (NP-hard). This study
proposes a new and efficient method to solve this problem without the need for predefined …
proposes a new and efficient method to solve this problem without the need for predefined …
[HTML][HTML] Energy-efficient network protocols and resilient data transmission schemes for wireless sensor Networks—An experimental survey
D Dhabliya, R Soundararajan, P Selvarasu… - Energies, 2022 - mdpi.com
Wireless sensor networks (WSNs) are considerably used for various environmental sensing
applications. The architecture and internal specifications of WSNs have been chosen based …
applications. The architecture and internal specifications of WSNs have been chosen based …
Intrusion detection system in wireless sensor network using conditional generative adversarial network
Wireless communication networks have much data to sense, process, and transmit. It tends
to develop a security mechanism to care for these needs for such modern-day systems. An …
to develop a security mechanism to care for these needs for such modern-day systems. An …
Unveiling the cutting edge: A comprehensive survey of localization techniques in WSN, leveraging optimization and machine learning approaches
Sensor node localization is an important feature of many applications, including wireless
sensor networks and location-based services. The accurate localization of sensor nodes …
sensor networks and location-based services. The accurate localization of sensor nodes …
P2CA-GAM-ID: Coupling of probabilistic principal components analysis with generalised additive model to predict the k− barriers for intrusion detection
Drastic advancement in computing technology and the dramatic increase in the usage of
explainable machine learning algorithms provide a promising platform for developing robust …
explainable machine learning algorithms provide a promising platform for developing robust …