Energy‐Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

Chen, Junfeng; Sackey, Samson Hansen; Anajemba, Joseph Henry; Zhang, Xuewu; He, Ye

doi:10.1155/2021/5541449

Cited by 20 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Localization Techniques. Numerous wireless sensor network (WSN) applications rely on localization to locate a target by comparing the signal strengths of transmitters and receivers already set up in the region of interest [33,34]. Some algorithms are essential for finding and assessing the location and position of the nodes and security enhance-ment for precise location of the target.…”

Section: Cluster Formation and Data Aggregationmentioning

confidence: 99%

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

show abstract

Section: Cluster Formation and Data Aggregationmentioning

confidence: 99%

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…clustering based wheel graph theory is presented to decrease the difficulty of succeeding CSO and normalize the subnetwork quantity. Chen et al [17] proposed energyeffective clustering and localization cantered on genetic algorithm (ECGAL), where the coverage connection, RE, and distance estimation are designed for procedure the FF. The presented method exhausts a smaller number of energy and expands the existence of wireless networks.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

Bhaskaran¹,

Kumar²,

Shanthi³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

In recent times, real time wireless networks have found their applicability in several practical applications such as smart city, healthcare, surveillance, environmental monitoring, etc. At the same time, proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks. This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed IM-EECNL technique involves two major processes namely node localization and clustering. Firstly, Chaotic Water Strider Algorithm based Node Localization (CWSANL) technique to determine the unknown position of the nodes. Secondly, an Oppositional Archimedes Optimization Algorithm based Clustering (OAOAC) technique is applied to accomplish energy efficiency in the network. Besides, the OAOAC technique derives a fitness function comprising residual energy, distance to cluster heads (CHs), distance to base station (BS), and load. The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency. A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio (PDR) of 0.985.

show abstract

“…(6) New nodes generated from GA after selection, crossover and mutation calculates its distance in order to find the estimated position. (7) If the distance between target node and known node is insignificant but ≠0. (8) Compute the localization error LE i using (3).…”

Section: Number Of Formed Clustersmentioning

confidence: 99%

“…Because a sensor node's energy supply is so low, it is unable to send estimates directly to the ground station. The network is divided into small groups known as grids (clusters) to decrease the amount of energy used for communication [ 6 , 7 ]. Every grid has a grid leader, which is the center of the grid (cluster center).…”

Section: Introductionmentioning

confidence: 99%

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Chen

Sackey

Ansere

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Finding the location of sensors in wireless sensor networks (WSNs) is a major test, particularly in a wide region. A salient clustering approach is laid out to achieve better performance in such a network using an evolutional algorithm. This paper developed a clustered network called neighborhood grid cluster which has a node assuming the part of a cluster center focused in every grid. Grid-based clustering is less difficult and possesses a lot of benefits compared to other clustering techniques. Besides, we proposed a localization algorithm that centers around assessing the target area by considering the least estimated distance embedded with the genetic algorithm. Performance standards incorporate the energy representation, connectivity stratagem, and distance measure as fitness functions that assess our localization problem to demonstrate its viability. Simulation results confirm that our approach further improves localization accuracy, energy utilization, node lifetime, and localization coverage.

show abstract

Energy‐Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

Cited by 20 publications

References 30 publications

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Contact Info

Product

Resources

About