Detection of Sybil Nodes in Wireless Sensor Networks

Debasis, Kumar; Singh, M. P.; Kumar, Prabhat; Bhaskar, Sachin

doi:10.17485/ijst/2017/v10i3/110641

Cited by 3 publications

(2 citation statements)

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“…The sensors are outfitted with the minute batteries to supply energy to sense, process & transmit the information to other nodes or the base station using wireless connectivity, nodes consume more energy to communicate depending on the location of the Cluster head (CH) or the Base Station (BS) rather than sensing & processing data. Once every sensor nodes inside network are out of power then entire network dies resulting in the network failure, hence it is much-needed to optimize the energy consumption of the node (13) . Optimization can be achieved by finding the rules or procedure that reduces the number of node's death (14) .…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar¹,

Anitha²,

Mallikarjunaswamy³

2021

IJST

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Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network.

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Section: Introductionmentioning

confidence: 99%

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar¹,

Anitha²,

Mallikarjunaswamy³

2021

IJST

View full text Add to dashboard Cite

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“…A group of micro sensor nodes constitute a wireless sensor network and are deployed in a particular area with at least one sink or base station. https://www.indjst.org/ In the wireless sensor network, there are different kinds of sensor nodes which include source sensor node (normal node), intermediate sensor nodes (cluster head), and base station (1)(2)(3) as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

Pavithra¹,

Babu²

2021

IJST

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Objectives: To efficiently monitor the highly confined area by using security measurement methods to enhance the lifetime of a large area coverage network. Methods : the modified nodes are included in the normal execution process of the network to calculate the efficiency and lifetime of the WSN. With the help of the simulation and analysis process, the proposed protocol is found to be more powerful to impact the sustainability of the network by prolonging the lifetime of the nodes. To perform this operation, the following parameters are considered: Area of the monitoring area, Number nodes, Sink portion, Energies of radio amplifier systems, Data aggregation, node electronics, and data packet length. Findings: The proposed optimal energy cluster routing protocol finds the best topological structural protocol to select the cluster head with optimal connectivity path with other nodes in a group. The data of energy utilization, data communication rate between nodes and cluster head, and analysis of the number of alive nodes concerning time are obtained. Novelty: the proposed Optimal efficient cluster routing protocol (OECRP) selects the cluster head by considering energy consumption, cluster sustainability, and its proper communication with other nodes. OECRP is used to develop the optimal topological structural protocol to connectivity path with other nodes in a group.

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

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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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.

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Detection of Sybil Nodes in Wireless Sensor Networks

Cited by 3 publications

References 0 publications

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

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

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