Accurate Range-Free Localization Algorithms Based on PSO for Wireless Sensor Networks

Hadir, Abdelali; Regragui, Younes; García, Nuno M.

doi:10.1109/access.2021.3123360

Cited by 21 publications

(12 citation statements)

References 48 publications

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“…Our primary effort is devoted to determining how well various hybrid-based improvements to the original DVhop algorithm perform in detecting and pinpointing hostile nodes that have hijacked the beacon node and are supplying false routing information [57]. All of our proposed algorithms have been implemented in the MATLAB simulator for thorough testing and analysis of their localization faults and precision.…”

Section: Simulation and Results Discussionmentioning

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

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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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Section: Simulation and Results Discussionmentioning

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

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“…Where 𝑓𝑓(•) denotes the function in (14), 𝐸𝐸𝑑𝑑 ̂𝑙𝑙𝑠𝑠 is the distance between the UN 𝑢𝑢 𝑙𝑙 and the AN 𝑎𝑎 𝑠𝑠 estimated by the model. If the predicted coordinate of the UN 𝑢𝑢 𝑙𝑙 is (𝑥𝑥 � 𝑙𝑙 , 𝑦𝑦 � 𝑙𝑙 ), the following set of distance equations can be constructed according to the coordinates of the AN 𝑎𝑎 𝑠𝑠 .…”

Section: Node Localizationmentioning

confidence: 99%

“…In the simulation experiment part, we simulated a 100𝑚𝑚 × 100𝑚𝑚 area with 100 randomly deployed wireless sensor nodes. The performance metrics of the algorithm are evaluated by changing the AN percentage and the sensor nodes' communication radius and comparing the DV-Hop [12], PSODV-Hop [14], WND-DV-Hop [15], and RANN [28] with the algorithm in this paper. Considering the interference factors in the real application scenario, we considered the Degree of Radio Irregularity (DOI) in our simulation and set it to 0.05 according to reference [36].…”

Section: Incremental Strategy-based Residual Regression Networkmentioning

confidence: 99%

“…The introduction of free gradient effectively solves the problem that the Cuckoo search algorithm converges slowly and tends to fall into local optimality and further improves the localization accuracy. Reference [14] improved the DV-Hop by introducing the particle swarm optimization (PSO) to convert the node localization problem into a particle search problem and analyzed the localization performance of the optimized algorithm under different network expansion structures. Reference [15] proposed a WND-DV-Hop algorithm by constructing a weighting factor based on the beacon node hop counts, and the least squares method was used to correct hop counts between nodes.…”

Section: Introductionmentioning

confidence: 99%

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Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

2022

KSII TIIS

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The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

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“…The particle swarm optimisation (PSO) algorithm is a well-known optimisation method, broadly used over the past two decades, which is effective for the swarm cooperation [18,19,20,21,22]. The PSO algorithm has been widely applied on WSNs to solve the cooperative localisation problem [23,24,25,26,27,28]. Chuang et al [23] carried out PSO-based localisation strategy based on the model with a sufficient and insufficient number of neighbouring anchors, respectively.…”

Section: Introductionmentioning

confidence: 99%

Cooperative localisation of UAV swarm based on adaptive SA-PSO algorithm

Fang

et al. 2022

Aeronaut. j.

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In this paper, to address the cooperative localisation of a heterogeneous UAV swarm in the GNSS-denied environment, an adaptive simulated annealing-particle swarm optimisation (SA-PSO) cooperative localisation algorithm is proposed. Firstly, the forming principle of the communication and measurement framework is investigated in light of a heterogeneous UAV swarm composition. Secondly, a reasonably cooperative localisation function is established based on the proposed forming principle, which can minimise the relative localisation error with limited available information. Then, an adaptive weight principle is incorporated into the particle swarm optimisation (PSO) for better performance. Furthermore, in order to overcome the drawbacks of PSO algorithm easily falling into the local extreme point, an adaptive SA-PSO algorithm is improved to promote the convergence speed of cooperative localisation. Finally, comparative simulations are performed among the adaptive SA-PSO, adaptive PSO, and PSO algorithms to demonstrate the feasibility and superiority of the proposed adaptive SA-PSO algorithm. Simulation results show that the proposed algorithm has better performance in convergence speed, and the cooperative localisation precision can be guaranteed.

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Accurate Range-Free Localization Algorithms Based on PSO for Wireless Sensor Networks

Cited by 21 publications

References 48 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

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

Cooperative localisation of UAV swarm based on adaptive SA-PSO algorithm

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