A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

Sensor Localization in Wireless Sensor Networks Using Cultural Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 106-122
Author(s):  
Vaishali Raghavendra Kulkarni ◽  
Veena Desai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Particle Swarm Optimization Algorithm ◽  
Computing Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cultural Algorithm ◽  
Sensor Localization ◽  
Bee Colony ◽  
Multi Stage

Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.

Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

2020 ◽  
pp. 415-436
Author(s):  
Vaishali Raghavendra Kulkarni ◽  
Veena Desai ◽  
Akash Sikarwar ◽  
Raghavendra V. Kulkarni
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Sensor Nodes ◽  
Circular Path ◽  
Cultural Algorithm ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Mobile Anchor ◽  
Planning Methods ◽  
Planning Algorithm

Sensor localization in wireless sensor networks has been addressed using mobile anchor (MA) and a metaheuristic algorithm. The path of a MA plays an important role in localizing maximum number of sensor nodes. The random and circle path planning methods have been presented. Each method has been evaluated for number of localized nodes, accuracy, and computing time in localization. The localization has been performed using trilateration method and two metaheuristic stochastic algorithms, namely invasive weed optimization (IWO) and cultural algorithm (CA). Experimental results indicate that the IWO-based localization outperforms the trilateration method and the CA-based localization in terms of accuracy but with higher computing time. However, the computing speed of trilateration localization is faster than the IWO- and CA-based localization. In the path-planning algorithms, the results show that the circular path planning algorithm localizes more nodes than the random path.

Improved DV-Hop Algorithm for Enhancing Localization Accuracy in WSN

2014 ◽  
Vol 543-547 ◽  
pp. 3256-3259 ◽  
Author(s):  
Da Peng Man ◽  
Guo Dong Qin ◽  
Wu Yang ◽  
Wei Wang ◽  
Shi Chang Xuan
Keyword(s):  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Global Average ◽  
Key Technologies ◽  
Simulation Results ◽  
Range Free

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

scholarly journals Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4152
Author(s):  
Sana Messous ◽  
Hend Liouane ◽  
Omar Cheikhrouhou ◽  
Habib Hamam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Position Estimation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Recursive Computation

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

scholarly journals Regularized Least Square Multi-hops Localization Algorithm for wireless sensor networks

2021 ◽  
Author(s):  
Hend Liouane ◽  
Sana Messous ◽  
Omar Cheikhrouhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Connectivity ◽  
Least Square ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Awareness ◽  
Localization Algorithm ◽  
Multihop Wireless ◽  
Localization Accuracy

Abstract Multi-hop localization is a an important technique for Wireless Sensor Networks. Location awareness is very crucial for almost existing sensor network applications. However, using Global Positioning System (GPS) receivers to every node is very expensive. Therefore, the Distance Vector-Hop algorithm (DV-Hop) is proposed and very famous for its simplicity and localization accuracy for Wireless Sensor Networks. The cited algorithm uses a small number of anchor nodes, which are equipped with GPS, thus their locations are known, while other nodes estimate their location from the network connectivity information. However, DV-Hop presents some deficiencies and drawbacks in terms of localization accuracy. Therefore, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multihop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

2020 ◽  
Vol 10 (1) ◽  
pp. 94-109 ◽  
Author(s):  
Rekha Goyat ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Hye-Jin Kim ◽  
Se-Jung Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Research Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Anchor Nodes ◽  
Least Energy ◽  
Range Free

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

scholarly journals Genetic Algorithms for Localization in WSN

2020 ◽  
Vol 9 (5) ◽  
pp. 2397-2401
Keyword(s):  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Bat Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Node Localization ◽  
Soft Computing Techniques ◽  
Anchor Nodes ◽  
The Mean

In wireless sensor networks, localization is a way to track the exact location of sensor nodes. Occasionally node localization may not be accurate due to the absence or limitation of anchor nodes. To reduce the mean localization error, soft computing techniques such as BAT and bacterial foraging driven bat algorithm (BDBA) are utilized in literature. For better localization with reduced error, in this paper, firefly driven bat algorithm (FDBA) is proposed, which combines the heuristic of firefly and BAT algorithms. Our proposed FDBA algorithm provides better localization in terms of error of 60% and 40 % less error as compared to BAT and BDBA algorithm, respectively.

scholarly journals Social Learning Class Topper Optimization (SL-CTO) Based Hop Localization Technique for Wireless Sensor Network

2021 ◽  
Author(s):  
TAPAN KUMAR MOHANTA ◽  
Dushmanta Kumar Das
Keyword(s):  
Social Learning ◽  
Computing Time ◽  
Error Variance ◽  
Localization Error ◽  
Localization Technique ◽  
Current Localization ◽  
The Social ◽  
Rectification Factor ◽  
Network Simulations ◽  
Anisotropic Network

Abstract To address the current situation limitation of traditional DV-Hop, we suggested a DV-Hop localization based on a rectification factor using the Social Learning Class Topper Optimization (SL - CTO) algorithm in that paper. In order to adjust the number of hops between beacon nodes, we have implemented a rectification factor in the suggested method. By measuring the dimensions of all the beacons at dumb nodes, the suggested algorithm decreases communication among unknown or dumb and beacon nodes. The model of network imbalance, It is often considered to be demonstrate a applicability of the Proposed approach in the anisotropic network. Simulations have been performed on LabVIEW@2015, and Comparisons were made with conventional DV-Hop, particle swarm optimization-based DV-Hop and runner-root optimization-based DV-Hop for our proposed algorithm. In comparison to current localization methods, simulation outcomes showed that the proposed localization technique reduces computing time, localization error variance and localization error.

scholarly journals Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

2021 ◽  
Author(s):  
Lismer Andres Caceres Najarro ◽  
Iickho Song ◽  
Kiseon Kim
Keyword(s):  
Sensor Node ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Healthcare Industry ◽  
Target Node ◽  
Node Localization ◽  
Localization Accuracy ◽  
Fundamental Limitations ◽  
Points Of View

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

scholarly journals Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

2021 ◽  
pp. 242-249
Author(s):  
M.Shahkhir Mozamir ◽  
◽  
Rohani Binti Abu Bakar ◽  
Wan Isni Soffiah Wan Din ◽  
Zalili Binti Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Localization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

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