A PSO Based Improved Localization Algorithm for Wireless Sensor Network

Singh, Santar Pal; Sharma, Sandesh

doi:10.1007/s11277-017-4880-1

Cited by 87 publications

(66 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments verify its influence on the average localization error in three aspects: the communication radius of nodes, the percentage of anchor nodes, and the total number of nodes. The parameters used in the DEIDV-Hop experiment are shown in Table 1, while the parameter settings for PSODV-Hop [43] and GSODV-Hop [44] are identical to original references and shown in Tables 2 and 3, When designing the C-shaped area, we considered excavating a part of the square area during the simulation. The cut-out area is 30 × 70 m 2 (the original area was 100 × 100 m 2 ).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Han

et al. 2020

Sensors

View full text Add to dashboard Cite

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The DEIDV-Hop algorithm is compared with DV-Hop, PSODV-Hop [43], and GSODV-Hop [44] through simulation implemented on MATLAB 2014a [45], running on a desktop PC with one Intel(R) Core(TM) i5-6500 CPU @3.20 GHz processor, 8 GB RAM, and Windows 7 OS.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Han

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The particle swarm optimization (PSO) is one of the best known swarm intelligence representatives [30]. This algorithm was modified/improved and adapted for tackling many real-world optimization problems [31][32][33]. The PSO metaheuristics has also implementations for various problems from cloud computing.…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

View full text Add to dashboard Cite

Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

show abstract

“…PSO algorithm performs better in terms of localization accuracy, computational complexity and/or convergence speed compared with the other optimization methods [24][25][26][27][28][29][30]. PSO and BFA (Bacterial Foraging Algorithm) are applied to localize a WSN deployed by an unmanned aerial vehicle in [24], and simulation results showed that PSO is faster than BFA.…”

Section: Related Workmentioning

confidence: 99%

“…The fuzzyextreme learning machine with PSO [29] uses resultant force to move approximate node location closer to actual position. PSO-based improved DV-hop algorithm [30] uses PSO to correct estimated positions after DV-hop.…”

Section: Related Workmentioning

confidence: 99%

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Cui

Liang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

show abstract

A PSO Based Improved Localization Algorithm for Wireless Sensor Network

Cited by 87 publications

References 26 publications

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Contact Info

Product

Resources

About