Comparison of Particle Swarm Optimization algorithms in Wireless Sensor Network node localization

Cao, Cen; Ni, Qingjian; Yin, Xiaoxin

doi:10.1109/smc.2014.6973916

Cited by 13 publications

(14 citation statements)

References 14 publications

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“…Moreover, some studies have shown that bigger inertia weight contributes global search, while smaller inertia weight contributes local search [36]. In [36], the authors use the following equation to express the inertia weight:

ω = ω_{\max} - \frac{ω_{\max} - ω_{\min}}{k_{\max}} \times k,

where,

ω_{\max}

is the initial weight,

ω_{\min}

is the final weight,

k_{\max}

is the maximum number of iterations, and

k

is the current iteration. In this paper, we use the same parameters as [36].…”

Section: Range-based Estimation Algorithm Using Psomentioning

confidence: 99%

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Luo

Fan

2017

Sensors

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Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assistance. Sensor nodes location in UWSNs is an especially relevant topic. Global Positioning System (GPS) information is not suitable for use in UWSNs because of the underwater propagation problems. Hence, some localization algorithms based on the precise time synchronization between sensor nodes that have been proposed for UWSNs are not feasible. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme based on the Particle Swarm Optimization (PSO) algorithm to obtain the coordinates of the unknown sensor nodes. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence, in this algorithm, we use a small number of mobile beacons to help obtain the location information without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA achieved by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

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ω = ω_{\max} - \frac{ω_{\max} - ω_{\min}}{k_{\max}} \times k,

where,

ω_{\max}

is the initial weight,

ω_{\min}

is the final weight,

k_{\max}

is the maximum number of iterations, and

k

is the current iteration. In this paper, we use the same parameters as [36].…”

Section: Range-based Estimation Algorithm Using Psomentioning

confidence: 99%

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Luo

Fan

2017

Sensors

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“…Each particle in DPSO [19] pays full attention to the historical information of all neighboring particles, instead of only focusing on the particle which gets the optimum position in the neighborhood. For particle i , the update function of DPSO is

\begin{matrix} P_{i j} = P_{i j} + c_{1} false(P_{i j} - P_{i j}^{'} false) + \frac{c_{2}}{K} \sum_{k = 1}^{K} false(Q_{k j} - P_{i j} false) + \frac{c_{3} φ_{3 j}}{K} \sum_{k = 1}^{K} | Q_{k j} - Q_{i j} | & j = 1, 2, \end{matrix}

where K is the number of neighboring particles of the i th particle.…”

Section: A Survey Of Pso-based Localization Algorithmsmentioning

confidence: 99%

“…TPSO [19] employs time-varying

c_{1}

c_{2}

, and ω (see Equation (9)) to achieve proper balance between global and local exploitation, where

c_{1} = (c_{1 f} - c_{1 s}) \frac{t}{t_{m a x}} + c_{1 s}, c_{2} = (c_{2 f} - c_{2 s}) \frac{t}{t_{m a x}} + c_{2 s},

where

c_{1 s}

c_{1 f}

c_{2 s}

, and

c_{2 f}

are initial and final values of

c_{1}

and

c_{2}

, respectively.…”

Section: A Survey Of Pso-based Localization Algorithmsmentioning

confidence: 99%

“…This paper focuses on the PSO-based localization algorithms for static WSNs, where all sensors are static after deployment. Some PSO-based localization algorithms with different population topologies are compared in Cao et al [19]. However, they do not consider the recently-proposed PSO-based localization algorithms, nor do they give parameter selections.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Cui

Shu

Song

et al. 2017

Sensors

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Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

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“…Implementation of binary PSO for assisting in localization problem was seen in the work of Zain and Shin [28]. Cao et al [29] have investigated the effectiveness of PSO by comparing with the conventional approach for solving localization problem. Jing et al [30] have presented a similar approach where PSO was found to enhance the clustering operation of WSN.…”

Section: Introductionmentioning

confidence: 99%

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

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Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency. [5] etc. There has been a voluminous amount of investigation carried out in this area for addressing such issues, but still majority of the issues are yet to meet its full-proof solution. The primary cause of this problem is basically the sensor node, which is very small in size, posses low computational capabilities, and powered by a battery that has limited lifetime. The biggest challenge for the researchers to prove the applicability of their presented system in real-time sensor node that is not found to be discussed in majority of the existing research work. However, some of the researchers avoid such problems by considering either benchmark test bed, or adopt the configuration of some real-time motes e.g. Berkley Mote, MicaZ mote [6], etc. Hence, optimization is the best possibility in such scenario of node resources constraints. There are various methods by which optimizations have been carried out towards improving the performance of sensor node e.g. neural network, genetic algorithm, swarm intelligences etc [7]. Particle Swarm Optimization (PSO) is one such technique that uses many numbers of iterations in order to explore the best solution against the problems posed [8], [9]. From computational viewpoint, PSO enhances the candidate solution in the perspective of the anticipated outcomes and given problem in wireless sensor network. The problem is optimized by considering the candidate solution and its population (also called as particles) and this form of the candidate solution is subjected to iterative processing in order to obtain personal and global best outcome from the position and velocity of the particles. The primary reason behind the adoption of PSO is basically its intelligence-based approach that can be possibly implemented on any of Keyword: Clustering

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Comparison of Particle Swarm Optimization algorithms in Wireless Sensor Network node localization

Cited by 13 publications

References 14 publications

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

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