Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Kulkarni, Raghavendra V.; Venayagamoorthy, G.K.

doi:10.1109/tsmcc.2010.2054080

Cited by 626 publications

(268 citation statements)

References 31 publications

(32 reference statements)

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“…Particle swarm optimization was inspired by the simulation of a simplified social mode for a group of birds, firstly proposed by Kennedy in 1995 [46]. The method has characteristics of ease of implementation, high quality of solutions, computational efficiency, and speed of convergence [47]. For the optimization of the sensors, the fitness function can be defined based on bending deformation energy.…”

Section: Experimental Schemementioning

confidence: 99%

Experimental Damage Identification of a Model Reticulated Shell

Hao

et al. 2017

Applied Sciences

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Abstract:The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg-Marquardt algorithm (L-M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

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Section: Experimental Schemementioning

confidence: 99%

Experimental Damage Identification of a Model Reticulated Shell

Hao

et al. 2017

Applied Sciences

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“…Particle swarm optimization (PSO) is a branch of heuristic techniques that optimizes a complex problem via repetitively attempting to get better candidate solution with respect to a specified measure of quality [22]. The flow chart representation of this method can be shown by Fig.1.…”

Section: A Psomentioning

confidence: 99%

An Improved Localization Algorithm for Error Minimization in Wireless Sensor Networks

Singh¹,

Sharma²

2017

IJET

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Abstract-Wireless sensor networks (WSNs) are comprised of huge number of resource constrained petite devices recognized as sensor nodes. Data collection and forwarding is an important function of WSN. In the majority of applications, the location of the data is of much significance. Such kind of info can be acquired by using localization schemes. So, node localization is extremely essential to determine the location of node by using localization algorithms. Hence, localization becomes a hot research issue in WSNs. Many localization algorithms have been projected to precisely locate nodes in WSNs. These algorithms are broadly classified into range based and range free categories. Range based methods use exact computation measure (distance or time) among nodes in the network. So range based methods needs some additional hardware for such computation while range free methods don't require any specific hardware. Range free methods use connectivity information between nodes in network. So range free methods are considered cost effective alternatives to range based methods. In this paper, we propose a novel range free localization algorithm for WSN on the basis of swarm intelligence. We perform the comparative analysis of proposed algorithm with DV-Hop algorithms on the basis of localization error and accuracy. Simulation results shows that proposed algorithm outperform the DV-Hop algorithm. In this paper, we proposed a modified algorithm for localization on the basis of distance vector hop algorithm. The proposed method reduces the localization error and improves accuracy without raising the cost of the sensor node. Here the presented work contributes three most important things to localization problem. First, we propose a new modified algorithm that is easy to use and cost effective. Second, the proposed algorithm reduces the localization error as compared to DV-Hop. Third, we explore the impact of anchors and communication range on performance of new modified algorithm. The remaining part of paper is planned as; section 2 focused on a literature review. Section 3 focused on the DV Hop localization algorithms. The proposed algorithm is illustrated in section 4. Section 5 focused on the result and discussion of performance of algorithms. Finally, section 6 concludes the work. Keyword

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“…The goal of data analysis is to make the fastest possible revelation toward the "useful" information. Swarm intelligence is an effective way to handle these data, and to obtain "useful" information [23].…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Swarm Intelligence in Big Data Analytics

Cheng

Shi

Qin

et al. 2013

Lecture Notes in Computer Science

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Abstract. This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data analytics, which include the high dimensionality of data, the dynamical change of data, and the multi-objective of problems, are discussed. Swarm intelligence, which works with a population of individuals, is a collection of nature-inspired searching techniques. It has effectively solved many large-scale, dynamical, and multi-objective problems. Based on the combination of swarm intelligence and data mining techniques, we can have better understanding of the big data analytics problems, and designing more effective algorithms to solve real-world big data analytics problems.

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Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Cited by 626 publications

References 31 publications

Experimental Damage Identification of a Model Reticulated Shell

Experimental Damage Identification of a Model Reticulated Shell

An Improved Localization Algorithm for Error Minimization in Wireless Sensor Networks

Swarm Intelligence in Big Data Analytics

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