Implementation of a PSO Based Improved Localization Algorithm for Wireless Sensor Networks

Singh, Santar Pal; Sharma, S. C.

doi:10.1080/03772063.2018.1436472

Cited by 69 publications

(57 citation statements)

References 25 publications

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“…Simulation narrates that the results of this approach are giving the best result in form of LE and accuracy. Again, the same author has designed an approach in which, by modification of basic DV‐HOP algorithm with the addition of PSO, using meta‐heuristic is done. The results of this approach have been analyzed in the form of various parameters.…”

Section: Related Workmentioning

confidence: 99%

“…The validation of the same analysis and comparison of the DEC‐hop algorithm must be done with other existing algorithms to prove its efficiency. The same has been done in the next section of this paper, where comparisons have been done with various improved DV‐hop localization algorithms …”

Section: Proposed (Dec‐hop) Algorithmmentioning

confidence: 99%

“…The amount of sensor nodes, anchor nodes, and range value is varied for measuring the effect of these parameters on the algorithms through the simulation metric, ie, LE, variance, and accuracy. From Figures , 28, and 30, the impact of changing the anchor node amount on the error, variance, and accuracy is evaluated, corresponding to the total anode amount of 100, and the range value set at 30 m. It is depicted from the simulation results that the proposed (DEC‐hop) algorithm performed better in all aspects in comparison to PSO‐based DV‐hop and DV‐hop for the LE, error variance, and accuracy. From Figure 26, it is concluded that error corresponding to anchor node amount of 20 in the case of the proposed algorithm is 18% lesser than PSO‐based DV‐hop and 42% lesser than the DV‐hop algorithm.…”

Section: Simulation and Comparisonsmentioning

confidence: 99%

“…From Figures , 34, and 36, the impact of changing the node amount on the error, variance, and accuracy is evaluated corresponding to the fixed anchor node amount of 10 and the range value set at 30 m. It is depicted that the DEC‐hop algorithm performed better in all aspects in comparison to PSO‐based DV‐hop and DV‐hop for the error, variance, and accuracy. From Figure 32, it is concluded that error variance corresponding to the node amount of 150 in the case of the proposed (DEC‐hop) algorithm is 7% lesser than PSO‐based DV‐hop and 9% lesser than the DV‐hop algorithm.…”

Section: Simulation and Comparisonsmentioning

confidence: 99%

“…Even though the basic DV‐hop is having low complexity, the accuracy is also very low. The computational cost of GA+ simplex method, improved DV‐hop (CAI), and PSO‐based DV‐hop is high because of the number of steps that needs to be executed in each one of them for the location estimation is more, which leads to more packets to be transferred during the execution of its phases. Hence, there is a trade‐off between the computational complexity and the accuracy for any localization algorithm.…”

Section: Simulation and Comparisonsmentioning

confidence: 99%

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Design and analysis of distance error correction–based localization algorithm for wireless sensor networks

Prashar

Jyoti

Kumar

2018

Trans Emerging Tel Tech

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Localization error is the main criteria to decide the efficiency on any localization approach. In case of range‐free hop‐based localization approaches, position coordinates estimation is done through the common steps like the calculation of the minimum hop count, hop size, and then later on applying the multilateration or least square approach. There are many improvements that have been made in the form of various algorithms through some changes in the common steps for the reduction of localization error. It has been observed that the existing algorithms are still having the highest value of localization error. To overcome the deficiencies in the existing algorithms, a novel distance error correction metric–based approach has been introduced. This paper presents a distance error correction–based algorithm (DEC‐hop) for the hop‐based localization approach for a wireless sensor network. Distance‐based error correction factor that is introduced in this paper further reduces the localization error value to some extent. For measuring the effectiveness and the preciseness of the given approach, it is inspected with other improved algorithms based on DV‐hop. Simulation results have confirmed that the proposed error correction–based algorithm outperforms the other variety of improved hop‐based algorithms. Results are analyzed in terms of various factors like the localization error, accuracy, and error variance against the parameters of interest taken as node amount, anchor ratio, and communication range of the node.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed (Dec‐hop) Algorithmmentioning

confidence: 99%

Section: Simulation and Comparisonsmentioning

confidence: 99%

Section: Simulation and Comparisonsmentioning

confidence: 99%

Section: Simulation and Comparisonsmentioning

confidence: 99%

See 3 more Smart Citations

Design and analysis of distance error correction–based localization algorithm for wireless sensor networks

Prashar

Jyoti

Kumar

2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

Abnoosian

2021

Concurrency and Computation

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Cloud computing (CC) provides dynamic hiring of server abilities as scalable virtualized services to end-users. However, data center hosting wastes massive amounts of energy resulting in high operational costs and carbon footprints. Also, virtualization is one of CC's main features, and physical resources are delivered by virtual machine (VM).Therefore, in the present article, a new method is provided to improve the VM energy consumption and execution time in the VM migration problem using a hybrid optimization algorithm. Since this issue is one of the famous NP-hard problems, a method is proposed in this article works based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The hybrid algorithm uses a GA to dominate PSO algorithms' constraints, such as weak convergence and stymie in global optima. The CloudSim simulator is employed to show the efficiency of the method compared to others. Using this method will keep the proficiency and power performance of the data centers at the same level. The results showed that energy consumption in the proposed method is better than the other three methods and has been improved by an average of 23.19%. Also, the results showed that execution time is better than the other three methods and has been improved by an average of 29.01%.

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Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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Summary Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.

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Implementation of a PSO Based Improved Localization Algorithm for Wireless Sensor Networks

Cited by 69 publications

References 25 publications

Design and analysis of distance error correction–based localization algorithm for wireless sensor networks

Design and analysis of distance error correction–based localization algorithm for wireless sensor networks

A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

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