scholarly journals Particle swarm optimization application for multiple attribute decision making in vertical handover in heterogenous wireless networks

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Mishal Al-Gharabally ◽  
◽  
Ali F. Almutairi ◽  
Ayed A. Salman ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Wireless Networks ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vertical Handover ◽  
Multiple Attribute Decision Making ◽  
Network Selection ◽  
Swarm Optimization ◽  
Multiple Attribute ◽  
Handover Process

In wireless heterogenous networks, mobile terminals are covered by different wireless networks with varying quality of services to ensure the delivery of different classes of services. In this paper, Particle swarm optimization (PSO) was applied to the distance to ideal alternative (DIA) technique in the framework of network selection in a heterogenous wireless network. The PSO was applied to overcome the subjectivity and bias in the weights’ assignment process used in multiple attribute decision making (MADM). The PSO was utilized to optimize the weights of the DIA method through the maximization of the absolute value of the summation of the ranking differences among candidate networks. In this regard, two different optimization functions were introduced and used to generate the optimum weights. The performance of the PSO-based handover for the DIA method was investigated in terms of ranking difference, ranking abnormalities, and network selection. The results show that the proposed PSO-based weights’ assignment technique increased the ranking difference and reduced the ranking abnormalities without degrading the network selection when compared to the conventional DIA technique. The results of this paper are expected to widen the application of the DIA method and other MADM techniques to the handover process in wireless networks and other decision-based challenges in other fields.

scholarly journals A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Shidrokh Goudarzi ◽  
Wan Haslina Hassan ◽  
Mohammad Hossein Anisi ◽  
Seyed Ahmad Soleymani ◽  
Parvaneh Shabanzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Particle Swarm ◽  
Signal Strength ◽  
Pso Algorithm ◽  
Vertical Handover ◽  
Received Signal Strength ◽  
Network Selection ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.

scholarly journals A fuzzy based vertical handover network selection scheme for device-to-device communication

2020 ◽  
Vol 17 (1) ◽  
pp. 324
Author(s):  
Meenakshi Subramani ◽  
Vinoth Babu Kumaravelu
Keyword(s):  
Wireless Networks ◽  
Vertical Handover ◽  
Multiple Attribute Decision Making ◽  
Network Selection ◽  
Selection Scheme ◽  
Handover Decision ◽  
Multiple Attribute ◽  
Device To Device ◽  
Simple Additive Weighting ◽  
The Impact

<p>One of the most attractive and challenging areas in the upcoming next-generation 5G wirelessnetworkistheverticalhandover(VHO).Recently,manyoftheheterogeneous wireless communication technologies are introduced to satisfy the demands of users in all situations. Due to the deployment of heterogeneous networks, the users can access the internet anywhere, anytime through different wireless networks. To obtain seamless service and service continuity, the device should be handed over to the best wireless networks. Here, a half handover scheme for Device-to-Device (D2D) communication is implemented for the selection of the best network. The target network selection for vertical handover can be handled using multiple attribute decision making (MADM) methods. An intelligent and fast vertical handover decision is much needed, which should be reliable even for random and uncertain environments. Fuzzy logic is proved to be effective in handling imprecise data. Hence, in this work, the impact of combining fuzzy with the conventional MADM scheme, simple additive weighting(SAW)isanalyzedandthehybridschemeiscomparedwiththeconventional MADM schemes like SAW, Techniques for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) in terms of handover decision delay. Since, the numbers of handovers executed are low,thehandoverdecisiondelayperformanceoftheproposedschemeissuperiorthan the considered classical MADM schemes.</p>

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

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