Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

A hybrid intelligent model for network selection in the industrial Internet of Things

Industrial Internet of Things (IIoT) plays an important role in increasing productivity and efficiency in heterogeneous wireless networks. However, different domains such as industrial wireless scenarios, small cell domains and vehicular ad hoc networks (VANET) require an efficient machine learning/intelligent algorithm to process the vertical handover decision that can maintain mobile terminals (MTs) in the preferable networks for a sufficient duration of time. The preferred quality of service parameters can be differentiated from all the other MTs. Hence, in this paper, the problem with the vertical handoff (VHO) decision is articulated as the process of the Markov decision aimed to maximize the anticipated total rewards as well as to minimize the handoffs’ average count. A rewards function is designed to evaluate the QoS at the point of when the connections take place, as that is where the policy decision for a stationary deterministic handoff can be established. The proposed hybrid model merges the biogeography-based optimization (BBO) with the Markov decision process (MDP). The MDP is utilized to establish the radio access technology (RAT) selection’s probability that behaves as an input to the BBO process. Therefore, the BBO determines the best RAT using the described multi-point algorithm in the heterogeneous network. The numerical findings display the superiority of this paper’s proposed schemes in comparison with other available algorithms. The findings shown that the MDP-BBO algorithm is able to outperform other algorithms in terms of number of handoffs, bandwidth availability, and decision delays. Our algorithm displayed better expected total rewards as well as a reduced average account of handoffs compared to current approaches. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model

Case study on handoff strategies for wireless overlay networks

Ceken, Celal/0000-0002-6912-0057;WOS: 000311525100014One of the most challenging topics for next-generation wireless networks is the process of vertical handoff since many of wireless technologies overlap each other and build a heterogeneous topology. Several parameters, pertaining to user/application requirements and network conditions, such as data rate, service cost, network latency, speed of mobile, and etc. must be considered in the handoff process of heterogeneous networks along with RSSI information. In this paper, adaptive fuzzy logic-based vertical handoff decision-making algorithms are presented for wireless overlay networks which consist of GSM/GPRS/Wi-Fi/IJMTS/WiMAX technologies. The parameters data rate, monetary cost, speed of mobile and RSSI information are processed as inputs of the proposed fuzzy-based systems. According to these parameters, an output value, which varies between 1 and 10, is produced. This output value is utilized to determine whether a handoff process is necessary or not and to select the best candidate access point in the vicinity. The results show that, compared to the traditional RSSI-based algorithm significantly enhanced outcomes can be achieved for both user and network as a consequence of the proposed fuzzy-based handoff systems. The simulation results are also compared with those of classical MADM (Multiple Attribute Decision Making) method, i.e. SAW (Simple Additive Weighting). According to the results obtained, the proposed vertical handoff decision algorithms are able to determine whether a handoff is necessary or not, properly, and select the best candidate access network considering the aforementioned parameters. Moreover, fuzzy-based algorithm noticeably reduces the number of handoffs compared to SAW-based algorithm. (C) 2012 Elsevier B.V. All rights reserved