Shidrokh Goudarzi

A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event’s location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments.

A Novel Method to Water Level Prediction using RBF and FFA

Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF-FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF–FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models’ 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 show that the developed RBF–FFA model provides more precise predictions compared to different ANNs, namely support vector machine (SVM) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real time water stage measurements. The results specify that the developed RBF–FFA model can be used as an efficient technique for accurate prediction of water stage of river.

BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

Beacon rate adaption is a way to cope with congestion of the wireless link and it consequently decreases the beacon drop rate and the inaccuracy of information of each vehicle in the network. In a vehicular environment, the beacon rate adjustment is strongly dependent on the traffic condition. Due to this, we firstly propose a new model to detect traffic density based on the vehicle’s own status and the surrounding vehicle’s status. We also develop a model based on fuzzy logic namely the BRAIN-F, to adjust the frequency of beaconing. This model depends on three parameters including traffic density, vehicle status and location status. Channel congestion and information accuracy are considered the main criteria to evaluate the performance of BRAIN-F under both LOS and NLOS. Simulation results demonstrate that the BRAIN-F not only reduces the congestion of the wireless link but it also increases the information accuracy.

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

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.

ABC-PSO for vertical handover in heterogeneous wireless networks

Cloud computing is currently emerging quickly as a client-server technology structure and, currently, providing distributed service applications. However, given the availability of a diverse range of wireless access technologies, people expect continuous connection to the most suitable technology that matches price affordability and performance goals. Among the main challenges of modern communication is the accessibility to wireless networks using mobile devices, with a high service quality (QoS) based on preferences of the users. Past literatures contain several heuristic approaches that use simplified rules to look for the best network that is available. Nevertheless, attributes of mobile devices need algorithms that are quick and effective in order to select best available network near real-time. This study proposes a hybrid intelligent handover decision algorithm primarily founded on two main heuristic algorithms: Artificial Bee Colony or ABC as well as Particle Swarm Optimization or PSO named ABC-PSO to select best wireless network during vertical handover process. The ABC-PSO algorithm has been optimized to achieve small cost function that are powered using the IEEE 802.21 standard taking into account different available wireless networks, the application requirements and the user preferences to improve QoS. Numerical results demonstrate that the ABC-PSO algorithm compared to the related work has lower cost and delay, higher available bandwidth and less number of handover.

Artificial bee colony for vertical-handover in heterogeneous wireless networks

Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, an artificial bee colony (ABC) algorithm for real-time vertical handover using different objective function has been presented to find the optimal network to connect. It can select an optimal set of weights for specified values, and find the optimal network selection solution. Simulation results illustrate that the proposed ABC algorithm has better performances than the existing methods in many evaluating parameters, and the computational time is also minimized.

Mobility-aware medium access control protocols for wireless sensor networks: A survey

Abstract The popularity of wireless sensor networks has grown rapidly in recent years, with new directions including healthcare monitoring and disaster response. This increased use in mobile applications has naturally led to new challenges to the design of sensor protocols, especially in the media access control (MAC) sublayer. In order to design a MAC protocol which takes mobility awareness into account, understanding how mobility can be described by mobility models is crucial. Moreover, for applications that transform between static, periodic-mobile, random-mobile, or variable number of nodes, flexibility in design is a key consideration. Therefore, in this paper mobility pattern, mobility models and mobility estimation algorithms for wireless sensor networks are discussed. The state of the art of medium access control protocols with mobility-handling capabilities is overviewed and a comparative study of the most well-known mobility aware MAC protocols is given. Finally, future research directions for mobility-aware MAC protocols are presented. We believe this paper will aid researchers by serving as a reference to orient future research in this area.

A Study towards the Relation of Customer Relationship Management Customer Benefits and Customer Satisfaction

The purpose of this study is to investigate the efficacy of customer relationship management (CRM) benefits for customers in relation to customer satisfaction. A model has been developed and empirically tested through survey data collected from 150 customers of three Malaysian companies. The results indicate that the benefits of CRM for customers have had a significant positive effect on their satisfaction in marketing companies. Personalized service, responsiveness to customers needs, customer segmentation, customization of marketing, multichannel integration, time-saving and improving customer knowledge are the benefits that we proposed would affect customer satisfaction in order to significantly improve marketing performance. Additionally, the results reveal that all the benefits found, with the exception of time-saving, enhanced customer satisfaction. This paper contributes to the existing literature by incorporating the benefits of CRM for customers and the relationships of these benefits with their satisfaction in the proposed model.

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

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.