Wan Haslina Hassan

A tracking mechanism using agents for personal mobility in IP networks

This paper presents our ongoing work in developing an agent architecture to support personal mobility in IP networks. In particular, the paper discusses a tracking mechanism that facilitates location management using agents where both host and user mobility is accorded by the network. Towards this end, a multi-layered agent architecture is proposed and this acts as an overlay providing intelligence in an IP-based network. The agent messaging and collaboration provide the necessary signaling and control mechanism for tracking user movement across multiple domains. This represents a departure from traditional methods of providing mobility services where intelligence is normally restricted to end systems. Ultimately, the role of the collaborative agent architecture is to provide an IP-based network with the ability to exhibit dasiaintelligentpsila behaviour i.e. awareness, dynamism and adaptation, in terms of user movement and preferences.

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.

Feasibility of Implementing Multi-factor Authentication Schemes in Mobile Cloud Computing

Mobile cloud computing is a new computing technology, which provides on-demand resources. Nowadays, this computing paradigm is becoming one the most interesting technology for IT enterprises. The idea of computing and offloading data in cloud computing is utilized to overcome the inherent challenges in mobile computing. This is carried out by utilizing other resource providers besides the mobile device to host the delivery of mobile applications. However, this technology introduces some opportunities as new computing concept, several challenges, including security and privacy are raised from the adoption of this IT paradigm. Authentication plays an important role to mitigate security and privacy issue in the mobile cloud computing. Even some authentication algorithms are proposed for mobile cloud computing, but most of these algorithms designed for traditional computing models, and are not using cloud capabilities. In mobile cloud computing, we access to pooled computation resources and applying more complicated authentication schemes is possible. Using different authentication factors, which is called multi-factor authentication algorithms, has been proposed for various areas. In this paper, feasibility of implementation of different kinds of multi-factor authentication protocols are discussed. Furthermore, the security and privacy of these algorithms are analyzed. Finally, some future directions are recommended.

A review of VHD approaches in next generation wireless networks

One of the most desirable features of next generation wireless networks is the ability to move seamlessly over different access networks regardless of the underlying infrastructure. Handovers between dissimilar networks may be facilitated by the use of an efficient vertical handover algorithm. This paper explores ongoing research on the development of vertical handover decision methods and their effectiveness. Our preliminary findings indicate that simple algorithms based on RSS values may facilitate vertical handovers with small delays but generally at a lower rate of throughput. Complex algorithms provide significant improvements in throughput but at a cost of higher delays. As such, there is a need for continued research to develop well-optimized algorithms that are able to yield high throughput while minimizing delays and lowering signaling cost especially in coping with the demands of real-time applications in next generation wireless networks.

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.

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.

An Agent-Based Mobility Protocol for Improved Mobile QoS in Next Generation IP Networks

This paper extends our work in developing an agent-based architecture for improved mobile QoS in next generation IP networks. Here the performance of the protocol, called AMP, is evaluated against the IETF’s standard Mobile IP protocol in supporting a variety of traffic. The analyses were done using the network simulator ns2 where specialized nodes were developed as part of the protocol. The parameters studied were packet loss and delay. Results from simulations indicate that AMP outperformed Mobile IP especially during handovers in terms of lower packet delay and packet loss under CBR, VBR and bursty traffic conditions. We conclude with a discussion on further work for the proposed architecture.

Optimized routing algorithm for mobile multicast source in wireless mesh networks

Mobility considerations in multicast algorithm design usually focus issues of mobile receivers while few researches emphasize on mobile multicast sources issues. However, mobility of source is a critical issue causing global tree disruption. Furthermore, major research challenges in the delivery of group applications over Wireless Mesh Networks (WMN) comprise delivery of large volume data, incurring large bandwidth and delay. This is in addition to multicast source mobility issues and interfering nature of scarce wireless resources. Mobile multicast scenarios emerge in group-based distribution of information such as obtains in the health sector, disaster recovery operation, remote bus, train control and video surveillance. The mobile multicast problem in this paper is formulated as a bandwidth and delay constrained minimum tree cost problem which is proved to be computationally intensive. Thus this paper proposes a Differential Evolution based optimized mobile multicast routing algorithm for the shared tree architecture. The proposed algorithm is implemented using MATLAB and the evaluation considers analytic and simulation techniques for convergence, scalability and multicast tree stability under source movement. Simulation results show that the proposed algorithm converges and outperforms a source based tree algorithm.

Evaluation of Nested Network Mobility Approaches

Recently, wireless devices have been used significantly due to the continuous and enormous Services that they provide. One of the major challenges that faces is how to achieve high network performance. The Internet Engineering Task Force (IETF) has developed Mobile IP, which allows Mobile node to be connected while it is moving as well as functioning in foreign mobile network zone using its original IP address. IETF also in order to manage mobility of an entire network as single unit, they developed NEMO Basic Support Protocol (NEMO BS), which allows Mobile Router to move and change their point of attachment. When a Mobile Router connected to other mobile router on foreign mobile network, it makes a hierarchical structure known as Nested Mobile Network. When the level of nesting becomes high, problems of tunneling overhead, handoff latency, packet losses and complexity are increased which will cause inefficiency and bad Performance. The purpose of this paper is an attempt to evaluate the current approaches and the proposed solutions that dealt with these issues.

Investigation of Gateway Placement Optimization Approaches in Wireless Mesh Networks Using Genetic Algorithms

Recently wireless mesh networks (WMNs) gained significant roles in the current communication technologies and have been used in numerous applications such as transportation systems, rescue systems, Surveillance systems, community and neighborhood networking and etc. Therefore, many researchers pay their attention to the wireless mesh network issues especially the gateway placement optimization problems. In this paper, we study and investigate the efforts of many researchers that dealt with the gateway placement optimization problem based on combinatorial optimization concepts in comparison with other conventional algorithms as well as comparing the combinatorial based algorithms with each other. The investigation result shows that the genetic algorithms based approaches on solving gateway optimization problem relatively outperform many other approaches in addition to that the strength of the genetic algorithm depends on the fitness function which is used in measuring the quality of the individuals (fitness value).