Controlling the handover mechanism in wireless mobile nodes using game theory

This paper proposes a novel network selection mechanism as an extension to the FMIPv6 [2] protocol, which improves handover latency in the MIPv6 [1] in the case where the Mobile Nodes (MN) have a single wireless interface with multiple Care-of-Addresses (CoA’s). Moreover, this paper proposes a novel interface/network selection mechanism, which is an extension to the MFMIPv6 [5], which work when the mobile node has more than one wireless interface. Generally, the previous access router (PAR) in the FMIPv6 protocol forwards all the arrived packets to the new access router (NAR) by setting up a tunnel to it in order to prevent packet losses incurred by latency during handover procedure. However, there is no protocol which offers the user and/or the application to dynamically choose the right NAR (i.e. the one offers a better service). What’s more, one of the main objectives of the next generation networks will be heterogeneity in the wireless access environment in which a mobile terminal will be able to connect to multiple radio networks simultaneously. For these reasons, network selection and efficient load balancing mechanisms among different networks will be required to achieve high-speed connectivity with seamless mobility. To this end; Game Theory [3], naturally becomes a useful and powerful tool to research this kind of problems. Game theory is a mathematical tool developed to understand competitive situations in which rational decision makers interact to achieve their objectives. The mechanism improves the handover latency, the user ability to choose the right interface/network and controls when to force the MN to make the handover

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Design of planar inverted-F antennas (PIFA) for multiband wireless applications

A small three bands printed inverted-F antenna with independently controlling the resonant frequency is presented. The proposed antenna consists of two arms supported by shorting walls fed by 50 Ω microstrip transmission line and a ground plane. The antenna occupied a compact size of 26 x 25 x 3.75 mm. The main radiated patch injected with slot and another arm to generate and control the three resonant frequencies to cover 2.4, 3.7 and 5.2GHz Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX). The simulated and measured results show that the antenna achieves a gain of 2, 3 and 5 dBi respectively and radiation efficiency of 50%, 60% and 85% for the three bands respectively. The simulated and measured result for the return loss is in good agreements

Multi-band antenna for different wireless applications

A small multi-band compact antenna is presented. The antenna is designed on Roger RT/duroid 5880 with dielectric constant 2.2, multi-band operations is achieved by inserting a slot on the top patch. The antenna has wide impedance bandwidth at 1.2, 1.6, 2.4 and 2.6 GHz with Gain 4.2, 1, 5 and 2 dBi respectively. The bandwidth before adding the shorting wall and the slot was 3.72%, whereas after adding the shorting wall and the slot the bandwidth get wider to 31.9% at the centre of 1.4 GHz. The radiation pattern has acceptable response with low cross polarization at both E-plane and H-plane. The overall dimension of the ground plane is 70 X 70 X 1.5 m

Bandwidth enhancement for small patch antenna using PBG structure for different wireless applications

A design strategy using Photonic Band Gap (PBG) structure on ground plane to achieve wider bandwidth for patch antenna is presented. It is found that, the impedance bandwidth has improved from 3.72% to 31.9% at centre frequency 9 GHz after adding PBG on the ground plane. The antenna has multi band operations at 5, 6 and 9 GHz. E-Plane and H-plane radiation patter is satisfied within this band

Bandwidth enhancement for patch antenna using PBG slot structure for 5, 6 and 9 GHz applications

A design strategy using photonic band gap (PBG) structure on ground plane to achieve wider bandwidth for patch antenna is presented. It is found that, the impedance bandwidth has improved from 3.72% to 31.9% at centre frequency 9 GHz after adding PBG on the ground plane. The antenna has multi band operations at 5, 6 and 9 GHz. E-Plane and H-plane radiation patter is satisfied within this bands

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area