Performance of Signal Strength prediction in Data transmission Using Elliott wave Theory

The article describes an algorithm for predicting the future signals with the aid of past signal samples. In the real signal processing environment, the amplitude and unsystematic in phase signal are lead to more complex to estimation the signal, thereby, customer service is enhanced by forecast. The forecast of financial marketplace are usually done by means of Elliot wave theory. In this article possibility and applicability survey of the EW Theory is proposed in the paper towards the power of the signal forecast. In nature, the EW theory has free declining environment, and also uncomfortable based on the customer and base station and height of the antenna. The proposed algorithm has tested in real life conditions, considering both, the pedestrian persons and the people travelling at 60 Km/Hr. Consequently, the predicted result incorporates the practical signal strength based on increasing distribution utility, signal to intervention noise ratio (SNR) and instability at their subsequent time. The end result of the algorithm shows 68% of successful prediction

Resource reservation in wireless networks based on pattern recognition

Resource reservation is very important for handoff control in wireless networks. Many researches have aimed to predict the user's destination cell based on its movement pattern for efficient resource reservation. In the future networks with small size cells, handoffs will occur more frequently and the user's movement will be more like a random process, so it is not practical to predict the accurate destination of a user. We propose a statistical strategy for resource reservation through the estimation of a user's transfer probabilities, which represent the possibilities of the user leaving the current cell and entering the neighboring cells. The resources reserved for a user in each base station are proportional to the user's transfer probabilities. A mathematical model is proposed to obtain the transfer probabilities of a user from the initial states (position, velocity and direction) through simulation of the user's movement. Neural networks are developed to predict the transfer probabilities of a user from the initial states and facilitate efficient resource reservation.published_or_final_versio

A Secure Mobile App Solution Using Human Behavioral Context and Analytic Hierarchy Process

AbstractMobile devices have gained popularity worldwide. The mobile device flexibility has encouraged users to turn their mobile devices into primary hubs for storing information. This paper adopts the classical CW-Lite security models as the framework and the human behavioral patterns as the context. The selected behavioral aspects refer to mobile application and mobility, deployed as major characteristics that determine security control decisions. This proposed paper requires the application of human behavioral context on mobile phones. The solution involves the novel use of behavioral aspects to improve the security of mobile phones. Two important scenarios are incorporated: analytic hierarchy process (AHP) mobile application and AHP mobility. The proposed methodology is superior because it can detect the change in the user behavior in comparison with an intruder. The applied intelligent human behavioral context on the CW-Lite model shows the advantages of AHP in detecting the changes in the user behavior and in authenticating the identity of the main user. These advantages ensure reliability and security of the phone

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A) environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS)/Access Points (APs) in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM) where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities) were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency

Mobility prediction and resource reservation in cellular networks with distributed Markov chains

Abstract-In recent years, mobile hosts desire to benefit a good level of satisfaction for the received services in wireless networks, with adequate quality of service guarantees. In this work the attention is focused on wireless services in cellular networks, where the hand-over effects need to be mitigated, through an appropriate reservation policy. Passive resources are used to ensure service continuity when mobile hosts are moving among different coverage cells. The system is modeled through a distributed set of Hidden Markov Chains and the related theory is used to design a mobility predictor (in terms of future probably visited cells), as the main component of the proposed idea, that does not depend on the considered transmission technology (GSM, UMTS, WLAN, etc.), mobility model or vehicular scenario (urban, suburban, etc.). MRSVP has been used in order to realize the active/passive bandwidth reservation in the considered network topology and a severe simulation campaign has been led-out in order to appreciate the robustness of the reservation scheme

Forecast scheduling for mobile users

International audienceIn future networks, Radio Resource Management (RRM) could benefit from Geo-Localized Measurements (GLM) thanks to the Minimization of Drive Testing (MDT) feature introduced in Long Term Evolution (LTE). Such measurements can be processed by the network and be used to optimize its performance. The purpose of this paper a is to use GLM to significantly improve scheduling. We introduce the concept of forecast scheduler for users in high mobility that exploit GLM. It is assumed that a Radio Environment Map (REM) can provide interpolated Signal to Interference plus Noise Ratio (SINR) values along the user trajectories. The diversity in the mean SINR values of the users during a time interval of several seconds allows to achieve a significant performance gain. The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α−fair utility function of the cumulated rates of the users along their trajectories. Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler. Index Terms—Forecast scheduler, alfa-fair, high mobility, Minimizing Drive Tests, MDT, Radio Environment Maps, REM, geo-localized measurement

On predictive routing of security contexts in an all-IP network

While mobile nodes (MNs) undergo handovers across inter-wireless access networks, their security contexts must be propagated for secure re-establishment of on-going application sessions, such as those in secure mobile internet protocol (IP), authentication, authorization, and accounting (AAA) services. Routing security contexts via an IP network either on-demand or based on MNs' mobility prediction, imposes new challenging requirements of secure cross-handover services and security context management. In this paper, we present a context router (CXR) that manages security contexts in an all-IP network, providing seamless and secure handover services for the mobile users that carry multimedia-access devices. A CXR is responsible for (1) monitoring of MNs' cross-handover, (2) analysis of MNs' movement patterns, and (3) routing of security contexts ahead of MNs' arrival at relevant access points. The predictive routing reduces the delay in the underlying security association that would otherwise fetch an involved security context from a remote server. The predictive routing of security contexts is performed based on statistical learning of MNs' movement pattern, gauging (dis)similarities between the patterns obtained via distance measurements. The CXR has been evaluated with a prototypical implementation based on an MN mobility model on a grid. Our evaluation results support the predictive routing mechanism's improvement in seamless and secure cross-handover services by a factor of 2.5. Also, the prediction mechanism is shown to outperform the Kalman filter-based method [13] as a Kalman Fiter-based mechanism up to 1.5 and 3.6 times regarding prediction accuracy and computation performance, respectively. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65037/1/135_ftp.pd

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms