User Movement Patterns Research Articles

A Hidden Genetic Layer Based Neural Network for Mobility Prediction

Problem statement: With numerous wireless devices increasingly connecting to the internet, WLAN infrastructure planning becomes very important to maintain desired quality of services. For maintaining desired quality of service it is desirable to know the movement pattern of users. Mobility prediction involves finding the mobile device's next access point as it moves through the wireless network. Hidden Markov models and Bayesian approach have been proposed to predict the next hop. Approach: In this study we propose a new method for feature extraction and propose a novel neural network classifier based on a hidden Genetic Algorithm layer-GA-SOFM Neural Network. We evaluate our hypotheses by using one month syslog data of Dartmouth college mobility traces available online to extract mobility features and use this features to find the classification accuracy of the proposed model. Results and Conclusion: Proposed methodology was implemented and obtained accuracy in mobility prediction was 83.68%. The output obtained is better than Naive Bayesian method by 15.56% and over CART by 11.94%.

Changing Trends in Modeling Mobility

A phenomenal increase in the number of wireless devices has led to the evolution of several interesting and challenging research problems in opportunistic networks. For example, the random waypoint mobility model, an early, popular effort to model mobility, involves generating random movement patterns. Previous research efforts, however, validate that movement patterns are not random; instead, human mobility is predictable to some extent. Since the performance of a routing protocol in an opportunistic network is greatly improved if the movement patterns of mobile users can be somewhat predicted in advance, several research attempts have been made to understand human mobility. The solutions developed use our understanding of movement patterns to predict the future contact probability for mobile nodes. In this work, we summarize the changing trends in modeling human mobility as random movements to the current research efforts that model human walks in a more predictable manner. Mobility patterns significantly affect the performance of a routing protocol. Thus, the changing trend in modeling mobility has led to several changes in developing routing protocols for opportunistic networks. For example, the simplest opportunistic routing protocol forwards a received packet to a randomly selected neighbor. With predictable mobility, however, routing protocols can use the expected contact information between a pair of mobile nodes in making forwarding decisions. In this work, we also describe the previous and current research efforts in developing routing protocols for opportunistic networks.

Fluid Flow and Random Walk Mobility Model for Wireless Mobile Network Research: A Review

In performance evaluation of mobility management protocols for Wireless Mobile Networks (WMN) estimating cell change rate of mobile users is very important. Based on the rate of cell change (also called handoff rate) other parameters such as location update cost, handoff latency etc. may be calculated. Moreover, any new mobility management protocol must be examined either mathematically or through simulation before its use in real network. Since the cell change rate is dependent on movement pattern of users; that is why modeling of user movement in order to evaluate the performance of protocols and architectures for WMN plays a vital role. The aim of this paper is to discuss Fluid Flow and Random Walk mobility and different method to estimate cell changing rate using these two models. Our intention is to support researchers with options to choose a suitable model when their protocol or architecture needs to be studied under various mobility patterns.

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.

Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.

BIO-INSPIRED ALGORITHMS FOR MOBILITY MANAGEMENT

This work reviews a number of bio-inspired algorithms and examines their suitability for solving the location management problem by using the paging cell and location area schemes. A comparison is performed using three test networks using realistic user movement patterns. The results provide new insight into the mobility management problem that can influence the design of future wireless networks.

Effects of Text Saliency on Eye Movements while Browsing a Web Portal

This study investigates the eye movement patterns of users viewing a portal web page. Previous research has shown that users scan a portal page in a top-to-bottom manner by row. In this study, the saliency of one of the portal channel titles was manipulated to see what impact this would have on the users' scan pattern. The saliency of the channel title was manipulated in two different page locations by modifying the color the text. Results indicate that channel location was found to be a stronger determinant of where users fixate and that the saliency of the channel title is a secondary determinant. Overall, users first fixated the portal page in the top, center channel regardless of the channel title color. Subsequent eye movements appear to be impacted by the salient title, but only when it was located on the left side of the page. Implications of these results to portal webpage design are discussed.

A Genetic Algorithm Method for Optical Wireless Channel Control

A genetic algorithm controlled multispot transmitter is proposed as an alternative approach to optimizing the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. By specifically tailoring the algorithm, it is shown that by dynamically altering the intensity of individual diffusion spots, a consistent power distribution, with negligible impact on bandwidth and rms delay spread, can be created in multiple rooms independent of reflectivity characteristics and user movement patterns. This advantageous adaptability removes the need for bespoke system design, aiming instead for the use of a more cost effective, optimal transmitter and receiver capable of deployment in multiple scenarios and applications. From the simulations conducted it is deduced, that implementing a receiver with a FOV=55deg in conjunction with either of two notable algorithms, the dynamic range of the rooms, referenced against the peak received power, can be reduced by up to 26% when empty, and furthermore to within 12% of this optimized case when user movement perturbs the channel.

Autonomic behaviour of opportunistic network routing

In opportunistic networks, end-to-end communication among users does not require a continuous end-to-end path between source and destination. Network protocols are designed to be extremely resilient to events such as long partitions, node disconnections, etc. which are very features of this type of self-organising ad hoc networks. This is achieved by temporarily storing messages at intermediate nodes, waiting for future opportunities to forward them towards the destination. Clearly, designing routing and forwarding schemes is one of the main challenges in this environment. In this article, we provide a survey of the main approaches to routing in purely infrastructure-less opportunistic networks, by classifying protocols based on the amount of context information they exploit. Then, we provide an extensive quantitative comparison between representatives of protocols that do not use any context information, and protocols that manage and exploit a rich set of context information. Mainly, we focus on the suitability of protocols to adapt to the dynamically changing network features, as resulting from the user movement patterns that are driven by their social behaviour. Our results show that context aware routing is extremely adaptive to dynamic networking scenarios, and, with respect to protocols that do not use any context information, is able to provide similar performance in terms of delay and loss rate, by using just a small fraction of the network resources.

Sociological orbit aware location approximation and routing (SOLAR) in MANET

Mobility affects routing protocol performance in a Mobile Ad Hoc NETwork (MANET). This paper introduces a novel concept of “macro-mobility” information obtained from the sociological movement pattern of MANET users, and proposes a routing protocol that can take advantage of the macro-mobility information. This macro-mobility information is extracted from our observation that the movement of a mobile user exhibits a partially repetitive “orbital” pattern involving a set of “hubs”. This partially deterministic movement pattern is not only practical, but also useful for locating nodes without the need for constant tracking and for routing packets to them without flooding. More specifically, this paper makes the following two contributions. First, it proposes an ORBIT mobility framework to achieve this macro-level abstraction of orbital movement. Second, to take advantage of this hub-based orbital pattern, it proposes a Sociological Orbit aware Location Approximation and Routing (SOLAR) protocol. Extensive performance analysis shows that SOLAR significantly outperforms conventional routing protocols like Dynamic Source Routing (DSR) and Location Aided Routing (LAR) in terms of higher data throughput, lower control overhead, and lower end-to-end delay.

Modeling frame synchronization for umts high-speed downlink packet access

Third Generation Partnership Project (3GPP) specification TR 25.950 proposed high-speed downlink packet access for the Universal Mobile Telecommunication System (UMTS). In this mechanism, an active set of cells is defined for every user equipment (UE) communication session. The cell with the best wireless link quality (called the serving cell) in the active set is selected for communication with the UE. When the wireless link quality of the old serving cell degrades below some threshold, a new serving cell in the active set is selected to continue the communication session. Our previous work proposed a high-speed downlink packet access (HSDPA) overflow control scheme with four frame synchronization algorithms to switch the serving cell, and formally proved the correctness of the scheme. We propose an analytic model to investigate the performance of these frame synchronization algorithms, and show how the user movement patterns affect the control message delivery costs of these algorithms.

Adaptive region-based location management for PCS systems

The personal communications services (PCSs) systems can provide ubiquitous and customized services. The key issue, which affects the performance of the whole system, is the location management. We propose a region-based location strategy by taking advantage of the user's movement behavior to improve the performance of the conventional systems. Each mobile user is associated with a set of regions, which are derived from the user's movement patterns. The registration processes in the same region can be eliminated such that the cost of location management can be significantly reduced. Several design issues are studied by considering the workload balance and the call-to-mobility ratio for a user. The proposed strategy can be dynamically adjusted based on different system parameters and user behavior. A performance analysis on the signaling cost and the database access cost is given to justify the benefits of this approach.

Mobility modeling in wireless networks

The movement pattern of mobile users plays an important role in performance analysis of wireless computer and communication networks. In this paper, we first give an overview and classification of mobility models used for simulation-based studies. Then, we present an enhanced random mobility model, which makes the movement trace of mobile stations more realistic than common approaches for random mobility. Our movement concept is based on random processes for speed and direction control in which the new values are correlated to previous ones. Upon a speed change event, a new target speed is chosen, and an acceleration is set to achieve this target speed. The principles for direction changes are similar. Finally, we discuss strategies for the stations' border behavior (i.e., what happens when nodes move out of the simulation area) and show the effects of certain border behaviors and mobility models on the spatial user distribution.

Restoration scheme of mobility databases by mobility learning and prediction in PCS networks

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in personal communication systems (PCSs). In PCSs, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the malfunction of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the malfunction of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a neuro-fuzzy inference system (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using a checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme.

Simulation of a Mobility Prediction Scheme Based on Neuro-Fuzzy Theory in Mobile Computing

Mobility prediction is an important issue for efficient management in mobile computing. Whenever users move to a new location, mobile systems should know about their location change so that the connection is not in terrupted. In this paper, we focus on the simulation of a user mobility prediction scheme based on neuro-fuzzy theory. Our scheme predicts future locations from the movement patterns of users accumulated over several days or months, i.e., the user's location history. In or der to model users' movements, we use a neuro-fuzzy inference system. Whenever a user moves to a new lo cation, the system autonomously learns various move ment patterns. The prediction of the future location to which users will move is obtained by the approximate reasoning of the neuro-fuzzy inference system. This ap proach can derive an appropriate future location from learned location information, even if the mobile systems do not know the actual location history. Simulation re sults show that our prediction scheme has high accuracy for various users' patterns. In addition, our scheme is applicable, with considerable prediction accuracy, to users whose history has not been learned by the neuro- fuzzy inference system.

Performance modeling of location tracking systems

A location system utilizes sensors to track the locations of mobile users with badges. A badge periodically transmits location messages to the sensor in the room. Based on a location message, the system updates or re-confirms the position of the badge. A major performance issue of the location system is the determination of the frequency that a badge transmits the location messages (or the registration message). In this paper, we propose an analytic model to study this performance issue. Our study indicates that in many cases, by decreasing the registration frequency (and consequently saving battery power), the location system may still maintain low lost-call probability. We also observe an interesting phenomenon that if the user movement pattern is highly irregular, the opportunity of losing a call becomes smaller.