Zainab R. Zaidi

Real-time mobility tracking algorithms for cellular networks based on Kalman filtering

We propose two algorithms for real-time tracking of the location and dynamic motion of a mobile station in a cellular network using the pilot signal strengths from neighboring base stations. The underlying mobility model is based on a dynamic linear system driven by a discrete command process that determines the mobile stations acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. The first algorithm consists of an averaging filter for processing pilot signal, strength measurements and two Kalman filters, one to estimate the discrete command process and the other to estimate the mobility state. The second algorithm employs a single Kalman filter without prefiltering and is able to track a mobile station even when a limited set of pilot signal measurements is available. Both of the proposed tracking algorithms can be used to predict future mobility behavior, which can be, useful in resource allocation applications. Our numerical results show that the proposed tracking algorithms perform accurately over a wide range of mobility parameter values.

Mobility estimation for wireless networks based on an autoregressive model

We propose an integrated scheme for estimating the mobility state and model parameters of a user based on a first-order autoregressive model of mobility that accurately captures the characteristics of realistic user movements in wireless networks. Estimation of the mobility parameters is performed by applying the Yule-Walker equations to the training data. Estimation of the mobility state, which consists of the position, velocity, and acceleration of the mobile station is accomplished via an extended Kalman filter using measurements from the wireless network. The integration of mobility state and model parameter estimation results in an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. The mobility estimation scheme can also be used to generate realistic mobility patterns to drive computer simulations of mobile networks. We validate the proposed mobility estimation scheme using mobile trajectories collected from drive-test data obtained from a live cellular network.

Robust mobility tracking for cellular networks

We propose a robust estimation algorithm for tracking the location and dynamic motion of a mobile unit in a cellular network. The underlying mobility model is a dynamic linear system driven by a discrete command process that determines the mobile units acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. Our proposed tracking algorithm is based on a modified Kalman filter in combination with an efficient hidden semi-Markov model (HSMM) estimation algorithm to estimate the parameters of the command process. Numerical results show that the proposed tracking algorithm performs accurately over a wide range of mobility parameter values.

A mobility tracking model for wireless ad hoc networks

We propose a novel scheme for tracking the mobility of users in a wireless ad hoc network. Mobile nodes track their positions using pilot signal strengths from neighboring nodes within a local coordinate system based on relative distances between nodes. Node mobility is modeled as a linear system driven by a discrete command semi-Markov process. Mobility tracking is performed using an extended Kalman filter preceded by an averaging filter. Our numerical results show that the mobility tracking scheme performs effectively and can be used to enhance routing performance in ad hoc networks.

Mobility Tracking Based on Autoregressive Models

We propose an integrated scheme for tracking the mobility of a user based on autoregressive models that accurately capture the characteristics of realistic user movements in wireless networks. The mobility parameters are obtained from training data by computing Minimum Mean Squared Error (MMSE) estimates. Estimation of the mobility state, which incorporates the position, velocity, and acceleration of the mobile station, is accomplished via an extended Kalman filter using signal measurements from the wireless network. By combining mobility parameter and state estimation in an integrated framework, we obtain an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. We consider two variants of an autoregressive mobility model in our study and validate the proposed mobility tracking scheme using mobile trajectories collected from drive test data. Our simulation results validate the accuracy of the proposed tracking scheme even when only a small number of data samples is available for initial training.

A two-tier representation of node mobility in ad hoc networks

We present a two-tier composite model of node mobility that captures group behavior in a mobile ad hoc network. The first tier represents individual node movement and is based on an autoregressive model of mobility. The second tier captures group mobility behavior by considering correlation among node mobility states. Based on the two-tier model, we propose a scheme to detect the presence of groups among the nodes of a network by performing a correlation index test on the node mobility states. We also propose a group-based mobility estimation scheme, which uses the mobility state of a representative node in a group to estimate the mobility states of the rest of the group members. The group estimation scheme can significantly reduce the amount of data collection required to track nodes exhibiting group mobility in mobile ad hoc networks. The two-tier mobility model and the group detection and estimation schemes are validated through simulations and with GPS data.

Experiences in deploying a wireless mesh network testbed for traffic control

Wireless mesh networks (WMN) have attracted considerable interest in recent years as a convenient, flexible and low-cost alternative to wired communication infrastructures in many contexts. However, the great majority of research on metropolitan-scale WMN has been centered around maximization of available bandwidth, suitable for non-real-time applications such as Internet access for the general public. On the other hand, the suitability of WMN for mission-critical infrastructure applications remains by and large unknown, as protocols typically employed in WMN are, for the most part, not designed for real-time communications. In this paper, we describe the Smart Transport and Roads Communications (STaRComm) project at National ICT Australia (NICTA), which sets a goal of designing a wireless mesh network architecture to solve the communication needs of the traffic control system in Sydney, Australia. This system, known as SCATS (Sydney Coordinated Adaptive Traffic System)and used in over 100 cities around the world, connects a hierarchy of several thousand devices -- from individual traffic light controllers to regional computers and the central Traffic Management Centre (TMC)-- and places stringent requirements on the reliability and latency of the data exchanges. We discuss our experience in the deployment of an initial testbed consisting of 7 mesh nodes placed at intersections with traffic lights, and share the results and insights learned from our measurements and initial trials in the process.

The Use of Simulation Models in Model Driven Experimentation

Abstract : In model driven or model based experimentation, the model of the experiment is a key component of the closed loop model of the process. The model is created through interaction with the team designing the experimental organizations as well as the team creating the experimental environment. Starting with preliminary descriptions, the model evolves as more specific details are available and influences the final experimental design. The methodology used to design the model reflects both the types of design information available and the underlying hypothesis of the experiment. Experiments validating fixed types of structures or processes lead to a model designed with a Structured Analysis Design Technique which leads to an explicit but rigid model design. Experiments investigating adaptation require a more flexible model which can be created using an Object Oriented design approach. This leads to a more flexible, object view of the experimental design. Either approach leads to an appropriate set of models from which an executable model can be derived. The executable model is used to carry out simulations In order to analyze the dynamic behavior of the model, an input scenario must be created based on the actual inputs that will be used in the experimental setting. When the model is stimulated with the scenario, its behavior can be observed and its performance measured on different criteria. Because it is a computer simulation, input parameters can be varied, constraints can be relaxed, and other variables (possibly) affecting the hypotheses can be explored to see their effect on the model and by inference the experiment. These results can then be made available to the design teams to influence further iterations of the design. Indeed, the model allows the consideration of many excursions, a situation that is not possible when the experiments include teams of humans.

Detection and Identification of Anomalies in Wireless Mesh Networks Using Principal Component Analysis (PCA)

Anomaly detection is becoming a powerful and necessary component as wireless networks gain popularity. In this paper, we evaluate the efficacy of PCA based anomaly detection for wireless mesh networks. PCA was originally developed for wired networks. Our experiments show that it is possible to detect different types of anomalies in an interference prone wireless environment. However, the sensitivity of PCA to small changes in flows prompted us to develop an anomaly identification scheme which automatically identifies the flow(s) causing the detected anomaly and their contributions in terms of number of packets. Our results show that the identification scheme is able to differentiate false alarms from real anomalies and pinpoint the culprit(s) in case of a real fault or threat. The experiments were performed over an 8 node mesh testbed deployed in an urban street layout in Sydney, under different realistic traffic scenarios. Our identification scheme facilitates the use of PCA based method for real-time anomaly detection in wireless networks as it can filter the false alarms locally at the monitoring nodes without excessive computational overhead.

Monitoring assisted robust routing in wireless mesh networks

In this paper, we present a monitoring assisted robust routing scheme for wireless mesh networks which exploits the broadcast nature of wireless transmissions at special routers with added monitoring functionalities. These routers passively listen to the transmissions in their neighborhood and compare the routing behavior against the routing state collectively maintained by them. If any inconsistency is found, as a result of software/hardware malfunction, these routers try to determine the node causing it and recover from it in a timely fashion. The scheme is developed for wireless mesh networks where the communication overhead is a critical issue. The performance evaluation of our scheme shows considerable improvement in reliability (i.e., delivery ratio achieved by standard routing protocols) with minimal overhead under situations of malfunctions.