Kun Chan Lan

Rapid Generation of Realistic Mobility Models for VANET

One emerging, new type of ad-hoc network is the vehicular ad-hoc network (VANET), in which vehicles constitute the mobile nodes in the network. Due to the prohibitive cost of deploying and implementing such a system in real world, most research in VANET relies on simulations for evaluation. A key component for VANET simulations is a realistic vehicular mobility model that ensures conclusions drawn from simulation experiments will carry through to real deployments. In this work, we introduce a tool MOVE that allows users to rapidly generate realistic mobility models for VANET simulations. MOVE is built on top of an open source micro-traffic simulator SUMO. The output of MOVE is a realistic mobility model and can be immediately used by popular network simulators such as ns-2 and qualnet. We evaluate and compare ad-hoc routing performance for vehicular nodes using MOVE to that using the random waypoint model. We show that the simulation results obtained when nodes moving according to a realistic mobility model is significantly different from that of the commonly used random waypoint model.

A Survey of Opportunistic Networks

We define an opportunistic network as one type of challenged networks where network contacts are intermittent or where link performance is highly variable or extreme. In such a network, there does not exist a complete path from source to destination for most of the time. In addition, the path can be highly unstable and may change or break quickly. Therefore, in order to make communication possible in an opportunistic network, the intermediate nodes may take custody of data during the blackout and forward it when the connectivity resumes. In this paper, we discuss some research challenges in an opportunistic network.

Measuring and Improving the Performance of Network Mobility Management in IPv6 Networks

Measuring the performance of an implementation of a set of protocols and analyzing the results is crucial to understanding the performance and limitations of the protocols in a real network environment. Based on this information, the protocols and their interactions can be improved to enhance the performance of the whole system. To this end, we have developed a network mobility testbed and implemented the network mobility (NEMO) basic support protocol and have identified problems in the architecture which affect the handoff and routing performance. To address the identified handoff performance issues, we have proposed the use of make-before-break handoffs with two network interfaces for NEMO. We have carried out a comparison study of handoffs with NEMO and have shown that the proposed scheme provides near-optimal performance. Further, we have extended a previously proposed route optimization (RO) scheme, OptiNets. We have compared the routing and header overheads using experiments and analysis and shown that the use of the extended OptiNets scheme reduces these overheads of NEMO to a level comparable with Mobile IPv6 RO. Finally, this paper shows that the proposed handoff and RO schemes enable NEMO protocol to be used in applications sensitive to delay and packet loss

Realistic mobility models for Vehicular Ad hoc Network (VANET) simulations

Vehicular ad-hoc network (VANET) is surging in popularity, in which vehicles constitute the mobile nodes in the network. Due to the prohibitive cost of deploying and implementing such a system in real world, most research in VANET relies on simulations for evaluation. A key component for VANET simulations is a realistic vehicular mobility model that ensures that conclusions drawn from simulation experiments will carry through to real deployments. In this work, we first introduce a tool MOVE that allows users to rapidly generate realistic mobility models for VANET simulations. MOVE is built on top of an open source micro-traffic simulator SUMO. The output of MOVE is a realistic mobility model and can be immediately used by popular network simulators such as ns-2 and qualnet. We evaluate the effects of details of mobility models in three case studies of VANET simulations (specifically, the existence of traffic lights, driver route choice and car overtaking behavior) and show that selecting sufficient level of details in the simulation is critical for VANET protocol design.

A Feasibility Study on Vehicle-to-Infrastructure Communication: WiFi vs. WiMAX

Vehicular Network is becoming increasingly popular in recent years, in which vehicles constitute a wireless mobile network. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are two different modes of communication in a vehicular network. Some measurement studies have previously been undertaken to understand the feasibility of using WiFi for V2V and V2I communication. Recently WiMAX is emerging as one of the possible candidates for next generation mobile networks. In this work, we set out to understand the feasibility of using WiMAX for V2I communication as compared to the use of WiFi. Due to the hardware limitation, we focus on a static setting in urban environment. Our initial measurement studies show that while WiMAX can offer a longer communication range than WiFi, its latency can be significantly larger than that of WiFi at a short distance (e.g. less than 100m). In addition, we show the setting of frame size has a strong impact on the performance of WiMAX.

Adaptive Position Update in Geographic Routing

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbours for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbour positions. We contend that periodic beaconing regardless of network mobility and traffic pattern does not make optimal ulilisation of the wireless medium and node energy. For example, if the beacon interval is too small compared to the rate at which a node changes its current position, periodic beaconing will create many redundant position updates. Similarly, when only a few nodes in a large network are involved in data forwarding, resources spent by all other nodes in maintaining their neighbour positions are greatly wasted. To address these problems, we propose the Adaptive Position Update (APU) strategy for geographic routing. Based on mobility prediction, APU enables nodes to update their position adaptively to the node mobility and traffic pattern. We embed APU into the well known Greedy Perimeter Stateless Routing Protocol (GPSR), and compare it with original GPSR in the ns-2 simulation platform. We conducted several experiments with randomly generated network topologies and mobility patterns. The results confirm that APU significantly reduces beacon overhead without having any noticeable impact on the data throughput of the network. This result is further validated through a trace driven simulation of a practical vehicular ad-hoc network topology that exhibits realistic movement patterns of public transport buses in a metropolitan city.

Using Smart-Phones and Floor Plans for Indoor Location Tracking

We implement pedestrian dead reckoning (PDR) for indoor localization. With a waist-mounted PDR based system on a smart-phone, we estimate the users step length that utilizes the height change of the waist based on the Pythagorean Theorem. We propose a zero velocity update (ZUPT) method to address sensor drift error: Simple harmonic motion and a low-pass filtering mechanism combined with the analysis of gait characteristics. This method does not require training to develop the step length model. Exploiting the geometric similarity between the user trajectory and the floor map, our map matching algorithm includes three different filters to calibrate the direction errors from the gyro using building floor plans. A sliding-window-based algorithm detects corners. The system achieved 98% accuracy in estimating user walking distance with a waist-mounted phone and 97% accuracy when the phone is in the users pocket. ZUPT improves sensor drift error (the accuracy drops from 98% to 84% without ZUPT) using 8 Hz as the cut-off frequency to filter out sensor noise. Corner length impacted the corner detection algorithm. In our experiments, the overall location error is about 0.48 meter.

Generation of high bandwidth network traffic traces

High bandwidth network traffic traces are needed to understand the behavior of high speed networks (such as the Internet backbone). However, the implementation of a mechanism to collect such traces is difficult in practice. In the absence of real traces, tools to generate high bandwidth traces would aid the study of high speed network behavior. We describe three methods of generating high bandwidth network traces: scaling low bandwidth network traffic traces; merging multiple low bandwidth traces; generating traces through simulation by scaling a structural model of real world traces. We evaluate the generated traces and discuss the advantages and disadvantages of each method. We also discuss some of the issues involved in generating traces by the structural model method.

Rapid model parameterization from traffic measurements

The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly is changing over time, existing approaches typically take years from collecting trace, analyzing the data to finally generating and implementing models. In this paper, we describe approaches and tools that support rapid parameterization of traffic models from live network measurements. Rather than treating measured traffic as a time-series of statistics, we utilize the traces to estimate end-user behavior and network conditions to generate application-level simulation models. We also show multi-scaling analytic techniques are helpful for debugging and validating the model. To demonstrate our approaches, we develop structural source-level models for web and FTP traffic and evaluate their accuracy by comparing the outputs of simulation against the original trace. We also compare our work with existing traffic generation tools and show our approach is more flexible in capturing the heterogeneity of traffic. Finally, we automate and integrate the process from trace analysis to model validation for easy model parameterization from new data.

An intelligent driver location system for smart parking

It is often frustrating for drivers to find parking spaces, and parking itself is costly in almost every major city in the world. Here we propose a crowdsourcing solution by exploiting sensors in smart-phones to collect real-time parking availability information. We design a phone-based system to track a drivers trajectory to detect when they are about to leave their parking spot. We focus on the efficiency and accuracy of using a phone to monitor the drivers walking trajectory, applying a waist-mounted PDR method that can measure the drivers moving distance with a high accuracy. In addition, we design a map matching algorithm to calibrate the direction errors when the driver is in an indoor environment, using widely-available building floor plans. The results of our experiment show that we can achieve about 98% accuracy in estimating the users walking distance, with an overall location error of about 0.48m.