Baris Dundar

Traffic Measurement and Vehicle Classification with Single Magnetic Sensor

Wireless magnetic sensor networks offer a very attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to vehicle count, occupancy and speed, the sensors yield traffic information (such as vehicle classification) that cannot be obtained from loop data. Because such networks can be deployed in a very short time, they can also be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors, based on two field experiments. The first experiment collected a two-hour trace of measurements on Hearst Avenue in Berkeley. The vehicle detection rate is better than 99 percent (100 percent for vehicles other than motorcycles); and estimates of vehicle length and speed appear to be better than 90 percent. Moreover, the measurements also give inter-vehicle spacing or headways, which reveal such interesting phenomena as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into 6 types. Sixty percent of the vehicles are classified correctly, when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high scan-rate inductive loop signals, which require extensive offline computation. We believe that when length is used as a feature, 80-90 percent of vehicles will be correctly classified.

Hybrid algorithm for indoor positioning using wireless LAN

Locating an indoor mobile station based on a wireless communication infrastructure, has practical applications. The most widely employed methods today use an RF propagation loss (PL) model or location fingerprinting (LF). The PL method is known to perform poorly compared to LF. But LF requires an extensive training dataset and cannot adapt well to configuration changes or receiver breakdown. In this paper, we develop a hybrid method that combines the strength of these two methods. It first formulates the RF PL in a nonlinear, censored regression model and adjusts the regression function to the observed signal strength in the fingerprint dataset. In the absence of a training dataset, the hybrid method coincides with the PL method, and, as the spatial granularity of the training dataset increases, the result of the algorithm approaches the result of the LF method. It balances the flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic. We evaluate the performance of the algorithm by applying it to a real site and observe satisfactory positioning accuracy.

Application of GPS to mobile IP and routing in wireless networks

We discuss how the location information provided by GPS can be used at different layers of the protocol stack of a mobile host. Our discussion focuses an application of the two GPS based networking protocols we have designed and implemented. The network topology we are considering is composed of fixed Internet with base station at the edges connected with an ad hoc networks of mobile nodes that are connecting information from sensors spread through a region. In the network, base stations and mobile nodes are GPS equipped. The idea is to leverage the information of the mobile and base station positions, obtained via the GPS, to improve performance of the ad hoc routing, to adaptively determine the appropriate base station capacity to be reserved strictly for handoffs, to collect data from sensors scattered in the medium and to inform mobiles about the prospective future location.

POSITION LEVERAGE SMOOTH HANDOVER ALGORITHM FOR MOBILE IP

Mobile IP is designed to support uninterrupted connectivity of mobile computers as they roam from place to place. We propose a fast intra-domain and inter-domain handoff scheme using the location of routers to meet the delay and packet loss requirements of real-time services. The scheme achieves an intelligent and dynamic neighborhood discovery and avoids the use of multicast addresses in intra-domain handoff. In addition, it decreases the registration time and distributes home agent data base dynamically in inter-domain handoff depending on how far from the home agent mobile host is. Simulation and implementation results for an interactive voice communication are presented.

Throughput analysis of an extended service set in IEEE 802.11

We present a method to determine the throughput of an extended service set in an IEEE 802.11 network. The method combines two tools. The first tool takes the location of access points and a floor plan and uses an RF propagation model to predict the spatial distribution of SNR and interference that mobile stations will experience. The second tool takes the SNR of mobile stations and predicts system throughput, based on an analytical Markov chain model. The method can help to intelligently plan WLAN deployment.