GPS calibrated ad-hoc localization for geosocial networking

LNCS v. 6406 is conference proceedings of UIC 2010Cost-effective localization for large-scale Geosocial networking service is a challenging issue in urban environment. This paper studies an ad-hoc localization technique which takes advantages of short-range interchanged location information for calibrating the location of mobile users carrying non-GPS mobile phones. We demonstrate by simulation that a small percentage of GPS-enabled mobile phones can greatly enable the localization of other non-GPS pedestrians in the urban environment. Based on the proposed localization technique, we implement a location-aware social networking tool called Mobile Twitter, similar to the microblogging service of Twitter, for fast propagation of social events happening in surroundings. Evaluation shows the our localization algorithm can achieve better accuracy of the location estimation and wider coverage as compared with the Amorphous algorithm and the Monte Carlo Localization (MCL) method. Moreover, we show that the Mobile Twitter implemented on an Android mobile phone is power-efficient in real-life usage scenarios. © 2010 Springer-Verlag.postprintThe 7th International Conference on Ubiquitous Intelligence and Computing (UIC) 2010, Xi'an, China, 26-29 October 2010. In Lecture Notes in Computer Science, 2010, v. 6406, p. 52-6

On the performance of social-based and location-aware forwarding strategies in urban vehicular networks

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work evaluates the performance of several location-based and social-aware forwarding schemes through emulations and in a real scenario. Gateway Location Awareness (GLA), a location-aware ranking classification, makes use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns. Aging Social-Aware Ranking (ASAR) exploits the social behavior of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles. To merge both location and social aforementioned algorithms, a HYBRID approach emerges, thus generating a more intelligent mechanism. For each strategy, we evaluate the influence of several parameters in the network performance, as well as we comparatively evaluate the strategies in different scenarios. Experiment results, obtained both in emulated (with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network) and real scenarios, show the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of mobile networks as a smart city network infrastructure.publishe

Wireless Networking for Vehicle to Infrastructure Communication and Automatic Incident Detection

Vehicular wireless communication has recently generated wide interest in the area of wireless network research. Automatic Incident Detection (AID), which is the recent focus of research direction in Intelligent Transportation System (ITS), aims to increase road safety. These advances in technology enable traffic systems to use data collected from vehicles on the road to detect incidents. We develop an automatic incident detection method that has a significant active road safety application for alerting drivers about incidents and congestion. Our method for detecting traffic incidents in a highway scenario is based on the use of distance and time for changing lanes along with the vehicle speed change over time. Numerical results obtained from simulating our automatic incident detection technique suggest that our incident detection rate is higher than that of other techniques such as integrated technique. probabilistic technique and California Algorithm. We also propose a technique to maximize the number of vehicles aware of Road Side Units (RSUs) in order to enhance the accuracy of our AID technique. In our proposed Method. IEEE 802.11 standard is used at RSUs with multiple antennas to assign each lane a specific channel. To validate our proposed approach. we present both analytical and simulation scenarios. The empirical values which are obtained from both analytical and simulation results have been compared to show their consistency. Results indicate that the IEEE 802.11 standard with its beaconing mechanism can be successfully used for Vehicle to Infrastructure (V2I) communications

Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations

The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters

Efficient wireless location estimation through simultaneous localization and mapping

Conventional Wi-Fi location estimation techniques using radio fingerprinting typically require a lengthy initial site survey. It is suggested that the lengthy site survey is a barrier to adoption of the radio fingerprinting technique. This research investigated two methods for reducing or eliminating the site survey and instead build the radio map on-the-fly. The first approach utilized a deterministic algorithm to predict the user's location near each access point and subsequently construct a radio map of the entire area. This deterministic algorithm performed only fairly and only under limited conditions, rendering it unsuitable for most typical real-world deployments. Subsequently, a probabilistic algorithm was developed, derived from a robotic mapping technique called simultaneous localization and mapping. The standard robotic algorithm was augmented with a modified particle filter, modified motion and sensor models, and techniques for hardware-agnostic radio measurements (utilizing radio gradients and ranked radio maps). This algorithm performed favorably when compared to a standard implementation of the radio fingerprinting technique, but without needing an initial site survey. The algorithm was also reasonably robust even when the number of available access points were decreased.Ph.D.Committee Chair: Owen, Henry; Committee Member: Copeland, John; Committee Member: Giffin, Jonathon; Committee Member: Howard, Ayanna; Committee Member: Riley, Georg