Towards reliable geographic broadcasting in vehicular networks

In Vehicular ad hoc Networks (VANETs), safety-related messages are broadcasted amongst cars, helping to improve drivers' awareness of the road situation. VANETs’ reliability are highly affected by channel contention. This thesis first addresses the issue of channel use efficiency in geographical broadcasts (geocasts). Constant connectivity changes inside a VANET make the existing routing algorithms unsuitable. This thesis presents a geocast algorithm that uses a metric to estimate the ratio of useful to useless packet received. Simulations showed that this algorithm is more channel-efficient than the farthest-first strategy. It also exposes a parameter, allowing it to adapt to channel load. Second, this thesis presents a method of estimating channel load for providing feedback to moderate the offered load. A theoretical model showing the relationship between channel load and the idle time between transmissions is presented and used to estimate channel contention. Unsaturated stations on the network were shown to have small but observable effects on this relationship. In simulations, channel estimators based on this model show higher accuracy and faster convergence time than by observing packet collisions. These estimators are also less affected by unsaturated stations than by observing packet collisions. Third, this thesis couples the channel estimator to the geocast algorithm, producing a closed-loop load-reactive system that allows geocasts to adapt to instantaneous channel conditions. Simulations showed that this system is not only shown to be more efficient in channel use and be able to adapt to channel contention, but is also able to self-correct suboptimal retransmission decisions. Finally, this thesis demonstrates that all tested network simulators exhibit unexpected behaviours when simulating broadcasts. This thesis describes in depth the error in ns-3, leading to a set of workarounds that allows results from most versions of ns-3 to be interpreted correctly

Query Processor

contribution Title: Implementation of a resource-aware, data clustering algorithm for the Sun SPOT Distribute

An integrated command and control architecture concept for unmanned systems in the year 2030

U.S. Forces require an integrated Command and Control Architecture that enables operations of a dynamic mix of manned and unmanned systems. The level of autonomous behavior correlates to: 1) the amount of trust with the reporting vehicles, and 2) the multi-spectral perspective of the observations. The intent to illuminate the architectural issues for force protection in 2030 was based on a multi-phased analytical model of High Value Unit (HVU) defense. The results showed that autonomous unmanned aerial vehicles are required to defeat high-speed incoming missiles. To evaluate the level of autonomous behavior required for an integrated combat architecture, geometric distributions were modeled to determine force positioning, based on a scenario driven Detect-to-Engage timeline. Discrete event simulation was used to schedule operations, and a datalink budget assessment of communications to determine the critical failure paths in the the integrated combat architecture. The command and control principles used in the integrated combat architecture were based on Boyd's OODA (Obseve, Orient, Decide, and Act) Loop. A conservative fleet size estimate, given the uncertainties of the coverage overlap and radar detection range, a fleet size of 35 should be anticipated given an UAV detection range of 20km and radar coverage overlap of 4 seconds.http://archive.org/details/anintegratedcomm109455244US Navy (USN) authorsApproved for public release; distribution is unlimited