An important problem in mobile ad-hoc wireless sensor networks is the localization of individual nodes, i.e., each node's awareness of its position relative to the network. In this paper, we introduce a variant of this problem (directional localization) where each node must be aware of both its position and orientation relative to the network. This variant is especially relevant for the applications in which mobile nodes in a sensor network are required to move in a collaborative manner. Using global positioning systems for localization in large scale sensor networks is not cost effective and may be impractical in enclosed spaces. On the other hand, a set of pre-existing anchors with globally known positions may not always be available. To address these issues, in this work we propose an algorithm for directional node localization based on relative motion of neighboring nodes in an ad-hoc sensor network without an infrastructure of global positioning systems (GPS), anchor points, or even mobile seeds with known locations. Through simulation studies, we demonstrate that our algorithm scales well for large numbers of nodes and provides convergent localization over time, even with errors introduced by motion actuators and distance measurements. Furthermore, based on our localization algorithm, we introduce mechanisms to preserve network formation during directed mobility in mobile sensor networks. Our simulations confirm that, in a number of realistic scenarios, our algorithm provides for a mobile sensor network that is stable over time irrespective of speed, while using only constant storage per neighbor.

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Location discovery, especially in mobile environments, has recently become the key component of many applications. Accurate location discovery, particularly in safety critical applications using autonomous robots or unmanned vehicles, however, is still an open problem. Existing popular methods either heavily rely on the use of global positioning systems (GPS) which do not readily lend themselves for use for the majority of applications where precision is of primary concern or are not suitable for ad-hoc deployments. In this paper, we propose a novel directional localization algorithm, called Dual Wireless Radio Localization (DWRL), which performs accurate node localizations in the plane using only distances between nodes, without the use of a GPS or nodes with known positions (anchors). The main novelty of DWRL is the use of an additional radio per node to support directional localization in static networks. To the best of our knowledge, this is the first time dual radios are employed in a localization setting. Existence of the dual radios on board enables DWRL algorithm to perform directional localization, which is not possible with existing single radio systems in static networks. We present the practical and theoretical benefits of the use of an additional radio per node in detail, test our algorithm under excessive synthetic and real-world noise scenarios, and show that DWRL algorithm is robust enough to perform directional localization even in high noise environments.

DNS is one of the most actively used distributed databases on earth, accessed by millions of people every day to transparently convert host names into IP addresses and vice versa. In order to improve their performance, DNS servers also keep temporary records of all requested domain names in their cache. While most of the DNS servers are configured to be used by their local users only, there still exist many DNS servers that respond to public queries. Querying these DNS servers reveals the recently visited domains. Exploiting the geographically distributed nature of DNS, one can gather usage statistics ranging from a single DNS server to global scale. In particular, this enables collecting statistics about geographic differences in web browsing behavior between different regions of a country or the world. In this paper, we present methods to identify these public DNS servers, discuss how to effectively crawl them, and describe our algorithm to extract usage estimations from the crawl data. We also evaluate our estimation algorithm using extensive simulations, and finally use our algorithms to crawl 150 U.S. universities for various domains, and explore the effects of location and time on the access rate of these domains.

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