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Worst Case Mobility in Ad Hoc Networks
, 2003
"... We investigate distributed algorithms for mobile ad hoc networks for moving radio stations with adjustable transmission power in a worst case scenario. We consider two models to find a reasonable restriction on the worst-case mobility. In the pedestrian model we assume a maximum speed v_max of the r ..."
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Cited by 16 (3 self)
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We investigate distributed algorithms for mobile ad hoc networks for moving radio stations with adjustable transmission power in a worst case scenario. We consider two models to find a reasonable restriction on the worst-case mobility. In the pedestrian model we assume a maximum speed v_max of the radio stations, while in the vehicular model we assume a maximum acceleration a_max of the points. Our goal is to maintain...
Kinetic and Dynamic Data Structures for Convex Hulls and Upper Envelopes
, 2005
"... Let S be a set of n moving points in the plane. We present a kinetic and dynamic (randomized) data structure for maintaining the convex hull of S. The structure uses O(n) space, and processes an expected number of O(n² βs+2(n)log n) critical events, each in O(log² n) expected time, including O(n) ..."
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Cited by 12 (2 self)
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Let S be a set of n moving points in the plane. We present a kinetic and dynamic (randomized) data structure for maintaining the convex hull of S. The structure uses O(n) space, and processes an expected number of O(n² βs+2(n)log n) critical events, each in O(log² n) expected time, including O(n) insertions, deletions, and changes in the flight plans of the points. Here s is the maximum number of times where any specific triple of points can become collinear, βs(q) = λs(q)/q, and λs(q) is the maximum length of Davenport-Schinzel sequences of order s on n symbols. Compared with the previous solution of Basch, Guibas and Hershberger [8], our structure uses simpler certificates, uses roughly the same resources, and is also dynamic.
Mobility in wireless networks
- In 32nd Annual Conference on Current Trends in Theory and Practice of Informatics, Czech
, 2006
"... Abstract. This article surveys mobility patterns and mobility models for wirelss networks. Mobility patterns are classified into the following types: pedestrians, vehicles, aerial, dynamic medium, robot, and outer space motion. We present the characteristics of each and shortly mention the specific ..."
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Cited by 8 (0 self)
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Abstract. This article surveys mobility patterns and mobility models for wirelss networks. Mobility patterns are classified into the following types: pedestrians, vehicles, aerial, dynamic medium, robot, and outer space motion. We present the characteristics of each and shortly mention the specific problems. We shortly present the specifics of cellular networks, mobile ad hoc networks, and sensor networks regarding mobility. Then, we present the most important mobility models from the literature. At last we give a brief discussion about the state of research regarding mobility in wireless networks. 1
Efficient Tradeoff Schemes in Data Structures for Querying Moving Objects
"... The ability to represent and query continuously moving objects is important in many applications of spatio-temporal database systems. In this paper we develop data structures for answering various queries on moving objects, including range and proximity queries, and study tradeoffsbetween various p ..."
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Cited by 4 (1 self)
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The ability to represent and query continuously moving objects is important in many applications of spatio-temporal database systems. In this paper we develop data structures for answering various queries on moving objects, including range and proximity queries, and study tradeoffsbetween various performance measures--query time, data structure size, and accuracy of results.
unknown title
"... Vol. 26 ISMB 2010, pages i21–i28 doi:10.1093/bioinformatics/btq178 ..."
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A Computational Framework for Incremental Motion
"... We propose a generic computational framework for maintaining a discrete geometric structure defined by a collection of static and mobile objects. We assume that the mobile objects move incrementally, that is, in discrete time steps. We assume that the structure to be maintained is a function of the ..."
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We propose a generic computational framework for maintaining a discrete geometric structure defined by a collection of static and mobile objects. We assume that the mobile objects move incrementally, that is, in discrete time steps. We assume that the structure to be maintained is a function of the current locations of the mobile and static objects (independent of their prior motion). Unlike other models for kinetic computation, we place no restrictions on the motion nor on its predictability. In order to handle unrestricted incremental motion, our framework is based on the coordination of two computational entities. The first is the incremental motion algorithm. It is responsible for maintaining the structure and a set of certificates, or conditions, that prove the structure’s correctness. The other entity, called the motion processor, is responsible for handling all the low-level aspects of motion, including computing and/or tracking the motion of the mobile objects, answering queries about their current positions and velocities, and validating that the object motions satisfy simple motion estimates, which are generated by the incremental motion algorithm. Computational efficiency is measured in terms of the number of interactions between these two entities.
Self-Adjusting Programming
, 2005
"... This papers proposes techniques for writing self-adjusting programs that can adjust to any change to their data (e.g., inputs, decisions made during the computation) etc. We show that the techniques are e#cient by considering a number of applications including several sorting algorithms, and the Gra ..."
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This papers proposes techniques for writing self-adjusting programs that can adjust to any change to their data (e.g., inputs, decisions made during the computation) etc. We show that the techniques are e#cient by considering a number of applications including several sorting algorithms, and the Graham Scan, and the quick hull algorithm for computing convex hulls. We show that the techniques are flexible by showing that self-adjusting programs can be trivially transformed into a kinetic programs that maintain their property as their input move continuously. We show that the techniques are practical by implementing a Standard ML library for kinetic data structures and applying the library to kinetic convex hulls. We show that the kinetic programs written with the library are more than an order of magnitude faster than recomputing from scratch. These results rely on a combination of memoization and dynamic dependence graphs. We show that the combination is sound by presenting a semantics based on abstraction of memoization via an oracle.
A Computational Framework for Incremental Motion
"... We propose a generic computational framework for main-taining a discrete geometric structure defined by a collection of static and mobile objects. We assume that the mobile ob-jects move incrementally, that is, in discrete time steps. We assume that the structure to be maintained is a function of th ..."
Abstract
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We propose a generic computational framework for main-taining a discrete geometric structure defined by a collection of static and mobile objects. We assume that the mobile ob-jects move incrementally, that is, in discrete time steps. We assume that the structure to be maintained is a function of the current locations of the mobile and static objects (in-dependent of their prior motion). Unlike other models for kinetic computation, we place no restrictions on the motion nor on its predictability. In order to handle unrestricted incremental motion, our framework is based on the coordination of two computa-tional entities. The first is the incremental motion algo-rithm. It is responsible for maintaining the structure and a set of certificates, or conditions, that prove the structure’s correctness. The other entity, called the motion processor, is responsible for handling all the low-level aspects of mo-tion, including computing and/or tracking the motion of the mobile objects, answering queries about their current posi-tions and velocities, and validating that the object motions satisfy simple motion estimates, which are generated by the incremental motion algorithm. Computational efficiency is measured in terms of the number of interactions between these two entities.
