S. Olariu

Rate-based borrowing scheme for QoS provisioning in multimedia wireless networks

Now that cellular networks are being called upon to support real-time interactive multimedia traffic such as video teleconferencing, these networks must be able to provide their users with quality-of-service (QoS) guarantees. Although the QoS provisioning problem arises in wireline networks as well, mobility of hosts, scarcity of bandwidth, and channel fading make QoS provisioning a challenging task in wireless networks. It has been noticed that multimedia applications can tolerate and gracefully adapt to transient fluctuations in the QoS that they receive from the network. The management of such adaptive multimedia applications is becoming a new research area in wireless networks. As it turns out, the additional flexibility afforded by the ability of multimedia applications to tolerate and adapt to transient changes in the QoS parameters can be exploited by protocol designers to significantly improve the overall performance of wireless systems. The main contribution of this paper is to propose a novel, rate-based, borrowing scheme for QoS provisioning in high-speed cellular networks carrying multimedia traffic. Our scheme attempts to allocate the desired bandwidth to every multimedia connection originating in a cell or being handed off to the cell. The novelty of our scheme is that, in case of insufficient bandwidth, in order not to deny service to requesting connections (new or hand-off), bandwidth will be borrowed, on a temporary basis, from existing connections. Our borrowing scheme guarantees that no connection gives up more than its fair share of bandwidth, in the sense that the amount of bandwidth borrowed from a connection is proportional to its tolerance to bandwidth loss. Importantly, our scheme ensures that the borrowed bandwidth is promptly returned to the degraded connections. Extensive simulation results show that our rate-based QoS provisioning scheme outperforms the best previously known schemes in terms of call dropping probability, call blocking probability, and bandwidth utilization.

Energy-efficient initialization protocols for single-hop radio networks with no collision detection

A radio network (RN, for short) is a distributed system consisting of n radio stations. We assume that the stations are small, bulk-produced, hand-held devices running on batteries and cannot be distinguished by serial or manufacturing number. Since recharging batteries may not be possible while on mission, we are interested in designing protocols that are highly energy-efficient. The initialization problem is to assign each of the n stations in the RN a unique ID. The initialization problem is nontrivial since the stations are assumed to be indistinguishable. The problem is fundamental, since practically all communication protocols for RNs proceed under the assumption that the RN has been initialized in advance. The main contribution of this work is to propose energy-efficient randomized initialization protocols for single-hop RNs lacking collision detection capabilities. First, we show that if the number n of stations is known beforehand, the single-channel RN can be initialized by a protocol that terminates, with probability exceeding 1-/sup 1///sub n/ in O(n) time slots, with no station being awake for more than O(log log n) time slots. We then go on to address the multichannel case and show that if k, (k/spl ges/1), channels are available, an n-station RN can be initialized, with probability exceeding 1-/sup 1///sub n/, in O(/sup n///sub k/+log n) time slots, with no station being awake for more than O(log log n) time slots.

An energy-efficient self-organization protocol for wireless sensor networks

The networks considered consist of tiny energy constrained commodity sensors massively deployed in an area of interest. In a wireless sensor network, energy conservation is the primary design goal. The most efficient way to save energy is keeping sensor nodes turned off as long as possible. These sleeping-or-awaking nodes need the capabilities of self-organization and re-organization to adapt to dynamic environment and network settings. The paper describes an energy efficient self-organization protocol designed for a wireless sensor network.

An optimal algorithm for the angle-restricted all nearest neighbor problem on the reconfigurable mesh, with applications

Given a set S of n points in the plane and two directions r/sub 1/ and r/sub 2/, the Angle-Restricted All Nearest Neighbor problem (ARANN, for short) asks to compute, for every point p in S, the nearest point in S lying in the planar region bounded by two rays in the directions r/sub 1/ and r/sub 2/ emanating from p. The ARANN problem generalizes the well-known ANN problem and finds applications to pattern recognition, image processing, and computational morphology. Our main contribution is to present an algorithm that solves an instance of size n of the ARANN problem in O(1) time on a reconfigurable mesh of size n/spl times/n. Our algorithm is optimal in the sense that /spl Omega/(n/sup 2/) processors are necessary to solve the ARANN problem in O(1) time. By using our ARANN algorithm, we can provide O(1) time solutions to the tasks of constructing the Geographic Neighborhood Graph and the Relative Neighborhood Graph of n points in the plane on a reconfigurable mesh of size n/spl times/n. We also show that, on a somewhat stronger reconfigurable mesh of size n/spl times/n/sup 2/, the Euclidean Minimum Spanning Tree of n points can be computed in O(1) time.

Fundamental data movement algorithms for reconfigurable meshes

A number of data movement algorithms for the two-dimensional reconfigurable mesh are presented. These include computing the prefix sum of a binary sequence and computing the prefix maxima of a sequence of real numbers. These algorithms lead to a fast algorithm to sort a sequence of n reals in O(log n/log m) time on a reconfigurable mesh of size mn*n with 3<or=m<or=n. The result implies that sorting n real numbers takes O(1) time on a reconfigurable mesh of size n/sup 1.5/*n. The sorting algorithm uses significantly fewer processors than the best-known algorithm to date. Next, it is shown that computing the convex hull of a planar set of n points takes O(log n/log m) time on a reconfigurable mesh of size mn*n with 3<or=m<or=n. The result implies that the convex hull of n points in the plane can be coupled in O(1) time on a reconfigurable mesh of size n/sup 1.5/*n.<<ETX>>

Numerical Simulations and Case Studies Using Visual C++.Net

Preface. 1. Developing Applications Using Visual C++.Net. 1.1 Object-Oriented Approach to Visual C++.Net. 1.2 MFC Fundamental Features. 1.3 Writing Applications Using MFC. 1.4 Writing the First Nonwizard Program. 1.5 Discussion. 1.6 Summary and Conclusion. Bibliography. 2. Interfaces for Numerical Problems. 2.1 Visualizing a Numerical Problem. 2.2 Handling Arrays. 2.3 Finding the Root of a Nonlinear Equation. 2.4 Solving a System of Linear Equations. 2.5 Summary and Conclusion. Bibliography. Code Listings. 3. Matrix Operations Using Wizard. 3.1 Document/View Architecture Using Wizard. 3.2 Matrix Algebra. 3.3 System of Linear Equations Problem Revisited. 3.4 Summary and Conclusion. Bibliography. Code Listings. 4. Differential Equations Problems. 4.1 Differential Equations. 4.2 Ordinary Differential Equations. 4.3 Partial Differential Equations. 4.4 Summary and Conclusion. Bibliography. Code Listings. 5. Drawing Curves. 5.1 Windows Graphics Representation. 5.2 MFC Functions for Displaying Graphics. 5.3 Drawing a Curve. 5.4 Cubic Spline Interpolation. 5.5 Summary and Conclusion. Bibliography. Code Listings. 6. Working with Images. 6.1 Handling Images. 6.2 Bitmap File Format. 6.3 Edge-Detection Problem. 6.4 Summary and Conclusion. Bibliographical Note. Code Listing. 7. Visualizing a Graph. 7.1 Elementary Graph Concepts. 7.2 Graph Visualization Model. 7.3 Minimum Spanning Tree Problem. 7.4 Summary and Conclusion. Bibliography. Code Listings. 8. Graph Applications. 8.1 Graph-Network Relationship. 8.2 Shortest-Path Problem. 8.3 Mesh Network Applications. 8.4 Summary and Conclusion. Bibliography. Code Listings. 9. Multiprocessor Scheduling Problem. 9.1 Parallel Computing Systems. 9.2 Task Scheduling Problem. 9.3 Task Scheduling Visualization Model. 9.4 Summary and Conclusion. Bibliography. Code Listings. 10. Discrete-Event Simulation. 10.1 Concepts of Simulation. 10.2 Simulation Model Development. 10.3 Discrete-Event System Simulations. 10.4 Multicounter System with Blocking. 10.5 Queueing Systems. 10.6 Summary and Conclusion. Bibliography. Code Listings. 11. Modeling Wireless Networks. 11.1 Wireless Cellular Networks. 11.2 Channel-Assignment Problem. 11.3 Channel Assignments: Discrete Model. 11.4 Solving the Channel-Assignment Problem. 11.5 Summary and Conclusion. Bibliography. Code Listings. Index.

Square Meshes are not Optimal for Convex Hull Computation

Recently it has been noticed that for semigroup computations and for selection, rectangular meshes with multiple broadcasting yield faster algorithms than their square counterparts. The contribution of this paper is to provide yet another example of a fundamental problem for which this phenomenon occurs.

An efficient EREW algorithm for minimum path cover and Hamiltonicity on cographs

It is shown that the notoriously difficult problem of finding the minimum number of paths that cover the vertices of a graph can be solved efficiently for cographs. The result implies that for this class of graphs finding a Hamiltonian path and a Hamiltonian cycle can be solved efficiently in parallel. Specifically, with an n-vertex cograph G represented by its parse tree as input, the algorithm determines the number of paths in a minimum path cover in O(log n) time using n/log n processors in the exclusive read exclusive write parallel random access machine (EREW-PRAM) model. The authors also exhibit all the paths in a minimum path cover of G in O(log/sup 2/n) time using n/log n processors in the EREW-PRAM. >

Optimal algorithms for the multiple query problem on reconfigurable meshes, with applications

The main contribution of this work is to show that a number of fundamental and seemingly unrelated problems in database design, pattern recognition, robotics, computational geometry, and image processing can be solved simply and elegantly by stating them as instances of a unifying algorithmic framework that we call the multiple query problem. The multiple query problem (MQ, for short) is a 5-tuple (Q, A, D, /spl phi/, /spl oplus/), where Q is a set of queries, A is a set of items, D is a set of solutions, /spl phi/: Q/spl times/A/spl rarr/D is a function, and /spl oplus/ is a commutative and associative binary operator over D. The input to the MQ problem consists of a sequence Q= of m queries from Q and of a sequence A= of n items from A. The goal is to compute, for every query q/sub i/ (1/spl les/i/spl les/m) its solution defined as /spl phi/(q/sub i/,A)=/spl phi/(q/sub i/,a/sub 1/)/spl oplus//spl phi/(q/sub i/,a/sub 2/)/spl oplus//spl middot//spl middot//spl middot//spl oplus//spl phi/(q/sub i/,a/sub n/). We begin by discussing a generic algorithm that solves a large class of MQ problems in O(/spl radic/m+f(n)) time on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n, where f(n) is the time necessary to compute the expression d/sub 1/ /spl oplus/ d/sub 2/ /spl oplus//spl middot//spl middot//spl middot//spl oplus/ d/sub n/ with d/sub i/ /spl isin/ D on such a platform. We then go on to show that the MQ framework affords us an optimal algorithm for the multiple point location problem on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n. Given a set A of n points and a set Q of m (m/spl les/n) points in the plane, our algorithm reports, in O(/spl radic/m+log log n) time, all points of Q that lie inside the convex hull of A. Quite surprisingly, our algorithm solves the multiple point location problem without computing the convex hull of A which, in itself, takes /spl Omega/(/spl radic/n) time on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n. Finally, we prove an /spl Omega/(/spl radic/m+g(n)) time lower bound for nontrivial MQ problems, where g(n) is the lower bound for evaluating the expression d/sub 1/ /spl oplus/ d/sub 2/ /spl oplus//spl middot//spl middot//spl middot//spl oplus/ d/sub n/ with d/sub i/ /spl isin/ D, on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n.

Time-optimal sorting and applications on n*n enhanced meshes

Time-optimal sorting and convex hull algorithms are proposed for two classes of enhanced meshes, the mesh with multiple broadcasting and the reconfigurable mesh. The authors show that the fundamental problem of sorting n items can be solved in O(log n) time on a mesh with multiple broadcasting of size n*n, which leads to an O(log n) time algorithm to compute the convex hull of an arbitrary set of n points in the plane. Based on the constant-time sorting algorithm on reconfigurable meshes, it is suggested that the convex hull problem of size n can be solved in constant time on a reconfigurable mesh of size n*n. All these algorithms can achieve time lower bounds for their respective models of computation.<<ETX>>