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Mobile telemedicine sensor networks with lowenergy data query and network lifetime considerations
 IEEE Transactions on Mobile Computing
, 2006
"... Abstract—In this paper, we use an integrated architecture that takes advantage of the low cost mobile sensor networks and 3G cellular networks to accommodate multimedia medical calls with differentiated QualityofService (QoS) requirements. We propose a lowenergy, distributed, and concentriczoneb ..."
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Abstract—In this paper, we use an integrated architecture that takes advantage of the low cost mobile sensor networks and 3G cellular networks to accommodate multimedia medical calls with differentiated QualityofService (QoS) requirements. We propose a lowenergy, distributed, and concentriczonebased data query mechanism that takes advantages of hierarchical ad hoc routing algorithms to enable a medical specialist to collect physiological data from mobile and/or remote patients. The medical specialist uses cellular network to report patients ’ data to the medical center. Moreover, we propose a transmission scheme among different zones with balancebased energy efficiency, which can extend network lifetime. We evaluate the validity of our proposals through simulations and analyze their performance. Our results clearly indicate the energy efficiency of the proposed sensor network query algorithms and the efficiency of our multiclass medical call admission control scheme in terms of meeting the multimedia telemedicine QoS requirements. Index Terms — Mobile telemedicine, ad hoc networks, sensor networks, 3G wireless cellular networks. æ 1
RankPairing Heaps
"... Abstract. We introduce the rankpairing heap, a heap (priority queue) implementation that combines the asymptotic efficiency of Fibonacci heaps with much of the simplicity of pairing heaps. Unlike all other heap implementations that match the bounds of Fibonacci heaps, our structure needs only one c ..."
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Abstract. We introduce the rankpairing heap, a heap (priority queue) implementation that combines the asymptotic efficiency of Fibonacci heaps with much of the simplicity of pairing heaps. Unlike all other heap implementations that match the bounds of Fibonacci heaps, our structure needs only one cut and no other structural changes per key decrease; the trees representing the heap can evolve to have arbitrary structure. Our initial experiments indicate that rankpairing heaps perform almost as well as pairing heaps on typical input sequences and better on worstcase sequences. 1
How to Find a Minimum Spanning Tree in Practice
 results and New Trends in Computer Science, volume 555 of Lecture Notes in Computer Science
, 1991
"... We address the question of theoretical vs. practical behavior of algorithms for the minimum spanning tree problem. We review the factors that influence the actual running time of an algorithm, from choice of language, machine, and compiler, through lowlevel implementation choices, to purely algor ..."
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We address the question of theoretical vs. practical behavior of algorithms for the minimum spanning tree problem. We review the factors that influence the actual running time of an algorithm, from choice of language, machine, and compiler, through lowlevel implementation choices, to purely algorithmic issues. We discuss how to design a careful experimental comparison between various alternatives. Finally, we present some results from an ongoing study in which we are using: multiple languages, compilers, and machines; all the major variants of the comparisonbased algorithms; and eight varieties of graphs with sizes of up to 130,000 vertices (in sparse graphs) or 750,000 edges (in dense graphs). 1 Introduction Finding spanning trees of minimum weight (minimum spanning trees or MSTs) is one of the best known graph problems; algorithms for this problem have a long history, for which see the article of Graham and Hell [6]. The best comparisonbased algorithm to date, due to Gabow...
A Generalization of Binomial Queues
 Information Processing Letters
, 1996
"... We give a generalization of binomial queues involving an arbitrary sequence (mk )k=0;1;2;::: of integers greater than one. Different sequences lead to different worst case bounds for the priority queue operations, allowing the user to adapt the data structure to the needs of a specific application. ..."
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We give a generalization of binomial queues involving an arbitrary sequence (mk )k=0;1;2;::: of integers greater than one. Different sequences lead to different worst case bounds for the priority queue operations, allowing the user to adapt the data structure to the needs of a specific application. Examples include the first priority queue to combine a sublogarithmic worst case bound for Meld with a sublinear worst case bound for Delete min. Keywords: Data structures; Meldable priority queues. 1 Introduction The binomial queue, introduced in 1978 by Vuillemin [14], is a data structure for meldable priority queues. In meldable priority queues, the basic operations are insertion of a new item into a queue, deletion of the item having minimum key in a queue, and melding of two queues into a single queue. The binomial queue is one of many data structures which support these operations at a worst case cost of O(logn) for a queue of n items. Theoretical [2] and empirical [9] evidence i...
The integration of ad hoc sensor and cellular networks for multiclass data transmission
, 2006
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On sorting, heaps, and minimum spanning trees
 Algorithmica
"... Let A be a set of size m. Obtaining the first k ≤ m elements of A in ascending order can be done in optimal O(m + k log k) time. We present Incremental Quicksort (IQS), an algorithm (online on k) which incrementally gives the next smallest element of the set, so that the first k elements are obtaine ..."
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Let A be a set of size m. Obtaining the first k ≤ m elements of A in ascending order can be done in optimal O(m + k log k) time. We present Incremental Quicksort (IQS), an algorithm (online on k) which incrementally gives the next smallest element of the set, so that the first k elements are obtained in optimal expected time for any k. Based on IQS, we present the Quickheap (QH), a simple and efficient priority queue for main and secondary memory. Quickheaps are comparable with classical binary heaps in simplicity, yet are more cachefriendly. This makes them an excellent alternative for a secondary memory implementation. We show that the expected amortized CPU cost per operation over a Quickheap of m elements is O(log m), and this translates into O((1/B)log(m/M)) I/O cost with main memory size M and block size B, in a cacheoblivious fashion. As a direct application, we use our techniques to implement classical Minimum Spanning Tree (MST) algorithms. We use IQS to implement Kruskal’s MST algorithm and QHs to implement Prim’s. Experimental results show that IQS, QHs, external QHs, and our Kruskal’s and Prim’s MST variants are competitive, and in many cases better in practice than current stateoftheart alternative (and much more sophisticated) implementations.
A back–to–basics empirical study of priority queues
, 2013
"... The theory community has proposed several new heap variants in the recent past which have remained largely untested experimentally. We take the field back to the drawing board, with straightforward implementations of both classic and novel structures using only standard, wellknown optimizations. We ..."
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The theory community has proposed several new heap variants in the recent past which have remained largely untested experimentally. We take the field back to the drawing board, with straightforward implementations of both classic and novel structures using only standard, wellknown optimizations. We study the behavior of each structure on a variety of inputs, including artificial workloads, workloads generated by running algorithms on real map data, and workloads from a discrete event simulator used in recent systems networking research. We provide observations about which characteristics are most correlated to performance. For example, we find that the L1 cache miss rate appears to be strongly correlated with wallclock time. We also provide observations about how the input sequence affects the relative performance of the different heap variants. For example, we show (both theoretically and in practice) that certain random insertiondeletion sequences are degenerate and can lead to misleading results. Overall, our findings suggest that while the conventional wisdom holds in some cases, it is sorely mistaken in others. 1
I/OEfficient Batched UnionFind and Its . . .
"... Despite extensive study over the last four decades and numerous applications, no I/Oefficient algorithm is known for the unionfind problem. In this paper we present an I/Oefficient algorithm for the batched (offline) version of the unionfind problem. Given any sequence of N mixed union andfin ..."
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Despite extensive study over the last four decades and numerous applications, no I/Oefficient algorithm is known for the unionfind problem. In this paper we present an I/Oefficient algorithm for the batched (offline) version of the unionfind problem. Given any sequence of N mixed union andfind operations, where each union operation joins two distinct sets, our algorithm uses O(SORT(N)) = O ( NB logM/B NB) I/Os, where M is the memory size and B is the disk block size. This bound isasymptotically optimal in the worst case. If there are union operations that join a set with itself, our algorithm uses O(SORT(N) + MST(N)) I/Os, where MST(N) is the number of I/Os needed to compute the minimum spanning tree of a graph with N edges. We also describe a simple and practical O(SORT(N) log ( NM))I/O algorithm, which we have implemented.The main motivation for our study of the unionfind problem arises from problems in terrain analysis. A terrain can be abstracted as a height function defined over R2, and many problems that deal with suchfunctions require a unionfind data structure. With the emergence of modern mapping technologies, huge amount of data is being generated that is too large to fit in memory, thus I/Oefficient algorithmsare needed to process this data efficiently. In this paper, we study two terrain analysis problems that benefit from a unionfind data structure: (i) computing topological persistence and (ii) constructing thecontour tree. We give the first O(SORT(N))I/O algorithms for these two problems, assuming that theinput terrain is represented as a triangular mesh with N vertices.Finally, we report some preliminary experimental results, showing that our algorithms give orderofmagnitude improvement over previous methods on large data sets that do not fit in memory.
DOI 10.1007/s1253200900028 FULL LENGTH PAPER
"... Abstract We describe a new implementation of the Edmonds’s algorithm for computing a perfect matching of minimum cost, to which we refer as Blossom V. A key feature of our implementation is a combination of two ideas that were shown to be effective for this problem: the “variable dual updates ” appr ..."
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Abstract We describe a new implementation of the Edmonds’s algorithm for computing a perfect matching of minimum cost, to which we refer as Blossom V. A key feature of our implementation is a combination of two ideas that were shown to be effective for this problem: the “variable dual updates ” approach of Cook and Rohe (INFORMS J Comput 11(2):138–148, 1999) and the use of priority queues. We achieve this by maintaining an auxiliary graph whose nodes correspond to alternating trees in the Edmonds’s algorithm. While our use of priority queues does not improve the worstcase complexity, it appears to lead to an efficient technique. In the majority of our tests Blossom V outperformed previous implementations of Cook and Rohe (INFORMS J Comput 11(2):138–148, 1999) and Mehlhorn and Schäfer (J Algorithmics Exp (JEA) 7:4, 2002), sometimes by an order of magnitude. We also show that for large VLSI instances it is beneficial to update duals by solving a linear program, contrary to a conjecture by Cook and Rohe.
A Practical Minimum Spanning Tree Algorithm Using the Cycle Property \Lambda
, 2002
"... Abstract We present a new algorithm for computing minimum spanning trees that is more than two times faster than the best previously known algorithms (For dense, "difficult " inputs). It is of conceptual interest that the algorithm uses the property that the heaviest edge in a cycl ..."
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Abstract We present a new algorithm for computing minimum spanning trees that is more than two times faster than the best previously known algorithms (For dense, &quot;difficult &quot; inputs). It is of conceptual interest that the algorithm uses the property that the heaviest edge in a cycle can be discarded. Previously this has only been exploited in asymptotically optimal algorithms that are considered to be impractical. An additional advantage of the algorithm is that it can greatly profit from pipelining to hide memory access latencies. 1 Introduction Given an undirected connected graph G with n nodes, m edges and nonnegative edge weights, the minimum spanning tree (MST) problem asks for a minimum total weight subset of the edges that forms a spanning tree of G. The current state of the art in MST algorithms shows a gap between theory and practice. The algorithms used in practice are among the oldest network algorithms [1, 4, 8] and are all based on the partition property: a lightest edge leaving a set of nodes can be used for an MST. More specifically, Kruskal's algorithm [8] is best for sparse graphs. Its running time is asymptotically dominated by the time for sorting the edges by weight. For dense graphs (m AE n),