Efficient distributed selection with bounded messages

Negro, Alberto; Santoro, Nicola; Urrutia, Jorge

doi:10.1109/71.588617

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…Different solutions and bounds exist for the distributed selection problem in the point-to-point network depending on the topology of the network ( [2], [8], and [14]). A general selection algorithm is developed using packets and its packet complexity is shown to be a significant improvement for a large range of packet sizes over the existing bounds [9]. It is bit optimal in star networks.…”

Section: Introductionmentioning

confidence: 95%

Improvement and performance analysis on statistical selection algorithms

Lun

Othman

2006

2006 International Conference on Computing &Amp; Informatics

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Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo and Choi has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process [4]. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This new algorithm is developed based on the simulation of the real multicast environment. Three modifications have been added in the new algorithm to solve the problem. Two performance measures have been conducted for the purpose of performance analysis between original and new algorithm.

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Section: Introductionmentioning

confidence: 95%

Improvement and performance analysis on statistical selection algorithms

Lun

Othman

2006

2006 International Conference on Computing &Amp; Informatics

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“…Distributed selection has been studied in general networks [15]. Recently, Kuhn et al [11] studied the distributed selection for general networks with n nodes and diameter D. They proved that distributed selection is strictly harder than convergecast by giving a lower bound of Ω(D log D n) on the time complexity.…”

Section: Related Workmentioning

confidence: 99%

“…We show that the message complexity of finding the median is no more than min{N + 4n C log f k , 4n log f k } based on methods in [15], [16] for wired networks. Here n C is the size of the connected dominating set and f k is the value of the k th smallest data.…”

Section: Other Modelsmentioning

confidence: 99%

Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks

Wang

2011

IEEE Trans. Comput.

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Abstract-Processing the gathered information efficiently is a key functionality for wireless sensor networks. In this article, we study the time complexity, message complexity (number of messages used by all nodes), and energy cost complexity (total energy used by all nodes for transmitting messages) of some tasks, such as data collection (collecting raw data of all nodes to a sink), data aggregation (computing the aggregated value of data of all nodes), and queries for a multi-hop wireless sensor network of n nodes. We first present a lower-bound on the complexity for the optimal methods, and then, for most of the tasks studied in this article, we provide an (asymptotically matching) upper-bound on the complexity by presenting efficient distributed algorithms to solve these problems.

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“…Information aggregation in a static network has been studied as well, e.g., in [15,19,21]. This allows to compute any function, depending on some initial values, at one node.…”

Section: Further Related Workmentioning

confidence: 99%

Distributed computing in fault-prone dynamic networks

Brandes

Heide

2012

Proceedings of the 4th International Workshop on Theoretical Aspects of Dynamic Distributed Systems

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Dynamics in networks is caused by a variety of reasons, like nodes moving in 2D (or 3D) in multihop cellphone networks, joins and leaves in peer-to-peer networks, evolution in social networks, and many others In order to understand such kinds of dynamics, and to design distributed algorithms that behave well under dynamics, many ways to model dynamics are introduced and analyzed w.r.t. correctness and efficiency of distributed algorithms. In [16], Kuhn, Lynch, and Oshman have introduced a very general, worst case type model of dynamics: The edge set of the network may change arbitrarily from step to step, the only restriction is that it is connected at all times and the set of nodes does not change. An extended model demands that a fixed connected subnetwork is maintained over each time interval of length T (T -interval dynamics). They have presented, among others, algorithms for counting the number of nodes under such general models of dynamics.In this paper, we generalize their models and algorithms by adding random edge faults, i.e., we consider fault-prone dynamic networks: We assume that an edge currently existing may fail to transmit data with some probability p. We first observe that strong counting, i.e., each node knows the correct count and stops, is not possible in a model with random edge faults. Our main two positive results are feasibility and runtime bounds for weak counting, i.e., stopping is no longer required (but still a correct count in each node), and for strong counting with an upper bound, i.e., an upper bound N on n is known to all nodes.

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Efficient distributed selection with bounded messages

Cited by 17 publications

References 12 publications

Improvement and performance analysis on statistical selection algorithms

Improvement and performance analysis on statistical selection algorithms

Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks

Distributed computing in fault-prone dynamic networks

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