Topic
Distributed algorithm
About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.
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01 Oct 2004TL;DR: This is the first work that provides a security-aware range-independent localization scheme for WSN, and it is shown that SeRLoc is robust against severe WSN attacks, such as the wormhole attack, the sybil attack and compromised sensors.
Abstract: In many applications of wireless sensor networks (WSN), sensors are deployed un-tethered in hostile environments. For location-aware WSN applications, it is essential to ensure that sensors can determine their location, even in the presence of malicious adversaries. In this paper we address the problem of enabling sensors of WSN to determine their location in an un-trusted environment. Since localization schemes based on distance estimation are expensive for the resource constrained sensors, we propose a range-independent localization algorithm called SeRLoc. SeRLoc is distributed algorithm and does not require any communication among sensors. In addition, we show that SeRLoc is robust against severe WSN attacks, such as the wormhole attack, the sybil attack and compromised sensors. To the best of our knowledge, ours is the first work that provides a security-aware range-independent localization scheme for WSN. We present a threat analysis and comparison of the performance of SeRLoc with state-of-the-art range-independent localization schemes.
473 citations
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05 Aug 1997TL;DR: This work proposes a Metacomputing Directory Service that provides efficient and scalable access to diverse, dynamic, and distributed information about resource structure and state and defines an extensible data model to represent required information and presents a scalable, high-performance, distributed implementation.
Abstract: High-performance execution in distributed computing environments often requires careful selection and configuration not only of computers, networks, and other resources but also of the protocols and algorithms used by applications. Selection and configuration in turn require access to accurate, up-to-date information on the structure and state of available resources. Unfortunately no standard mechanism exists for organizing or accessing such information. Consequently different tools and applications adopt ad hoc mechanisms, or they compromise their portability and performance by using default configurations. We propose a Metacomputing Directory Service that provides efficient and scalable access to diverse, dynamic, and distributed information about resource structure and state. We define an extensible data model to represent required information and present a scalable, high-performance, distributed implementation. The data representation and application programming interface are adopted from the Lightweight Directory Access Protocol; the data model and implementation are new. We use the Globus distributed computing toolkit to illustrate how this directory service enables the development of more flexible and efficient distributed computing services and applications.
467 citations
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TL;DR: This paper identifies a class of smooth functions for which one can synthesize in a systematic way distributed algorithms that achieve consensus, applies this result to the family of weighted power mean functions, and characterize the exponential convergence properties of the resulting algorithms.
465 citations
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TL;DR: This work presents the first massively distributed architecture for deep reinforcement learning, using a distributed neural network to represent the value function or behaviour policy, and a distributed store of experience to implement the Deep Q-Network algorithm.
Abstract: We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN). Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.
464 citations
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26 Apr 2004TL;DR: An evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab is presented, demonstrating that the distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.
Abstract: We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.
463 citations