We analyze average-based distributed algorithms relying on simple and pairwise random interactions among a large and unknown number of anonymous agents. This allows the characterization of global properties emerging f...
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We analyze average-based distributed algorithms relying on simple and pairwise random interactions among a large and unknown number of anonymous agents. This allows the characterization of global properties emerging from these local interactions. Agents start with an initial integer value, and at each interaction keep the average integer part of both values as their new value. The convergence occurs when, with high probability, all the agents possess the same value, which means that they all know a property of the global system. Using a well-chosen stochastic coupling, we improve upon existing results by providing explicit and tight bounds on the convergence time. We apply these general results to both the proportion problem and the system size problem.
Recent advances in wireless communications along with developments in low-power circuit design and micro-electro mechanical systems (MEMS) have heralded the advent of compact and inexpensive wireless micro-sensor devi...
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Recent advances in wireless communications along with developments in low-power circuit design and micro-electro mechanical systems (MEMS) have heralded the advent of compact and inexpensive wireless micro-sensor devices. A large network of such sensor nodes capable of communicating with each other provides significant new capabilities for automatically collecting and analyzing data from physical environments.
A notable feature of these networks is that more nodes than are strictly necessary may be deployed to cover a given region. This permits the system to provide reliable information, tolerate many types of faults, and prolong the effective service time. Like most wireless systems, achieving low power consumption is a key consideration in the design of these networks. This thesis presents algorithms for managing power at the distributed system level, rather than just at the individual node level. These distributed algorithms allocate work based on user requests to the individual sensor nodes that comprise the network. The primary goal of the algorithms is to provide a robust and scalable approach for tasking nodes that prolongs the effective life of the network.
Theoretical analysis and simulation results are presented to characterize the behavior of these algorithms. Results obtained from simulation experiments indicate that the algorithms can achieve a significant increase in the life of the network. In some cases this may be by an order of magnitude. The algorithms are also shown to ensure a good quality of sensor coverage while improving the network life. Finally, they are shown to be robust to faults and scale to large numbers of nodes.
Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of ...
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Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of.. we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly. Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.
The articles in this special issue focus on security and privacy of distributed algorithms and network systems. The integration of computation, communication, and control technologies has led to the widespread emergen...
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The articles in this special issue focus on security and privacy of distributed algorithms and network systems. The integration of computation, communication, and control technologies has led to the widespread emergence of large scale engineering systems. These network systems are deployed in numerous fields, including electric and smart grids, transportation and smart cities, health-care and manufacturing, and so forth. Due to the rapid growth in number of geographically deployed units, such as sensors, computers and controllers, traditional centralized control and optimization algorithms may not be efficient, robust, or even applicable at all. Instead, distributed control and optimization algorithms offer a promising and desirable approach to operate and guarantee the well functioning of network systems. The aim of this special issue is to provide a window into the recent developments of the fundamentals and applications of security and privacy of distributed algorithms in network systems.
In this paper we show how quantitative program logic (Morgan et al., ACM Trans. Programming Languages Systems 18 (1996) 325) provides a formal framework in which to promote standard techniques of program analysis to a...
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In this paper we show how quantitative program logic (Morgan et al., ACM Trans. Programming Languages Systems 18 (1996) 325) provides a formal framework in which to promote standard techniques of program analysis to a context where probability and nondeterminism interact, a situation common to probabilistic distributed algorithms. We show that overall expected time can be formulated directly in the logic and that it can be derived from local properties of components. We illustrate the methods with an analysis of expected running time of the probabilistic dining philosophers (Lehmann and Ravin, Proc 8th Annu. ACM. Symp. on principles of Programming Languages, ACM, New York, 1981, p. 133). (C) 2002 Published by Elsevier Science B.V.
We present the problem of finding a maximal independent set (MIS) (named as MIS Filling problem) of an arbitrary connected graph having n vertices with luminous myopic mobile robots. The robots enter the graph one aft...
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We present the problem of finding a maximal independent set (MIS) (named as MIS Filling problem) of an arbitrary connected graph having n vertices with luminous myopic mobile robots. The robots enter the graph one after another from a particular vertex called the Door and move along the edges of the graph without collision to occupy vertices such that the set of occupied vertices form a maximal independent *** this paper, we explore two versions of the MIS filling problem, where two separate algorithms are proposed. First version of the problem considers a Single Door where our IND algorithm forms an MIS of size m in O (m(2)) epochs under an asynchronous scheduler, where an epoch is the smallest time interval in which each participating robot gets activated at least once and performs a Look-Compute-Move cycle. The robots have three hops of visibility range, Delta + 8 number of colors, and O (log Delta) bits of persistent storage, where A is the maximum degree of the graph. The second version of the problem has Multiple Doors for which we present our MULTIND algorithm that forms an MIS in O (m(2)) epochs under a semi-synchronous scheduler using robots with five hops of visibility range, Delta + k + 7 number of colors, and O (log(Delta + k)) bits of persistent storage, where k is the number of Doors. We also achieve a lower bound of Omega(n) for MIS filling problem with Single Door, where n is the number of vertices of the graph.(c) 2023 Elsevier B.V. All rights reserved.
Packet scheduling is a particular challenge in wireless networks due to interference from nearby transmissions. A distance-2 interference model serves as a useful abstraction here, and we study packet routing and sche...
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Packet scheduling is a particular challenge in wireless networks due to interference from nearby transmissions. A distance-2 interference model serves as a useful abstraction here, and we study packet routing and scheduling under this model of interference. The main focus of our work is the development of fully distributed (decentralized) protocols. We present polylogarithmic/constant factor approximation algorithms for various families of disk graphs (which capture the geometric nature of wireless-signal propagation), as well as near-optimal approximation algorithms for general graphs. A basic distributed coloring procedure, originally due to Luby [1993] (Journal of Computer and System Sciences, 47:250-286, 1993), underlies many of our algorithms. The work of Finocchi et al. [2002] (Proc. ACM-SIAM Symposium on Discrete algorithms, 2002) showed that a natural modification of this algorithm leads to improved performance. A rigorous explanation of this was left as an open question, and we prove that the modified algorithm is indeed provably better in the worst case.
This paper investigates the problem of finding a fixed point for a global nonexpansive operator under time-varying communication graphs in real Hilbert spaces, where the global operator is separable and composed of an...
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This paper investigates the problem of finding a fixed point for a global nonexpansive operator under time-varying communication graphs in real Hilbert spaces, where the global operator is separable and composed of an aggregate sum of local nonexpansive operators. Each local operator is only privately accessible to each agent, and all agents constitute a network. To seek a fixed point of the global operator, it is indispensable for agents to exchange local information and update their solution cooperatively. To solve the problem, two algorithms are developed, called distributed Krasnosel'ski- Mann (D-KM) and distributed block-coordinate Krasnosel'ski-Mann (D-BKM) iterations, for which D-BKM is a block-coordinate version of D-KM in the sense of randomly choosing and computing only one block-coordinate of local operators at each time for each agent. It is shown that the proposed two algorithms can both converge weakly to a fixed point of the global operator. Meanwhile, the designed algorithms are applied to recover the classical distributed gradient descent (DGD) algorithm, devise a new block-coordinate DGD algorithm, handle a distributed shortest distance problem in the Hilbert space for the first time, and solve linear algebraic equations in a novel distributed approach. Finally, the theoretical results are corroborated by a few numerical examples. (c) 2020 Elsevier Ltd. All rights reserved.
We consider solutions for distributed multicommodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. We show first distributed solutions that allow (1 + epsilon)...
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We consider solutions for distributed multicommodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. We show first distributed solutions that allow (1 + epsilon) approximation and whose convergence time is essentially linear in the maximal path length, and is independent of the number of commodities and the size of the graph. Our algorithms use a very natural approximate steepest descent framework, combined with a blocking flow technique to speed up the convergence in distributed and parallel environment. Previously known solutions that achieved comparable convergence time and approximation ratio required exponential computational and space overhead per agent.
We present novel results on and efficient deterministic as well as randomized synchronous message-passing distributed algorithms for generalized graph alliances, a new concept incorporating and expanding previous ones...
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ISBN:
(纸本)9783642245497;9783642245503
We present novel results on and efficient deterministic as well as randomized synchronous message-passing distributed algorithms for generalized graph alliances, a new concept incorporating and expanding previous ones. An alliance is here a group of nodes of a connected network or a population fulfilling certain thresholds for their neighbourhood. More precisely, every node outside and inside the alliance must have a minimum number of neighbours inside the alliance. A threshold function defining this number may be specific to each node. We are interested in finding minimal alliances of generalized type: the threshold function might be any. We also investigate conditions in which it is possible to have anonymity, a praised property in population protocols.
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