In recent years we have witnessed strong development and widespread use of powerful wirelessly connected platforms, thus the set of the related problems that need to be solved by distributed algorithms is growing rapi...
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In recent years we have witnessed strong development and widespread use of powerful wirelessly connected platforms, thus the set of the related problems that need to be solved by distributed algorithms is growing rapidly. Some of them present large obstacles in harnessing the full potential of this new technology, so there is an imminent need for a fast and easy evaluation of new ideas and approaches. Simulation is a fundamental part of distributed algorithm design and evaluation process. In this paper, we present a library for event-based simulation and evaluation of distributed algorithms. This library provides a set of simple but powerful tools with a goal to ease virtual setup of a complex system such as a distributed network of communicating entities and to define, simulate, and analyze its behavior. In order to reduce a huge problem space inherent in such systems, our library is using a high level of abstraction. This is made possible by a strict and complete definition of the distributed computing environment. The library is implemented in Python whose simple and expressive syntax provides a possibility of minimal implementations and a mild learning curve. In addition to executing automated simulations or larger experiments, the library fully supports interactive mode along with a step-by-step execution, which can be a very powerful combination.
This paper proposes a discrete-time, distributed algorithm for multi-agent networks to achieve the minimum l 1 -norm solution to a group of linear equations known to possess a family of solutions. We assume each agent...
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This paper proposes a discrete-time, distributed algorithm for multi-agent networks to achieve the minimum l 1 -norm solution to a group of linear equations known to possess a family of solutions. We assume each agent in the network knows only one equation and can communicate with only its neighbors. The algorithm is developed based on a combination of the projection-consensus idea and the sub-gradient descent method. Given the underlying network graph to be directed and strongly connected, we prove that the algorithm enables all agents to achieve a common minimum l 1 -norm solution. The major difficulty to be dealt with is the non-smooth nature of the norm and the lack of strict convexity of the associated relevant performance index.
The graph exploration problem requires a group of mobile robots, initially placed arbitrarily on the nodes of a graph, to work collaboratively to explore the graph such that each node is eventually visited by at least...
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ISBN:
(数字)9783030624019
ISBN:
(纸本)9783030624002;9783030624019
The graph exploration problem requires a group of mobile robots, initially placed arbitrarily on the nodes of a graph, to work collaboratively to explore the graph such that each node is eventually visited by at least one robot. One important requirement of exploration is the termination condition, i.e., the robots must know that exploration is completed. The problem of live exploration of a dynamic ring using mobile robots was recently introduced in [Di Luna et al., ICDCS 2016]. In it, they proposed multiple algorithms to solve exploration in fully synchronous and semi-synchronous settings with various guarantees when 2 robots were involved. They also provided guarantees that with certain assumptions, exploration of the ring using two robots was impossible. An important question left open was how the presence of 3 robots would affect the results. In this paper, we try to settle this question in a fully synchronous setting and also show how to extend our results to a semisynchronous setting. In particular, we present algorithms for exploration with explicit termination using 3 robots in conjunction with either (i) unique IDs of the robots and edge crossing detection capability (i.e., two robots moving in opposite directions through an edge in the same round can detect each other), or (ii) access to randomness. The time complexity of our deterministic algorithm is asymptotically optimal. We also provide complementary impossibility results showing that there do not exist any explicit termination algorithms for 2 robots even when each robot has a unique ID, edge crossing detection capability, and access to randomness. We also present an algorithm to achieve exploration with partial termination using 3 robots with unique IDs in the semi-synchronous setting.
We consider standard T-interval dynamic networks, under the synchronous timing model and the broadcast CONGEST model. In a T-interval dynamic network, the set of nodes is always fixed and there are no node failures. T...
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ISBN:
(纸本)9781450369350
We consider standard T-interval dynamic networks, under the synchronous timing model and the broadcast CONGEST model. In a T-interval dynamic network, the set of nodes is always fixed and there are no node failures. The edges in the network are always undirected, but the set of edges in the topology may change arbitrarily from round to round, as determined by some adversary and subject to the following constraint: For every T consecutive rounds, the topologies in those rounds must contain a common connected spanning subgraph. Let H-r to be the maximum (in terms of number of edges) such subgraph for round r through r + T - 1. We define the backbone diameter d of a T-interval dynamic network to be the maximum diameter of all such H-r's, for r >= 1. We use n to denote the number of nodes in the network. Within such a context, we consider a range of fundamental distributed computing problems including CouNT/MAx/MEDIAN/Sum/LEADERELECT/CONSENSUS/CONFIRMEDFLOOD. Existing algorithms for these problems all have time complexity of Omega(n) rounds, even for T = infinity and even when d is as small as O(1). This paper presents a novel O (d(3) log(2) n) deterministic algorithm for computing COUNT, for T-interval dynamic networks with T >= c . d(2) log(2) n. Here c is a (sufficiently large) constant independent of d, n, and T. To our knowledge, our algorithm is the very first such algorithm whose complexity does not contain a Theta(n) term. For d = O(n(a)) with constant a < 1/3, our deterministic algorithm has o(n) complexity, which is better than all (both randomized and deterministic) existing COUNT algorithms in this setting. For d = O(polylog(n)), our algorithm is exponentially faster. Following the framework of our COUNT algorithm, this paper further develops novel algorithms for solving MAX/MEDIAN/SUM/LEADERELECT/CONSENSUS/CONFIRMEDFLOOD, while incurring either O (d(3) log(2) n) or O(d(3) log(3) n) complexity. Again, for all these problems, our algorithms are the first ones
Recently, distributed algorithms for power system state estimation have attracted significant attention. Along with such advantages as decomposition, parallelization of the original problem and absence of a central co...
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Recently, distributed algorithms for power system state estimation have attracted significant attention. Along with such advantages as decomposition, parallelization of the original problem and absence of a central computation unit, distributed state estimation may also serve for local information privacy reasons since the only information to be transferred is the boundary states of neighboring areas. In this paper, we propose some novel approaches for speeding up the ADMM-based distributed state estimation algorithms by utilizing some recent results in optimization theory. We also thoroughly analyze the theoretical and practical performance, concluding that accelerated approach outperforms the existing ones. The theoretical considerations are verified through the experiments on a scalable example.
In this paper, we consider optimization problems involving multiple agents. Each agent introduces its own constraints on the optimization vector, and the constraints of all agents depend on a common source of uncertai...
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In this paper, we consider optimization problems involving multiple agents. Each agent introduces its own constraints on the optimization vector, and the constraints of all agents depend on a common source of uncertainty. We suppose that uncertainty is known locally to each agent through a private set of data (multi-agent scenarios), and that each agent enforces its scenario-based constraints to the solution of the multi-agent optimization problem. Our goal is to assess the feasibility properties of the corresponding multi-agent scenario solution. In particular, we are able to provide a priori certificates that the solution is feasible for a new occurrence of the global uncertainty with a probability that depends on the size of the datasets and the desired confidence level. The recently introduced wait-and-judge approach to scenario optimization and the notion of support rank are used for this purpose. Notably, decision-coupled and constraint-coupled uncertain optimization programs for multi-agent systems fit our framework and, hence, any distributed optimization scheme to solve the associated multi-agent scenario problem can be accompanied with our a priori probabilistic feasibility certificates.
We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles following the well-celebrated classic oblivious robots model. We study the fundamental problem of gather...
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We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles following the well-celebrated classic oblivious robots model. We study the fundamental problem of gathering N autonomous robots on a plane, which requires all robots to meet at a single point (or to position within a small area) that is not known beforehand. We consider limited visibility under which robots are only able to see other robots up to a constant Euclidean distance and focus on the time complexity of gathering by robots under limited visibility. There exists an O(DG) time algorithm for this problem in the fully synchronous setting, assuming that the robots agree on one coordinate axis (say north), where DG is the diameter of the visibility graph of the initial configuration. In this article, we provide the first O(DE) time algorithm for this problem in the asynchronous setting under the same assumption of robots' agreement with one coordinate axis, where DE is the Euclidean distance between farthest-pair of robots in the initial configuration. The runtime of our algorithm is a significant improvement since for any initial configuration of N & GE;1 robots, DE & LE;DG, and there exist initial configurations for which DG can be quadratic on DE, i.e., DG=& UTheta;(DE2). Moreover, our algorithm is asymptotically time-optimal since the trivial time lower bound for this problem is omega(DE).
In speech processing applications, e.g., speech recognition, hearing aids (HAs), video conferencing, and human-computer interaction, speech enhancement or noise reduction is an essential front-end task, as the recorde...
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In speech processing applications, e.g., speech recognition, hearing aids (HAs), video conferencing, and human-computer interaction, speech enhancement or noise reduction is an essential front-end task, as the recorded speech signals are inevitably corrupted by interference, including coherent/incoherent noise and reverberation. Traditional noise reduction algorithms are mostly based on spatial filtering techniques using a microphone array. The performance of the noise reduction algorithms scales with the number of microphones that are involved in filtering, but a large-sized microphone array cannot be mounted in many realistic systems, e.g., HAs. In the last few decades, with a great development in micro-electro-mechanical systems, wireless devices are more and more commonly-used in our daily life, like the smartphone, laptop, wireless HA, and ipad. These devices have acoustic sensors equipped and a capability of wireless communication, leading to a wireless acoustic sensor network (WASN). The WASN can be organized in a centralized fashion where all the devices are only allowed to connect with a fusion center (FC), or in a decentralized way where the devices are connected with the close-by counterparts via wireless links. ThisWASN can resolve the disadvantages of the traditional microphone array systems, since thewireless devices can be placed anywhere in the vicinity and one device is able to make use of measurements from other external devices. More importantly, the acoustic scene can be sampled more comprehensively, resulting in a potential improvement in noise reduction performance.
Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus befo...
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Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case.
We study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the agents, ...
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We study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the agents, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value that is equal to the quantized average. We conclude with comparisons against existing quantized average consensus algorithms that illustrate the performance and potential advantages of the proposed algorithm.
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