In this paper, we study the convergence behavior of distributed iterative algorithms with quantized message passing. We first introduce a general iterative function evaluation algorithms for solving fixed point proble...
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ISBN:
(纸本)9781424497218
In this paper, we study the convergence behavior of distributed iterative algorithms with quantized message passing. We first introduce a general iterative function evaluation algorithms for solving fixed point problems distributively. We then analyze the convergence of the distributed algorithms, e.g. Jacobi scheme and Gauss-Seidel scheme, under the quantized message passing. Based on the closed-form convergence performance derived, we propose two quantizer designs, namely the time invariant convergence-optimal quantizer (TICOQ) and the time varying convergence-optimal quantizer (TVCOQ) to minimize the effect of the quantization error on the convergence. We also study the tradeoff between the convergence error and message passing overhead for both TICOQ and TVCOQ. As an example, we apply the TICOQ and TVCOQ designs to the iterative waterfilling algorithm of MIMO interference game.
Dominating sets are one of the most important graph theoretic structures used in clustering and routing protocols in wireless sensor networks. For this reason, many distributed dominating set algorithms have been stud...
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ISBN:
(纸本)9781728119045
Dominating sets are one of the most important graph theoretic structures used in clustering and routing protocols in wireless sensor networks. For this reason, many distributed dominating set algorithms have been studied by researchers. Since wireless sensor networks are usually composed of battery powered devices, it is important to minimize the resource consumption of dominating set algorithms. In this study, experimental performance of four synchronous distributed dominating set algorithms including 1-hop span-based, 2-hop span-based, random and identity (ID)-based are measured in SimPy environment in terms of time, message, cluster size and energy per node. While performing these tests, the node count and the average node degree in the wireless sensor networks are changed and a large number of measurements are taken to compare the algorithms. As a result of the detailed experiments, 2-hop span-based algorithm has the best results in terms of set size and the ID-based algorithm has the best results in terms of resource utilization.
In this paper, relations between distributed consensus-based optimization and a network resource allocation problem are considered. It is shown that first-order gradient algorithm for distributed consensus-based optim...
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ISBN:
(纸本)9781665422581
In this paper, relations between distributed consensus-based optimization and a network resource allocation problem are considered. It is shown that first-order gradient algorithm for distributed consensus-based optimization can be used for finding an optimal solution of distributed resource allocation with synchronous protocol under weaker assumptions than those given in the literature. Moreover, second-order gradient algorithm for distributed consensus-based optimization is presented that can be employed for solving distributed resource allocation problems. As a result, several algorithms used for distributed consensus-based optimization can now be applied to derive distributed algorithms for resource allocation. It is shown that first and second order gradient algorithms for distributed resource allocation can be utilized for finding an optimal solution of distributed consensus-based optimization as well. The results presented in this paper can be applied to time-varying or random directed networks with or without synchronous protocols with arbitrary initialization. Finally, a numerical example of a distributed estimation in wireless sensor networks is given to demonstrate the results.
Wireless sensor networks have a wide range of applications because they can be adapted for various environments. They can operate independently in harsh places where a human presence is risky or even impossible. Since...
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ISBN:
(纸本)9781479980888
Wireless sensor networks have a wide range of applications because they can be adapted for various environments. They can operate independently in harsh places where a human presence is risky or even impossible. Since their life time is dependent on their batteries and replacing or recharging their batteries is impossible in rough places, it is necessary to find energy efficient routing protocols for them. In this paper, a number of well-know energy efficient routing algorithms for WSNs have been classified and presented based on their attributes.
distributed Approximation is a new and rapidly developing discipline that lies at the crossroads of various well-established areas of Computer Science - distributed Computing, Approximation algorithms, Graph Theory an...
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distributed Approximation is a new and rapidly developing discipline that lies at the crossroads of various well-established areas of Computer Science - distributed Computing, Approximation algorithms, Graph Theory and often, Computational Geometry. This thesis focuses on the design and analysis of distributed algorithms to solve optimization problems that usually arise in large-scale, heavily dynamic, resource constrained networks, e.g. wireless ad-hoc and sensor networks, P2P systems, mobile networks etc. These problems can often be abstracted by variations of well-known combinatorial optimization problems, such as topology control, clustering etc. Many of these problems are known to be hard (NP-complete). But we need fast and light-weight distributed algorithms for these problems, that yield near-optimal solutions. The results presented in this thesis can be broadly divided in two parts. The first part contains a set of results that obtain improved solutions to the classic problem of computing a sparse “backbone” for Wireless Sensor Networks (WSNs). In graph-theoretic terms, the goal is to compute a spanning subgraph of the input graph, that is sparse, lightweight and has low stretch. The term “low stretch” indicates that in spite of dropping many edges, the distance between any two nodes in the graph is not increased by much. We model WSNs as geometric graphs - unit ball graphs, quasi-unit ball graphs etc. in Euclidean spaces, as well as in more general metric spaces of low doubling dimension. We identify and exploit a variety of geometric features of those models to obtain our results. In the second part of the thesis we focus on distributed algorithms for clustering problems. We present several distributed approximation algorithms for clustering problems (e.g., minimum dominating set, facility location problems) that improve on best known results so far. The main contribution here is the design of distributed algorithms where the running time is a “tunable” pa
In recent years, with the rapid expansion of electric vehicles, charging service market also rapid development, the construction of charging station operation will be dominated by the government gradually to various s...
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ISBN:
(数字)9798331506797
ISBN:
(纸本)9798331506803
In recent years, with the rapid expansion of electric vehicles, charging service market also rapid development, the construction of charging station operation will be dominated by the government gradually to various social capital in investment construction, different charging stations belonging to different charging station operators, in order to maximize their own interests, a competitive relationship between the charging stations. In order to solve the problem of multi-subject interest balance in the charging market scenario composed of multiple charging stations and multiple electric vehicle (EV) travelers, it is necessary to study the charging price pricing method of calculating the interest game of charging stations. Therefore, this paper proposes the EV charging pile pricing method optimized based on the particle swarm algorithm to solve the problem that the charging price pricing method cannot take into account the multi-party interests of charging stations and EV travelers.
The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and engineering in which candidate solutions, or “individuals”, are manipulated in ways analogous to biological evolution...
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The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and engineering in which candidate solutions, or “individuals”, are manipulated in ways analogous to biological evolution, to produce new solutions until one with the desired quality is found. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of genetic algorithms.
This thesis proposes a new distributed architecture for genetic algorithms, based on distributed storage of the individuals in a persistent pool. Processors extract individuals from the pool in order to perform the computations and then insert the resulting individuals back into the pool. Unlike previously proposed approaches, the new approach is tailored for distributed systems in which processors are loosely coupled, failure-prone and can run at different speeds. Proof-of-concept simulation results are presented for four benchmark functions and for a real-world Product Lifecycle Design problem. We have experimented with both the crash failure model and the Byzantine failure model. The results indicate that the approach can deliver improved performance due to the distribution and tolerates a large fraction of processor failures subject to both models.
Self-Reconfigurable Modular Robots typically consist of high number of modules with uniform docking interfaces, allowing them to transform into various shape. Recognizing the shape of such a system composed of hundred...
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ISBN:
(数字)9798331519957
ISBN:
(纸本)9798331519964
Self-Reconfigurable Modular Robots typically consist of high number of modules with uniform docking interfaces, allowing them to transform into various shape. Recognizing the shape of such a system composed of hundreds of modules is a significant challenge. Given a new configuration, a modular robots system must be able to determine and update its shape dynamically and in a distributed manner. In a previous work, we developed an algorithm that identifies overlapping boxes to cover the entire robot configuration through message-passing, enabling robots to determine a representation of their current shape. However, this algorithm was static and did not react to changes in real time. In this paper, we introduce an updated shape recognition algorithm that dynamically and in real-time recognizes the addition of modules to update the shape description of the entire configuration using local information. The dynamic algorithm to update the shape description is tested in a simulated environment and compared to re-executing the shape recognition algorithm on the whole configuration. The results show the efficiency of our algorithm in updating the robot’s current shape in real time.
In this paper, we consider the all best swap edges problem in a distributed environment. We are given a 2-edge connected positively weighted network X, where all communication is routed through a rooted spanning tree ...
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In this paper, we consider the all best swap edges problem in a distributed environment. We are given a 2-edge connected positively weighted network X, where all communication is routed through a rooted spanning tree T of X. If a tree edge e = {x, y} fails, the communication network will be disconnected. However, since X is 2-edge connected, communication can be restored by replacing e by non-tree edge e', called a swap edge of e, whose ends lie in different components of T \ {e}. Of all possible swap edges of e, we would like to choose the best, according to four different objective functions. Overall, the problem is to identify the best swap edge for every tree edge, so that in case of any edge failure, the best swap edge can be activated quickly. There are solutions to this problem for a number of cases in the literature. A major concern for all these solutions is to minimize the number of messages. However, especially in fault-transient environments, time is a crucial factor. In this paper we present a novel technique that addresses this problem from a time perspective;in fact, we present a distributed solution that works in linear time with respect to the height h of T for a number of different criteria, while retaining the optimal number of messages and O (delta(x)) space per each processor x of degree delta(x). To the best of our knowledge, there is no prior algorithm for the all best swap edges problem whose asymptotic complexity matches ours in all three measures: time, space, and number of messages. (C) 2020 Elsevier B.V. All rights reserved.
With the advance in mobile network-based systems, dynamic system has become one of the hotspots in fundamental study of distributed systems. In this article, we consider the dynamic system with frequent topology chang...
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With the advance in mobile network-based systems, dynamic system has become one of the hotspots in fundamental study of distributed systems. In this article, we consider the dynamic system with frequent topology changes arising from node mobility or other reasons, which is also referred to as dynamic network. With the model of dynamic network, fundamental distributed computing problems, such as information dissemination and election, can be formally studied with rigorous correctness. Our work focuses on the node counting problem in dynamic environments. We first define two new dynamicity models, named (Q, S)-distance and (Q, S)*-distance, which describe dynamic changes of information propagation time against topology changes. Based on these two models, we design three different counting algorithms which basically adopt the approach of diffusing computation. These algorithms mainly differ in communication cost due to different information collection procedures. The correctness of all the algorithms is formally proved and their performance is evaluated via both theoretical analysis and experimental simulations.
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