Nowadays, with the development of micro-electro-mechanical technologies, sweep coverage with mobile sensors is more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such a...
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Nowadays, with the development of micro-electro-mechanical technologies, sweep coverage with mobile sensors is more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such as message ferrying and data routing in ad-hoc networks. In order to reduce the sweep cycle and the number of mobile sensors, we propose the Distance-Sensitive-Route-Scheduling (DSRS) problem, which is to consider the effect of sensing range. We prove that DSRS is NP-hard, and consider three different scenarios: the single sensing-point case, the general case, and the extended case. In the single sensing-point case, we propose an approximation algorithm ROSE to schedule the routes of the mobile sensors efficiently. For the general case and the extended case, we present two other approximation algorithms G-ROSE and E-ROSE based on ROSE. We further characterize the non-locality property and design a distributed algorithm D-ROSE, coordinating sensors to meet the sweep requirements with best effort. Our algorithms are scalable to different sweep coverage problems, and according to the simulation results, they greatly outperform other existing algorithms up to 45 percent especially with a large sensing range.
This study considers the constrained consensus problem of continuous-time multi-agent networks with hypercube state constraints, non-convex input constraints and non-uniform delays. It is assumed that each agent can o...
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This study considers the constrained consensus problem of continuous-time multi-agent networks with hypercube state constraints, non-convex input constraints and non-uniform delays. It is assumed that each agent can only perceive its own constraint sets, the communication graphs are switching over time and the joint communication graphs are strongly connected. By introducing the time-varying gains and the constraint operators, a new distributed algorithm is proposed. Then, it is proved that the constraint consensus can be reached under the proposed algorithm by reduction, while the states and the inputs are constrained to stay at the corresponding constraint sets. Finally, simulation examples are given to examine the effectiveness of the proposed results.
The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number o...
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The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number of interconnected microgrids. Thus, the optimal power flow (OPF) problem should be studied by properly taking account of the coupling between the microgrids. In this paper, the models of standalone microgrid and coupled microgrids are formulated first. Then, a decentralized approach is shown in detail to cooperatively solve the coupled OPF problem. Specifically, each microgrid solves the local OPF problem, leading to the optimal solution of the coupled OPF problem via negotiation between microgrids. The privacy of each microgrid is also preserved since no sensitive information is shared between microgrids. Finally, simulation results show that the optimality gaps between the proposed approach and an existing solver are small for both the AC OPF and the DC OPF. Furthermore, the total cost of the microgrids is reduced by the cooperative OPF.
In recent years, wireless sensor network (WSN) is one of the rapidly growing area in various domains such as disaster relief operation, military applications, health applications, etc. In WSN, to eliminate the redunda...
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In recent years, wireless sensor network (WSN) is one of the rapidly growing area in various domains such as disaster relief operation, military applications, health applications, etc. In WSN, to eliminate the redundant information from the gathered data, data aggregation is a prime approach by which we can considerably reduce the energy consumption of each node. This reduction supports the achievement of the higher lifetime of WSN. In this paper, we have endeavored to introduce a review of various existing data aggregation algorithms in the literature. We have also tried to summarize all these existing data aggregation algorithms on the basis of different performance metrics such as energy consumption, network lifetime, delay, energy cost, etc. Finally, this paper supports the reader an idea to select the data aggregation algorithm for the desired application. (C) 2020 Elsevier B.V. All rights reserved.
In this article, we provide a distributed optimization algorithm, termed as TV-AB, that minimizes a sum of convex functions over time-varying, random directed graphs. Contrary to the existing work, the algorithm we pr...
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In this article, we provide a distributed optimization algorithm, termed as TV-AB, that minimizes a sum of convex functions over time-varying, random directed graphs. Contrary to the existing work, the algorithm we propose does not require eigenvector estimation to estimate the (non-1) Perron eigenvector of a stochastic matrix. Instead, the proposed approach relies on a novel information mixing approach that exploits both row- and column-stochastic weights to achieve agreement toward the optimal solution when the underlying graph is directed. We show that TV-AB converges linearly to the optimal solution when the global objective is smooth and strongly convex, and the underlying time-varying graphs exhibit bounded connectivity, i.e., a union of every C consecutive graphs is strongly connected. We derive the convergence results based on the stability analysis of a linear system of inequalities along with a matrix perturbation argument. Simulations confirm the findings in this article.
Hybrid density-functional calculation is one of the most commonly adopted electronic structure theories in computational chemistry and materials science because of its balance between accuracy and computational cost. ...
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Hybrid density-functional calculation is one of the most commonly adopted electronic structure theories in computational chemistry and materials science because of its balance between accuracy and computational cost. Recently, we have developed a novel scheme called NAO2GTO to achieve linear scaling (Order-N) calculations for hybrid density-functionals. In our scheme, the most time-consuming step is the calculation of the electron repulsion integrals (ERIs) part, so creating an even distribution of these ERIs in parallel implementation is an issue of particular importance. Here, we present two static scalable distributed algorithms for the ERIs computation. Firstly, the ERIs are distributed over ERIs shell pairs. Secondly, the ERIs are distributed over ERIs shell quartets. In both algorithms, the calculation of ERIs is independent of each other, so the communication time is minimized. We show our speedup results to demonstrate the performance of these static parallel distributed algorithms in the Hefei Order-N packages for ab initio simulations.
Skip Graph is a promising distributed data structure for large scale systems and known for its capability of range queries. Although several methods of routing range queries in Skip Graph have been proposed, they have...
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Skip Graph is a promising distributed data structure for large scale systems and known for its capability of range queries. Although several methods of routing range queries in Skip Graph have been proposed, they have inefficiencies such as a long path length or a large number of messages. In this paper, we propose a novel routing method for range queries named Split-Forward Broadcasting (SFB). SFB introduces a divide-and-conquer approach, enabling nodes to make full use of their routing tables to forward a range query. It brings about a shorter average path length than existing methods, as well as a smaller number of messages by avoiding duplicate transmission. We clarify the characteristics and effectiveness of SFB through both analytical and experimental comparisons. The results show that SFB can reduce the average path length roughly 30% or more compared with a state-of-the-art method.
In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the...
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In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation.
Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel...
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Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel from a family of channels W, which can be instantiated to capture, among others, both the communication- and privacy-constrained settings. The referee uses the players' messages to solve an inference problem on the unknown distribution that generated the samples. We derive lower bounds for the sample complexity of learning and testing discrete distributions in this information-constrained setting. Underlying our bounds is a characterization of the contraction in chi-square distance between the observed distributions of the samples when information constraints are placed. This contraction is captured in a local neighborhood in terms of chi-square and decoupled chi-square fluctuations of a given channel, two quantities we introduce. The former captures the average distance between distributions of channel output for two product distributions on the input, and the latter for a product distribution and a mixture of product distribution on the input. Our bounds are tight for both public- and private-coin protocols. Interestingly, the sample complexity of testing is order-wise higher when restricted to private-coin protocols.
Many tasks executed in dynamic distributed systems, such as sensor networks or enterprise environments with bring-your-own-device policy, require central coordination by a leader node. In the past it has been proven t...
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Many tasks executed in dynamic distributed systems, such as sensor networks or enterprise environments with bring-your-own-device policy, require central coordination by a leader node. In the past it has been proven that distributed leader election in dynamic environments with constant changes and asynchronous communication is not possible. Thus, state-of-the-art leader election algorithms are not applicable in asynchronous environments with constant network changes. Some algorithms converge only after the network stabilizes (an unrealistic requirement in many dynamic environments). Other algorithms reach consensus in the presence of network changes but require a global clock or some level of communication synchrony. Determining the weakest assumptions, under which bounded leader election is possible, remains an unresolved problem. In this study we present a leader election algorithm that operates in the presence of changes and under weak (realistic) assumptions regarding message delays and regarding the clock drifts of the distributed nodes. The proposed algorithm is self-sufficient, easy to implement and can be extended to support multiple regions, self-stabilization, and mobile ad-hoc networks. We prove the algorithms correctness and provide a complexity analysis of the time, space, and number of messages required to elect a leader.
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