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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sadeghi, Armin Smith, Stephen L. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781538660263

This paper focuses on the problem of deploying a set of autonomous robots to efficiently monitor multiple types of events in an environment. There is a density function over the environment for each event type representing the weighted likelihood of the event at each location. The robots are heterogeneous in that each robot is equipped with a set of sensors and it is capable of sensing a subset of event types. The objective is to deploy the robots in the environment to minimize a linear combination of the total sensing quality of the events. We propose a new formulation for the problem which is a natural extension of the homogeneous problem. We propose distributed algorithms that drive the robots to locally optimal positions in both continuous environments that are obstacle-free, and in discrete environments that may contain obstacles. In both cases we prove convergence to locally optimal positions. We provide extension to the case where the density functions are unknown prior to the deployment in continuous environments. Finally, we present benchmarking results and physical experiments to characterize the solution quality.

关键词： Robot sensing systems Density functional theory distributed algorithms Linear programming Silicon

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Inference under Information Constraints: Lower Bounds from Chi-Square Contraction 32

Inference under Information Constraints: Lower Bounds from C...

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32nd Conference on Learning Theory (COLT) part of the ACM Federated Computing Research Conference

作者： Acharya, Jayadev Canonne, Clement L. Tyagi, Himanshu Cornell Univ Ithaca NY 14853 USA Stanford Univ Stanford CA 94305 USA Indian Inst Sci Bangalore Karnataka India

Multiple users getting one sample each from an unknown distribution seek to enable a central server to conduct statistical inference. However, each player can only provide limited amount of information about its sample to the server. We propose a unified framework to study such distributed inference problems under local information constraints. We model the local information constraints by a set of channels W: each player chooses a channel from W, and then passes their data through this channel before transmitting the output to the server. The goal in this distributed setting is to understand the blow-up in data requirement imposed by the information constraints, compared to the centralized setting where all data samples are available to the server. We introduce two notions of chi-square fluctuations which provide bounds for the average distance and the distance to the average of a local perturbation. When information constraints are imposed, by the standard data-processing inequality, pairwise distances contract and so do our chi-square fluctuations. We provide a precise characterization of this contraction for discrete k-ary distributions and use it to obtain to general lower bounds for distribution learning and testing under information constraints. Our results involve notions of minmax and maxmin chi-square fluctuations, where the maximum is over the choice of channels and the minimum is over perturbations. The former emerges when considering public-coin protocols for testing and is bounded in terms of Frobenius norm of a positive semidefinite matrix H, a function of the channel family W. The latter appears for private-coin protocols and is bounded by the nuclear norm of H which is smaller than its Frobenius norm, establishing a separation between the sample complexity of testing using public and private coins.

关键词： statistical inference distribution testing distribution learning distributed algorithms

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Computation Time Analysis of Centralized and distributed Optimization algorithms Applied to Automated Irrigation Networks 3rd

Computation Time Analysis of Centralized and Distributed Opt...

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3rd Australian Control Conference (AUCC)

作者： Farhadi, Alireza Dower, Peter M. Cantoni, Michael Sharif Univ Technol Dept Elect Engn Tehran Iran Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

ISBN: (纸本)9781479924981;9781479924974

This paper compares the computation time of two algorithms for solving a structured constrained linear optimal control problem with finite horizon quadratic cost within the context of automated irrigation networks. The first is a standard centralized algorithm based on the active set method that does not exploit problem structure. The second is distributed and is based on a consensus algorithm, not specifically tailored to account for system structure. It is shown that there is a significant advantage in terms of computation overhead (the time spent computing the optimal solution) in using the second algorithm in large-scale networks. Specifically, for a fixed horizon length the computation overhead of the centralized algorithm grows as O(n(5)) with the number n of sub-systems. By contrast, it is observed via a combination of analysis and experiment that given n times resources for computation the computation overhead of the distributed algorithm grows as O(n) with the number n of sub-systems.

关键词： complex networks computational complexity distributed algorithms irrigation optimal control optimisation set theory

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Deterministic contention resolution on a shared channel 39

Deterministic contention resolution on a shared channel

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39th IEEE International Conference on distributed Computing Systems (ICDCS)

作者： De Marco, Gianluca Kowalski, Dariusz R. Stachowiak, Grzegorz Univ Salerno Dipartimento Informat Salerno Italy Univ Liverpool Dept Comp Sci Liverpool Merseyside England SWPS Univ Warsaw Poland Univ Wroclaw Inst Comp Sci Wroclaw Poland

ISBN: (纸本)9781728125190

A shared communication channel (also known as a multiple access channel) is among the most popular and widely studied models of communication and distributed computing. In this model, stations are able to communicate by transmitting and listening to a shared channel. A fundamental problem, called contention resolution, is to allow any station to successfully deliver its message by resolving the conflicts that arise when several stations transmit simultaneously on the channel. Despite a long history, many fundamental questions remain open in the realistic scenario when up to k stations out of N join the channel at different times. In this work we explore the impact of asynchrony, knowledge (or linear estimate) of contenders, and acknowledgments, on latency and channel utilization of non-adaptive deterministic algorithms. We show that if the number of contenders k (or a linear upper bound on it) is known and the stations switchoff after acknowledgment of their successful transmissions, the channel admits efficient solutions. In the same settings, we show that the ignorance of contention k makes the channel nearly quadratically less efficient, even if the stations could switch-off after acknowledgments. We present an algorithm which nearly matches this complexity (for unknown k) which is achieved even if acknowledgments are not provided. We show how the above algorithm could be further improved if stations could switch off upon acknowledgment. Surprisingly, our results imply an exponential impact of knowledge of contention on deterministic utilization of asynchronous channel by deterministic algorithms - it is known that for synchronized channel this feature does not influence asymptotically the channel utilization. The second implication concerns the impact of acknowledgments - they exponentially improve deterministic channel utilization if (some estimate of) k is known, unlike in the case of randomized algorithms where the improvement is only polynomial, while they a

关键词： Collision avoidance Clocks Switches distributed algorithms Upper bound Schedules Adaptation models

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Flight Coordination Approaches of UAV Squads for WSN Data Collection

Flight Coordination Approaches of UAV Squads for WSN Data Co...

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作者： Bruno Jose Olivieri de Souza Pontificia Universidade Catolica do Rio de Janeiro

学位级别：博士

Olivieri de Souza, Bruno Jose; Endler, Markus (Advisor). Flight Coordination Approaches of UA V Squads for WSN Data Collection. Rio de Janeiro, 2019. 111p. Tese de doutorado – Departamento de Informatica, Pontificia Universidade Catolica do Rio de Janeiro. Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as monitoring large or hazardous areas. The retrieval of WSN data can yield better results with the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of collected data and decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with great residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be put in place. Some studies present approaches to the use of UAVs for the collection of WSN data, focusing mainly on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results show that the amount of data collected by DADCA is similar or superior, by up to 1%, to path optimization approaches. In the proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other approaches presented, while also adding the advantages of a distributed approach.

关键词： Unmanned Aereal Vehicles Data Collection Wireles Sensors Networks distributed algorithms Swarm

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On distributed Scheduling of Flexible Demand and Nash Equilibria in the Electricity Market

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DYNAMIC GAMES AND APPLICATIONS 2018年第4期8卷 761-798页

作者： De Paola, Antonio Angeli, David Strbac, Goran Imperial Coll London Dept Elect & Elect Engn South Kensington Campus London SW7 2AZ England

This paper presents a novel game theory approach for large-scale deployment of price-responsive electrical appliances. In the proposed distributed control scheme, each appliance independently schedules its power consumption on the basis of a broadcast demand/price signal, aiming to complete its task at minimum cost. The conflicting interactions of the appliances, competing for power consumption at the cheapest hours of the day, are modelled through a differential game with a continuum of players, and efficient deployment of flexible demand is characterized as a Nash equilibrium. A novel approach is adopted to derive necessary and sufficient equilibrium conditions: intrinsic properties of the problem (price monotonicity, unidirectionality of power transfers) are exploited to perform an equilibrium study based on sublevel sets of the considered demand profiles. As a result, it is possible to determine for which penetration levels of flexible demand, types of appliances and inflexible demand profiles it is possible to achieve an equilibrium. Such stable configuration is achieved through the broadcast of a single demand/price signal and does not require iterated exchange of information between devices and coordinator. In addition, the global optimality of the equilibrium is proved, necessary conditions for Pareto optimality are derived, and a preliminary analysis of devices with partial time availability is carried out. The performance of the proposed control strategy is evaluated in simulation, considering realistic future scenarios of the UK power system with large penetration of flexible demand.

关键词： Smart grid distributed algorithms Price-responsive appliances Electricity market Differential games

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Using Theory to Self-Tune Migration Periods in distributed Genetic algorithms

Using Theory to Self-Tune Migration Periods in Distributed G...

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IEEE Congress on Evolutionary Computation

作者： Osorio, Karel Alba, Enrique Luque, Gabriel Univ Ciencias Informat Carretera San Antonio de los Banos Km 2 1-2 Havana Cuba ETSI Informat Dpt Lenguajes & Ciencias Computac Malaga Spain

ISBN: (纸本)9781479904549;9781479904532

In this paper we design a new distributed genetic algorithm, which is able to self-adapt the value of one of the most important parameter in this kind of techniques using the information provided by theoretical models. We study different alternative ways to use the mathematical results in our genetic algorithm. We test our technique on a wide set of instances of the well-known MAX-SAT problem. Experiments show that our self-* proposal is able to obtain similar, or even better, results when it is compared to traditional algorithms whose setting is made by hand. We also show the benefits in terms of saving time and complexity of migration policy settings for distributed genetic algorithms without reducing their efficiency.

关键词： computability distributed algorithms genetic algorithms

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More Consensus is not always Beneficial

More Consensus is not always Beneficial

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American Control Conference

作者： Xuan Wang Shaoshuai Mou School of Aeronautics and Astronautics Purdue University West Lafayette IN USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper shows that more consensus is not always beneficial to the convergence rate of consensus-based distributed computational algorithms. Specifically, we focus on the consensus-based distributed algorithm for solving linear equations, which aims to enable multiple agents in a network to cooperatively find a solution to large-scale linear equations. Such algorithms have two key components: local computation that happens within each agent;and global consensus that happens among connected agents through the networks. Intuitively, one expects more consensus in each iteration should speed up the convergence of such algorithms. However, according to the theoretical analysis and numerical simulations provided in this paper, such assumption does not hold. Counter-intuitively, we show that more consensus is not always beneficial to consensus-based distributed algorithms, and sometimes slow down the convergence.

关键词： distributed algorithms Linear equation Converge Agents algorithms iterative methods Theoretical analysis NETWORKING AGENT

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Evacuation from a Finite 2D Square Grid Field by a Metamorphic Robotic System

Evacuation from a Finite 2D Square Grid Field by a Metamorph...

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International Symposium on Computing and Networking (CANDAR)

作者： Junya Nakamura Sayaka Kamei Yukiko Yamauchi Information and Media Center Toyohashi University of Technology Aichi Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9781728182216

ISBN: (纸本)9781728182223

We consider evacuation from a finite two-dimensional (2D) square grid field by a metamorphic robotic system (MRS). An MRS is composed of anonymous memoryless modules. Each module of an MRS executes an identical distributed algorithm and moves autonomously while keeping the connectivity of modules. Since the modules are memoryless, an MRS utilizes its shape to remember the progress of execution. The number of available shapes that an MRS can form depends on the number of modules, which is thus an important complexity measure for a behavior of an MRS. In this paper, we investigate the minimum number of modules required to solve the evacuation problem with several conditions. First, we consider a rectangular field surrounded by walls with at least one exit and show that two modules are necessary and sufficient for evacuation from any rectangular field if the modules are equipped with a global compass, which allows the modules to have a common sense of direction. Then, we focus on the case where modules do not have a global compass and show that four (resp. seven) modules are necessary and sufficient for restricted (resp. any) initial states of an MRS. We also show that two modules are sufficient in the special case where an MRS is on a wall in an initial configuration. Finally, we extend these results to another type of fields, that is, mazes.

关键词： Shape Shape measurement Two dimensional displays Turning Compass distributed algorithms Robots

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Maritime Transportation Optimization Using Evolutionary algorithms In The Era Of Big Data And Internet Of Things

Maritime Transportation Optimization Using Evolutionary Algo...

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作者： Fatemeh Cheraghchi University of Ottawa

学位级别：博士

With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide Quality of Services (QOS) [18] in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem us- ing evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things (IoT). Ac- cordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Sched- ule Recovery Problem (VSRP) is reformulated and a bi-objective optimiza- tion approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimiz- ing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their perfor- mance significance. According to the experiments, the problem complexity varies in different scenarios, while Non-dominated Sorting Genetic Algorithm II (NSGAII) performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data [2]. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed [133] regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimiza- tion problem called Granular Speed-based Vessel Schedule Recovery Problem (G-S-VSRP) with three objectives (i. e., minimizing loss, delay, and maxim

关键词： multiobjective optimization evolutionary algorithms vessel schedule recovery problem big data distributed algorithms cooperative coevolution Apache Spark

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