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Optimal Placement of UAVs for Minimum Outage Probability

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 9558-9570页

作者： Shabanighazikelayeh, Maryam Koyuncu, Erdem Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

We consider multiple unmanned aerial vehicles (UAVs) serving a density of ground terminals (GTs) as base stations. The objective is to minimize the outage probability of GT-to-UAV transmissions. Optimal placement of UAVs under different UAV altitude constraints and GT densities is studied. A practical variant of the Rician fading model, which has been developed by Azari et al. (2018) specifically for UAV systems, is used to model the communication channels. First, the structure of optimal deployments is determined when the common altitude constraint is large. For a wide class of GT densities, it is shown that all UAVs should be placed to the same location in an optimal deployment. A design implication is that one can use a single multi-antenna UAV as opposed to multiple single-antenna UAVs without loss of optimality. Next, using a random deployment argument, a general upper bound on the optimal outage probability is found for any density of GTs and any number of UAVs. Further, for any arbitrary user density, centralized and distributed numerical algorithms are designed using particle swarm optimization and modified gradient descent algorithms, respectively. Simulations show that our distributed algorithm provides almost optimal results even with considerably reduced communication and sensing ranges at the UAVs.

关键词： Probability Power system reliability Autonomous aerial vehicles Rician channels Numerical models distributed algorithms Base stations distributed algorithms optimal placement outage probability UAV-aided communications

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Two-Facet Scalable Cooperative Optimization of Multi-Agent Systems in the Networked Environment

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2022年第6期30卷 2317-2332页

作者： Huo, Xiang Liu, Mingxi Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA

Cooperatively optimizing a vast number of agents over a large-scale network faces unprecedented scalability challenges. The scalability of existing optimization algorithms is limited by either the agent population size or the network dimension. As a radical improvement, this article, for the first time, constructs a two-facet scalable distributed optimization framework. This novel framework distributes the computing load among agents (scalability w.r.t. population size) and enables each agent to only consider partial network constraints in its primal variable updates (scalability w.r.t. network dimension). We first develop a systemic network dimension reduction technique to virtually cluster the agents and lower the dimension of network-induced constraints and then constitute a novel shrunken primal-multi-dual subgradient (SPMDS) algorithm based on the reduced-dimension network for strongly coupled convex optimization problems. Optimality and convergence of the proposed distributed optimization framework are rigorously proved. The SPMDS-based optimization framework is free of agent-to-agent or cluster-to-cluster communication. Besides, the proposed method can achieve significant floating-point operations (FLOPs) reduction compared with full-dimension cases. The efficiency and efficacy of the proposed approaches are demonstrated, in comparison with benchmark methods, through simulations of electric vehicle charging control problems and traffic congestion control problems.

关键词： Optimization Scalability Convergence Electric vehicle charging Linear programming Dimensionality reduction Distribution networks Cooperative optimization dimension reduction distributed algorithms electric vehicle (EV) charging control multi-agent systems

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The central role of data repositories and data models in Data Science and Advanced Analytics

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 129卷 13-17页

作者： Bellatreche, Ladjel Ordonez, Carlos Mery, Dominique Golfarelli, Matteo Abdelwahed, El Hassan LIAS ISAE ENSMA Poitiers Univ Poitiers France Univ Houston Data Sci Syst Lab Houston TX 77004 USA LORIA Nancy France Univ Bologna Comp Engn Bologna Italy Univ Cadi Ayyad Comp Sci Marrakech Morocco

In the age of ''Data Science and Advanced Analytics'', we are witnessing a race for developing data-driven smart systems in various domains such as business, finance, healthcare, environment, cybersecurity, etc. due the explosion of the data issued by various providers. This development contributes in getting added value for companies and citizens. Two complementary ingredients are required for ensuring valuable systems: data and models. The data dimension is mainly related to Data Science that unifies machine learning, statistics, data mining, databases, and distributed systems. The achievement of this value may pass through the augmentation of input data by resources such as Knowledge Graphs. The success of the above techniques strongly depends on the quality of the input data and the consideration of other non-functional properties related to legal, ethical, and economical aspects. On the other hand, modeling plays a crucial role in Data Science since it covers all steps of Data Science workflow. Regarding data provenance and its quality, models contribute to providing vendor-independent solutions. At the algorithmic level, models help in explaining the inner working of the used methods/algorithms to system designers, users, regulators, and citizens to achieve trust and accountability. Therefore, the success of Data Science depends on our skill to use it a smart way and simultaneously exploiting data and modeling capabilities. (C) 2021 Published by Elsevier B.V.

关键词： Data Science Data science workflow Advanced Analytics Value Knowledge Graphs distributed data storage Model engineering Formal methods Model driven engineering distributed algorithms

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A note on improved results for one round distributed clique listing

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INFORMATION PROCESSING LETTERS 2023年 181卷

作者： Liu, Quanquan C. Northwestern Univ Comp Sci 2233 Tech DrThird Floor Evanston IL 60208 USA

In this note, we investigate listing cliques of arbitrary sizes in bandwidth-limited, dynamic networks. The problem of detecting and listing triangles and cliques was originally studied in great detail by Bonne and Censor-Hillel (ICALP 2019). We extend this study to dynamic graphs where more than one update may occur as well as resolve an open question posed by Bonne and Censor-Hillel (2019). Our algorithms and results are based on some simple observations about listing triangles under various settings and we show that we can list larger cliques using such facts. Specifically, we show that our techniques can be used to solve an open problem posed in the original paper: we show that detecting and listing cliques (of any size) can be done using O (1)-bandwidth after one round of communication under node insertions and node/edge deletions. We conclude with an extension of our techniques to obtain a small bandwidth 1-round algorithm for listing cliques when more than one node insertion/deletion and/or edge deletion update occurs at any time. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Triangle counting Clique counting distributed algorithms Graph algorithms Dynamic algorithms

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Phase transition of a nonlinear opinion dynamics with noisy interactions

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SWARM INTELLIGENCE 2022年第4期16卷 261-304页

作者： d'Amore, Francesco Clementi, Andrea Natale, Emanuele Univ Cote dAzur Inria CNRS I3S Sophia Antipolis France Univ Roma Tor Vergata Rome Italy

In several real Multi-Agent Systems, it has been observed that only weaker forms of metastable consensus are achieved, in which a large majority of agents agree on some opinion while other opinions continue to be supported by a (small) minority of agents. In this work, we take a step towards the investigation of metastable consensus for complex (nonlinear) opinion dynamics by considering the popular Undecided-State dynamics in the binary setting, which is known to reach consensus exponentially faster than the Voter dynamics. We propose a simple form of uniform noise in which each message can change to another one with probability p and we prove that the persistence of a metastable consensus undergoes a phase transition for p = 1/6. In detail, below this threshold, we prove the system reaches with high probability a metastable regime where a large majority of agents keeps supporting the same opinion for polynomial time. Moreover, this opinion turns out to be the initial majority opinion, whenever the initial bias is slightly larger than its standard deviation. On the contrary, above the threshold, we show that the information about the initial majority opinion is "lost" within logarithmic time even when the initial bias is maximum. Interestingly, we show our results have explicit connections to two different concrete frameworks. The first one concerns a specific setting of a well-studied value-sensitive decision mechanism inspired by cross-inhibition in house-hunting honeybee swarms. The second framework consists of a consensus process where a subset of agents behave in a stubborn way.

关键词： Opinion dynamics Communication noise distributed algorithms Multi-agent systems Stochastic processes

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Efficient Bounded Timestamping from Standard Synchronization Primitives 23

Efficient Bounded Timestamping from Standard Synchronization...

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42nd ACM SIGACT-SIGOPS Symposium on Principles of distributed Computing (PODC)

作者： Bashari, Benyamin Jamadi, Ali Woelfel, Philipp Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400701214

Bounded timestamps [10, 20] allow a temporal ordering of events in executions of concurrent algorithms. They are a fundamental and well studied building block used in many shared memory algorithms. A concurrent timestamp system keeps track of.. timestamps, which is usually greater or equal to the number of processes in the system, n. A process may, at any point, obtain a new timestamp, and later determine a total order of all process's most recent timestamps. Known timestamp algorithms do not scale well in the number of processes. Getting a new timestamp takes at least a linear number of steps, and a lower bound by Israeli and Li [20] implies that each timestamp needs to be represented by at least Omega(m) bits. We introduce a slightly different semantics for timestamping, where there is no fixed timestamp value associated with an event. A process can execute operation updateTS() to update its latest timestamp, associating it with the (linearization) point of that operation, and isEarlier(p,q) to determine the temporal order of the latest updateTS() operations executed by processes p and q. Since no static timestamp value is returned by updateTS(), the lower bound of Israeli and Li does not apply. We present efficient linearizable and wait-free implementations of these methods using a single bounded fetch-and-add object and O(n(2)) bounded compare-and-swap objects, which are available on standard hardware. The step complexity of each method call is constant, and base objects need only store O (log n) bits.

关键词： timestamping shared memory concurrency distributed algorithms

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Filling MIS Vertices of a Graph by Myopic Luminous Robots 19th

Filling MIS Vertices of a Graph by Myopic Luminous Robots

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19th International Conference on distributed Computing and Intelligent Technology (ICDCIT)

作者： Pramanick, Subhajit Samala, Sai Vamshi Pattanayak, Debasish Mandal, Partha Sarathi Indian Inst Technol Guwahati Dept Math Gauhati India LUISS Univ Rome Italy

ISBN: (纸本)9783031248474;9783031248481

We present the problem of finding a maximal independent set (MIS) (named as MIS Filling problem) of an arbitrary connected graph 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 forms a maximal independent set. This paper explores two versions of the MIS filling problem. For the MIS Filling with Single Door case, 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 and executes the algorithm at least once. The robots have three hops of visibility range, Delta + 8 number of colors, and O(log Delta) bits of persistent storage, where Delta is the maximum degree of the graph. For the MIS Filling with Multiple Doors case, our MULTIND algorithm 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.

关键词： distributed algorithms Multi-agent systems Mobile robots MIS Filling problem Luminous robots

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A Flexible Approach to Interference Cancellation in distributed Sensor Networks

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IEEE COMMUNICATIONS LETTERS 2021年第6期25卷 1853-1856页

作者： Shamsi, M. Haghighi, A. M. Bagheri, N. Marvasti, F. Sharif Univ Technol Adv Commun Res Inst ACRI Elect Engn EE Dept Multimedia & Signal Proc Lab MSL Tehran *** Iran

In a distributed algorithm several processing nodes communicate with each other and incorporate in order to reach a common goal. Each unit has its own locally-observed environment while it can either help or mislead other units when sharing its information. This could be either intentional or simply due to the observation noise. Previous works mainly focus on locating the agents properly and assigning weights based on stationary environment in order to minimize the impact of noisy nodes. We, however, propose a method capable of assigning combination weights in accordance with momentary performance of the neighboring nodes. In order to demonstrate this capability, the proposed algorithm has been applied to well-known diffusion LMS algorithms. Unlike uniform weighting policy, the proposed method is robust to changes caused by malfunctioning nodes.

关键词： Estimation Signal processing algorithms distributed algorithms Noise measurement Steady-state Adaptation models Tuning distributed algorithm noise reduction distributed estimation learning on graph noise propagation

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Fast distributed Stochastic Gradient Descent for Big Data Classification 23

Fast Distributed Stochastic Gradient Descent for Big Data Cl...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Mingfei Wang, Huihui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the needs of big data applications. When a single machine is trained, it will face the problem of insufficient memory and limited computing capacity. Therefore, distributed algorithms have been proposed to solve big data classification, and have received much attention. Particularly, distributed gradient descent (GD) is a widely-used algorithm for solving big data problem. However, distributed algorithms based on GD always converge slowly and suffer from expensive time in the training process. To alleviate this problem, we propose a novel fast distributed stochastic gradient descent algorithm for big data classification in this paper. By formulating the original optimization problem as a series of subproblems for classification in each machine. In particular, we exploit a fast stochastic method combined with the Nesterov acceleration method for sub-problem optimization in parallel to improve the convergence speed, and thus reduce the training time. The experimental results on some public and benchmark data sets show that our proposed distributed algorithm can converge faster with the competitive accuracy performance compared with distributed GD-based methods, which is suitable to big data classification. © 2021 IEEE.

关键词： Training Machine learning algorithms Smart cities Big Data Benchmark testing Classification algorithms distributed algorithms

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Robust distributed Fixed-Time Economic Dispatch Under Time-Varying Topology

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IEEE CONTROL SYSTEMS LETTERS 2021年第4期5卷 1183-1188页

作者： Baranwal, Mayank Garg, Kunal Panagou, Dimitra Hero, Alfred O. Tata Consultancy Serv Div Data & Decis Sci Mumbai 400607 Maharashtra India Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

The centralized power generation infrastructure that defines the North American electric grid is slowly moving to the distributed architecture due to the explosion in use of renewable generation and distributed energy resources (DERs), such as residential solar, wind turbines and battery storage. Furthermore, variable pricing policies and profusion of flexible loads entail frequent and severe changes in power outputs required from the individual generation units, requiring fast availability of power allocation. To this end, a fixed-time convergent, fully distributed economic dispatch algorithm for scheduling optimal power generation among a set of DERs is proposed. The proposed algorithm incorporates both load balance and generation capacity constraints.

关键词： Generators Convergence Topology Heuristic algorithms Power system stability Robustness Power generation distributed algorithms nonlinear control systems optimization power generation dispatch

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