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Competitive Age of Information in Dynamic IoT Networks

IEEE INTERNET OF THINGS JOURNAL 2021年第20期8卷 15160-15169页

作者： Yu, Dongxiao Zou, Yifei Xu, Minghui Xu, Yicheng Zhang, Yong Gong, Bei Xing, Xiaoshuang Shandong Univ Inst Intelligent Comp Sch Comp Sci & Technol Qingdao 266237 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China George Washington Univ Dept Comp Sci Washington DC 20052 USA Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Changshu Inst Technol Dept Comp Sci & Engn Suzhou 215500 Peoples R China

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio;and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

关键词： Base stations Heuristic algorithms Interference Dynamic scheduling Scheduling algorithms Wireless communication Throughput Age-of-Information (AoI) scheduling distributed algorithms dynamic IoT networks

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On Adversary Robust Consensus Protocols Through Joint-Agent Interactions

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第4期66卷 1646-1657页

作者： Angeli, David Manfredi, Sabato Imperial Coll London Elect & Elect Engn Dept Control & Power Grp London SW7 2BU England Univ Florence Dipartimento Ingn Informaz I-50121 Florence Italy Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

A generalized family of adversary robust consensus protocols is proposed and analyzed. These are distributed algorithms for multiagent systems seeking to agree on a common value of a shared variable, even in the presence of faulty or malicious agents, which are updating their local state according to the protocol rules. In particular, we adopt monotone joint-agent interactions, a very general mechanism for processing locally available information and allowing cross-comparisons between state-values of multiple agents simultaneously. The salient features of the proposed class of algorithms are abstracted as a Petri net and convergence criteria for the resulting time evolutions formulated by employing structural invariants of the net.

关键词： Consensus protocol Petri nets Topology Multi-agent systems Convergence Additives Autonomous systems distributed algorithms faulty and malicious agents robust consensus

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Anytime Minibatch With Delayed Gradients

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 208-221页

作者： Al-Lawati, Haider Draper, Stark C. Univ Toronto Elect & Comp Engn Dept Toronto ON M5S 2E4 Canada

distributed optimization is widely deployed in practice to solve a broad range of problems. In a typical asynchronous scheme, workers calculate gradients with respect to out-of-date optimization parameters while the master uses stale (i.e., delayed) gradients to update the parameters. While using stale gradients can slow the convergence, asynchronous methods speed up the overall optimization with respect to wall clock time by allowing more frequent updates and reducing idling times. In this paper, we present a variable per-epoch minibatch scheme called Anytime Minibatch with Delayed Gradients (AMB-DG). In AMB-DG, workers compute gradients in epochs of a fixed time while the master uses stale gradients to update the optimization parameters. We analyze AMB-DG in terms of its regret bound and convergence rate. We prove that for convex smooth objective functions, AMB-DG achieves the optimal regret bound and convergence rate. We compare the performance of AMB-DG with that of Anytime Minibatch (AMB) which is similar to AMB-DG but does not use stale gradients. In AMB, workers stay idle after each gradient transmission to the master until they receive the updated parameters from the master while in AMB-DG workers never idle. We also extend AMB-DG to the fully distributed setting. We compare AMB-DG with AMB when the communication delay is long and observe that AMB-DG converges faster than AMB in wall clock time. We also compare the performance of AMB-DG with the state-of-the-art fixed minibatch approach that uses delayed gradients. We run our experiments on a real distributed system and observe that AMB-DG converges more than two times.

关键词： Asynchronous optimization delayed gradient methods distributed algorithms stragglers

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A continuous strategy for collisionless gathering

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THEORETICAL COMPUTER SCIENCE 2021年 852卷 41-60页

作者： Li, Shouwei Markarian, Christine Heide, Friedhelm Meyer Auf Der Podlipyan, Pavel Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany Univ Dubai Dept Engn & Informat Technol Dubai U Arab Emirates

Over the past decades, the Gathering problem, which asks to gather a group of robots in finite time given some restrictions, has been intensively studied. In this paper, we are given a group of n autonomous, dimensionless, deterministic, and anonymous robots, with bounded viewing range. Assuming a continuous time model, the goal is to gather these robots into one point in finite time. We introduce a simple convergence criterion that defines a new class of algorithms which perform gathering in O (nd) time, where d is the diameter of the initial robot configuration. We show that some gathering algorithms in the literature belong to this class and propose two new algorithms that belong to this class and have quadratic running time, namely, Go-To-The-Relative-Center algorithm (GTRC) and Safe-Go-To-The-Relative-Center algorithm (S-GTRC). We prove that the latter can perform gathering without collision by using a slightly more complex robot model: non oblivious, chiral, and luminous (i.e. robots have observable external memory, as in [8]). We also consider a variant of the Gathering problem, the Near-Gathering problem, in which robots must get close to each other without colliding. We show that S-GTRC solves the Near-Gathering problem in quadratic time and assumes a weaker robot model than the one assumed in the current state-of-the-art. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Local algorithms distributed algorithms Collisionless gathering Mobile robots Multiagent system

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A Real-Time Alternating Direction Method of Multipliers Algorithm for Nonconvex Optimal Power Flow Problem

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第1期57卷 70-82页

作者： Lu, Xi Yin, Hao Xia, Shiwei Zhang, Dongying Shahidehpour, Mohammad Zhang, Xian Ding, Tao North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China IIT Chicago IL 60616 USA Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China

The high penetration rate of smart devices such as storage elements brings new challenges to the optimal power flow (OPF) problem in power systems, which is generally nonconvex and difficult to be solved in real time. This article proposes a set of two fully distributed algorithms by combining the alternating direction method of multipliers and proximal alternating minimization techniques. The first one is a basic distributed algorithm for offline scheduling of 24-h ahead OPF. The other extended one is the warm-starting algorithm by using the offline scheduling solution as the initial point, and the warm-starting algorithm can get converged faster than the basic algorithm. Both algorithms aim to provide a highly feasible solution for the nonconvex OPF problem, and simulation conducted on four radial power systems with batteries has validated the performance of these two algorithms.

关键词： Load flow Real-time systems Optimization Convex functions distributed algorithms Minimization Alternating direction method of multipliers optimal power flow (OPF) proximal alternating minimization real-time operation

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Recovery algorithms for Paxos-Based State Machine Replication

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2021年第2期18卷 623-640页

作者： Konczak, Jan Wojciechowski, Pawel T. Santos, Nuno Zurkowski, Tomasz Schiper, Andre Poznan Univ Tech Inst Comp Sci PL-61051 Poznan Poland Ecole Polytech Fed Lausanne EPFL Sch Comp & Commun Sci CH-1015 Lausanne Switzerland

In this article, we propose and evaluate three different state recovery algorithms aimed for Paxos-one of the most popular distributed agreement protocols. Paxos is commonly used to maintain consistency among state machine replicas despite of failures of processes. The first algorithm, that we call FullSS, originates from the original Paxos and requires that the system frequently uses stable storage during regular (non-faulty) execution. The other two state recovery algorithms, ViewSS and EpochSS, scarcely require access to stable storage, and the recovering process must do much less work to restore its lost state, and to catch up on the current state of the system. We thoroughly analyze and compare the behavior of the three algorithms during state recovery and also during regular, non-faulty system execution, under various workloads (e.g., causing the network or CPU saturation). The experimental results show that by using ViewSS and EpochSS, we can significantly improve process recovery with respect to the original Paxos, if only it can be assumed that at any time a majority of replicas are up running (excluding those replicas that are just recovering). Moreover, these algorithms do not impact the performance of Paxos during regular (non-faulty) operation. However, FullSS is the only choice out of the three, if the system must tolerate catastrophic failures.

关键词： Computer crashes Protocols Fault tolerance Fault tolerant systems System performance Writing distributed algorithms Paxos state machine replication fault-tolerance

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distributed Optimization for Computation Offloading in Edge Computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第12期19卷 8179-8194页

作者： Lin, Rongping Zhou, Zhijie Luo, Shan Xiao, Yong Wang, Xiong Wang, Sheng Zukerman, Moshe Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China UESTC Sch Astronaut & Aeronaut Chengdu 611731 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.

关键词： Resource management Edge computing Optimization Wireless communication distributed algorithms Delays Delay effects Edge computing computation offloading Lyapunov optimization branch-and-bound

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Adaptive Topology Reconfiguration in Smart Distribution Networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 716-729页

作者： Liu, Jingyuan Srikantha, Pirathayini York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

The proliferation of diverse power entities (e.g. renewable generation, electric vehicles and storage) in the active Distribution Networks (DNs) offers many advantages while also introducing reliability challenges due to over-voltage and under-voltage conditions in DN buses and overcurrents in the DN lines. The focus of this work is to enable the adaptive reconfiguration of the electrical topology of the DN in order to proactively mitigate these inefficiencies. The cyber-physical nature of the modern DN facilitates the exchange of carefully crafted signals amongst actuating and monitoring power entities in the DN. These signals are designed to iteratively solve the topology reconfiguration problem that is a mixed integer non-linear program. The non-convexities inherent in this formulation are addressed by applying strategic decompositions that lead to sub-problems for which exact convex relaxations exist. Strong convergence properties and performance guarantees are established from theoretical constructs of S-procedure, Schur's complement, and additive separability. Simulation studies conducted on practical DN settings and comparisons with the state-of-the-art highlight the efficacy of the proposed algorithm.

关键词： distributed algorithms optimization methods power system management

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Fault-tolerant complete visibility for asynchronous robots with lights under one-axis agreement

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THEORETICAL COMPUTER SCIENCE 2021年 850卷 116-134页

作者： Poudel, Pavan Aljohani, Aisha Sharma, Gokarna Kent State Univ Dept Comp Sci Kent OH 44242 USA

We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles and communicate with other robots using colored lights following the robots with lights model. We assume obstructed visibility under which a robot cannot see another robot if a third robot is positioned between them on the straight line connecting them. We study the fundamental COMPLETE VISIBILITY problem of repositioning N robots, starting from N distinct points, on a plane so that each robot is visible to all others. We are interested in fault-tolerant algorithms. We study fault-tolerance with respect to failures on the mobility of robots. Therefore, any algorithm for COMPLETE VISIBILITY is required to provide visibility between all non-faulty robots (i.e., no three non-faulty robots are collinear and no faulty robot is between two non-faulty robots on the straight line connecting them), independently of the behavior of the faulty ones. We model mobility failures as crash faults in which each faulty robot may stop its movement at any time and, once it stopped moving, it will remain stationary indefinitely thereafter. There exists an algorithm for this problem that tolerates a single faulty robot in the semi-synchronous setting under both-axis agreement. In that algorithm, the light of the faulty robot does not need to work correctly after the robot experiences fault. In this article, we assume the model in which, even after a robot experiences fault, its light still operates correctly and provide the first algorithm for COMPLETE VISIBILITY that tolerates f <= N faulty robots in the asynchronous setting under one-axis agreement. The proposed algorithm has many interesting properties. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed algorithms Mobile robots Lights Crash faults Visibility Collisions Convex hull Obstruction

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Compressed Monte Carlo with application in particle filtering

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INFORMATION SCIENCES 2021年 553卷 331-352页

作者： Martino, Luca Elvira, Victor Univ Rey Juan Carlos URJC Dept Signal Proc Mostoles Spain Univ Carlos III Madrid UC3M Getafe Spain Univ Edinburgh Ctr Stat Edinburgh Midlothian Scotland

Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approximation of complicated integrals involving posterior distributions. For this purpose, Monte Carlo (MC) methods, such as Markov Chain Monte Carlo and importance sampling algorithms, are often employed. In this work, we introduce the theory and practice of a Compressed MC (C-MC) scheme to compress the statistical information contained in a set of random samples. In its basic version, C-MC is strictly related to the stratification technique, a well-known method used for variance reduction purposes. Deterministic C-MC schemes are also presented, which provide very good performance. The compression problem is strictly related to the moment matching approach applied in different filtering techniques, usually called as Gaussian quadrature rules or sigma-point methods. C-MC can be employed in a distributed Bayesian inference framework when cheap and fast communications with a central processor are required. Furthermore, C-MC is useful within particle filtering and adaptive IS algorithms, as shown by three novel schemes introduced in this work. Six numerical results confirm the benefits of the introduced schemes, outperforming the corresponding benchmark methods. A related code is also provided. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Bayesian inference MCMC importance sampling Particle filtering Gaussian quadrature Sigma points Herding algorithms distributed algorithms

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