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Emergency control of firefighting vehicles based on distributed integer programming

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第8期47卷 1570-1580页

作者： Wu, Wendi Wang, Zhikang Jiang, Baoping Zhang, Xin Wu, Zhengtian Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China

Fire-related emergency vehicle scheduling has always been an important part of autonomous vehicle emergency management. To maintain the fire safety of buildings and reduce the transportation loss of emergency resources, this paper constructs a multi-objective integer planning model for fire emergency resource scheduling, which minimizes the time spent in the transportation of fire materials and considers the transportation needs of potential fire vehicles. The fixed-point iterative algorithm is realized and optimized, and distributed computing is adopted to improve the solving speed. In this paper, the comparison and simulation experiments with Branch and Bound, improved particle swarm optimization and other algorithms are carried out. The results show that the model and the algorithm can effectively deal with the fire emergency vehicle scheduling problem under different simulated fires and multiple fires occurring at the same time, and have certain advantages in numerical stability and convergence speed.

关键词： Integer programming vehicle dispatch control emergency management distributed computing

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distributed edge intelligence for structural dynamic response estimation using knowledge distillation and data fusion

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ENGINEERING STRUCTURES 2025年 335卷

作者： Zhang, Fan Fu, Yuguang Shen, Kongjian Zhang, Yufeng Wang, Jingquan Southeast Univ Natl Key Lab Safety Durabil & Hlth Operat Long Spa Nanjing 211189 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Jiangsu Prov Transportat Engn Construct Bur Nanjing 210004 Peoples R China China Railway Bridge & Tunnel Technol Co Ltd Nanjing 210061 Jiangsu Peoples R China JSTI Grp Nanjing 211112 Peoples R China Jiangsu Univ Zhenjiang 212013 Peoples R China

Accurate and timely estimations of structural dynamic responses under extreme events are essential to enable rapid condition assessment and ensure the safety of civil infrastructure. For example, dynamic displacement estimation is an effective index for building assessment under earthquakes. In most structural health monitoring (SHM) systems, measurement data is transmitted and processed at a central workstation or cloud server to estimate structural dynamic response using machine learning (ML) or digital signal processing (DSP). The centralized approach faces challenges of latency caused by extensive data transmission, especially for wireless SHM systems where radio transmission is unreliable and power-hungry. To address this challenge, the study proposes a novel distributed edge intelligence framework, where ML meets edge computing suitable for wireless sensor networks (WSNs), to estimate nonlinear structural dynamic displacements using knowledge distillation and data fusion. Particularly, a distributed sensing strategy is designed to collect and process data in local clusters of wireless smart sensors, and a hybrid feature-knowledge distillation approach is proposed to enable lightweight ML models executable on edge devices in WSN. Furthermore, a data fusion algorithm is developed to effectively integrate estimations from overlapping nodes between multiple clusters of sensors. The performance of the proposed approach is validated through three case studies, including nonlinear time history analysis on a 10-story RC structure, a long-span cable-stayed bridge under seismic excitations, and an experimental study on an 18-story steel frame, respectively. The results demonstrate that the proposed framework significantly reduces data transmission burdens, enhances computational efficiency, and minimizes assessment latencies, while still maintaining the precision of nonlinear structural dynamic response estimations. This benefit can make it feasible to deploy structural

关键词： distributed computing Edge intelligence Structural health monitoring LSTM Knowledge distillation Data fusion

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Learning-Based distributed Spatio-Temporal k Nearest Neighbors Join

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 861-878页

作者： Li, Ruiyuan Li, Jiajun Zhou, Minxin Wang, Rubin He, Huajun Chen, Chao Bao, Jie Zheng, Yu Chongqing Univ Chongqing 400044 Peoples R China JD iCity Beijing 100176 Peoples R China JD Intelligent Cities Res Beijing 100176 Peoples R China Xidian Univ Xian 710126 Shaanxi Peoples R China

The rapid development of positioning technology produces an extremely large volume of spatio-temporal data with various geometry types such as point, line string, polygon, or a mixed combination of them. As one of the most fundamental but time-consuming operations, k nearest neighbors join (kNN join) has attracted much attention. However, most existing works for kNN join either ignore temporal information or consider only point data. Besides, most of them do not automatically adapt to the different features of spatio-temporal data. This paper proposes to address a novel and useful problem, i.e., ST-kNN join, which considers both spatial closeness and temporal concurrency. To support ST-kNN join over a large amount of spatio-temporal data with any geometry types efficiently, we propose a novel distributed solution based on Apache Spark. Specifically, our method adopts a two-round join framework. In the first round join, we propose a new spatio-temporal partitioning method that achieves spatio-temporal locality and load balance at the same time. We also propose a lightweight index structure, i.e., Time Range Count Index (TRC-index), to enable efficient ST-kNN join. In the second round join, to reduce the data transmission among different machines, we remove duplicates based on spatio-temporal reference points before shuffling local results. Furthermore, we design a set of models based on Bayesian optimization to automatically determine the values for the introduced parameters. Extensive experiments are conducted using three real big datasets, showing that our method is much more scalable and achieves 9X faster than baselines, and that the proposed models can always predict appropriate parameters for different datasets.

关键词： AI4DB Bayesian optimization distributed computing knob tuning spatio -temporal kNN join AI4DB Bayesian optimization distributed computing knob tuning

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[email protected] —A BOINC distributed computing project for asteroid shape reconstruction

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Astronomy and computing 2015年 13卷 80-84页

作者： J. Ďurech J. Hanuš R. Vančo Astronomical Institute Faculty of Mathematics and Physics Charles University in Prague V Holešovičkách 2 180 00 Prague 8 Czech Republic UNS-CNRS-Observatoire de la Côte d’Azur BP 4229 06304 Nice Cedex 4 France Czech National Team Czech Republic

We present the project [email protected] that uses distributed computing to solve the time-consuming inverse problem of shape reconstruction of asteroids. The project uses the Berkeley Open Infrastructure for Network computing (BOINC) framework to distribute, collect, and validate small computational units that are solved independently at individual computers of volunteers connected to the project. Shapes, rotational periods, and orientations of the spin axes of asteroids are reconstructed from their disk-integrated photometry by the lightcurve inversion method.

关键词： Asteroids distributed computing BOINC

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DIDS: A distributed inference framework with dynamic scheduling capability

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 162卷

作者： Yan, Yuwei Hu, Yikun Cai, Qinyun Yang, Wangdong Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Lushan Rd S Changsha 410082 Hunan Peoples R China

distributed inference is a promising solution for deploying Deep Neural Network (DNN) applications in resource-constrained edge environments. However, due to the complexity and variability of edge scenarios, efficiently completing DNN inference remains challenging. While previous works have made significant progress in various aspects such as partition strategy, device diversity, and memory overhead, few consider the impact of environmental dynamics, particularly environmental workloads. This oversight renders existing solutions impractical. In this study, we propose DIDS, a distributed inference framework with dynamic scheduling capability that considers environmental dynamics. We first conduct a formal analysis of distributed inference and design two basic scheduling strategies: Re-Partition and Complete-Push. Then we introduce Dynamic Push, a runtime scheduler based on Complete-Push that aims to mitigate the impact of environmental workloads through runtime scheduling. The evaluation results show that DIDS can achieve up to 3.7x speedup compared to static distributed inference, in addition, DIDS is also able to achieve significant improvement compared to the state-of-the-art method.

关键词： Edge intelligence distributed computing Runtime scheduling AIoT

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A modularized parallel distributed High-Performance computing framework for simulating seasonal frost dynamics in Canadian croplands

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期212卷

作者： Li, Ziwei Qi, Zhiming Liu, Yuchen Zheng, Yunjia Yang, Yi McGill Univ Dept Bioresource Engn Ste Anne De Bellevue PQ H9X 3V9 Canada Indiana Univ Dept Comp Sci Bloomington IN 47408 USA McGill Univ Sch Comp Sci Montreal PQ H3A 2A7 Canada DevOps Fortinet Technol Burnaby BC V5C 6C6 Canada Mcgill Univ 21111 Lakeshore Rd Ste Anne de Bellevue PQ H9X 3V9 Canada

The winter/spring season in cold climate regions has been recognized as a critical period for cropland nutrient loss and greenhouse gas emissions and has been predicted to be vulnerable to climate change. Agricultural system models serve as essential tools in anticipating potential outcomes. The RZ-SHAW model, combining the Simultaneous Heat and Water Model (SHAW) with the Root Zone Water Quality Model 2 (RZWQM2), comprehensively describes snow and soil freezing dynamics. Showing promising results in simulating winter frost dynamics it, however, remains computationally intensive. High-performance computing has been recognized as a promising approach to reducing simulation runtimes. A modularized parallel distributed highperformance computing (HPC) framework (RS-DPCF) with both multi-threading (MT) and multi-processing (MP) capability was developed for RZ-SHAW calibration and simulation. Two case studies demonstrated that the developed RS-DPCF program could significantly reduce RZ-SHAW calibration time, allow improved computation resource scalability, and perform field-scale simulations of overwintering conditions for multiple croplands across Canada. For the RZ-SHAW model, MT provided a parallel-computing efficiency superior to that of MP.

关键词： Parallel computing RZ-SHAW Winter hydrology distributed computing RZWQM2

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distributed computing Approach to Optimize Road Traffic Simulation 3

Distributed Computing Approach to Optimize Road Traffic Simu...

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International Conference Parallel distributed Grid computing

作者： Sinha, Amritanshu Saini, Tapas Srikanth, S. V. Ctr Dev Adv Comp Hyderabad India

ISBN: (纸本)9781479976836

distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. distributed computing generally uses the existing resources of the organization. Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.

关键词： distributed computing Traffic simulation Evolutionary algorithm Root Mean Square Error SUMO

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distributed ADMM With Linear Updates Over Directed Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1396-1407页

作者： Rokade, Kiran Kalaimani, Rachel Kalpana Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

distributed optimization over a network of agents is ubiquitous in applications such as power system, robotics and statistical learning. In many settings, the communication network is directed, i.e., the communication links between agents are unidirectional. While several variations of gradient-descent-based primal methods have been proposed for distributed optimization over directed networks, an extension of dual-ascent methods to directed networks remains a less-explored area. In this paper, we propose a distributed version of the Alternating Direction Method of Multipliers (ADMM) with linear updates for directed networks using balancing weights, called BW-DADMM (Balancing Weights Directed ADMM). We show that if the objective function of the minimization problem is smooth and strongly convex, then BW-DADMM achieves a geometric rate of convergence to the optimal point. Our algorithm exploits the robustness inherent to ADMM by not enforcing accurate consensus, thereby significantly improving the convergence rate. We illustrate this by numerical examples, where we compare the performance of BW-DADMM with that of state-of-the-art ADMM methods over directed graphs.

关键词： Convex functions Optimization Directed graphs Convergence Standards Vectors Linear programming Approximation algorithms Statistical learning Robustness Alternating Direction Method of Multipliers (ADMM) dual-ascent directed graphs distributed computing parallel computing

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Scaling Up Optuna: P2P distributed Hyperparameters Optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Cudennec, Loic Minist Armed Forces Ministerial Agcy Def Artificial Intelligence AMIAD Bruz France

In machine learning (ML), hyperparameter optimization (HPO) is the process of choosing a tuple of values that ensures an efficient deployment and training of an AI model. In practice, HPO not only applies to ML tuning but can also be used to tune complex numerical simulations. In this context, a numerical model of a given object is created to be used in realistic simulations. This model is defined by a set of values describing properties such as the geometry of the object or other unknown parameters related to physical quantities. While HPO for ML usually requires finding a few parameters, a numerical model can involve the tuning of more than a hundred parameters. As a consequence, a large number of tuples have to be explored and evaluated before finding a relevant solution, offering new challenges in high-performance computing for efficiently driving the optimization. In this work we rely on the Optuna HPO framework, primarily designed for ML tasks and including state-of-the-art sampling and pruning algorithms. We report on its use to optimize a complex numerical model onto a 1024-core machine. We suggest 1.5M tuples and evaluate 5M simulations using different Optuna-distributed layouts to build several tradeoffs between performance and energy consumption metrics. In order to further scale up the optimization process onto resources, we introduce OptunaP2P, an extension of Optuna based on the peer-to-peer paradigm. This allows to remove any bottleneck in the management of the shared knowledge between optimization processes. With OptunaP2P, we were able to compute up to 3 times faster compared to the regular Optuna-distributed implementation and to obtain close-to-similar results in terms of quality in this reduced time-frame.

关键词： distributed computing energy consumption high-performance computing hyperparameters optimization machine learning operational research optuna peer-to-peer

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Parallel Committees: a scalable, secure, and fault-tolerant distributed NoSQL database architecture

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-48页

作者： Solat, Siamak Univ Paris Cite 45 Rue St Peres F-75006 Paris France

Most existing Byzantine fault-tolerant algorithms are slow and not designed for large participant sets trying to reach consensus. Consequently, distributed databases that use consensus mechanisms to process transactions face significant limitations in scalability and throughput. These limitations can be substantially improved using sharding, a technique that partitions a state into multiple shards, each handled in parallel by a subset of the network. Sharding has already been implemented in several data replication systems. While it has demonstrated notable potential for enhancing performance and scalability, current sharding techniques still face critical scalability and security issues. In this article, we propose a novel, fault-tolerant, self-configurable, scalable, secure, decentralized, high-performance distributed NoSQL database architecture. Our approach employs an innovative sharding technique to enable Byzantine fault-tolerant consensus mechanisms in very large-scale networks. We introduce a new sharding method for data replication that leverages a classic consensus mechanism, such as PBFT, to process transactions. Our approach modifies node allocation among shards through the public key generation process, effectively reducing the frequency of cross-shard transactions, which are generally more complex and costly than intra-shard transactions. Additionally, our method eliminates the need for a shared ledger between shards, which typically imposes further scalability and security challenges on the network. We also explain how to automatically form new committees based on the availability of candidate processor nodes. This technique optimizes network capacity by employing inactive surplus processors from one committee's queue in forming new committees, thereby increasing system throughput and efficiency. Our approach maximizes processor node utilization as well as computational and storage capacity across the network, enhancing both processing and storage sha

关键词： Sharding Data replication distributed consensus mechanisms distributed databases NoSQL distributed computing Byzantine-fault-tolerance Cryptography

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