Pub Date : 2024-08-13DOI: 10.1109/JSYST.2024.3433023
Boyan Zhu;Liang Zhang;Ben Niu;Ning Zhao
This article addresses the adaptive optimized consensus tracking control problem of nonlinear multiagent systems (MASs) via a reinforcement learning (RL) algorithm. Specifically, the nonlinear high-order MASs are formulated in a canonical form, with considerations for both actuator effectiveness loss and time-varying bias faults. First, neural networks (NNs) are utilized to approximate unknown nonlinear dynamics, and a state identifier and a fault estimator based on NNs are established, both of which are essential for evaluating state information and bias faults, respectively. Second, to achieve a high-order canonical dynamic consensus and enhance the efficiency of the consensus control strategy, a sliding-mode mechanism is employed to regulate tracking errors. Moreover, we develop an adaptive NN-based fault-tolerant optimal control method by integrating the sliding-mode mechanism with an actor–critic structured RL algorithm. It is proved that the outputs of the MASs precisely align with the desired reference signals, while ensuring the boundedness of all closed-loop signals. Finally, the proposed control methodology's effectiveness is validated through a simulation example.
本文通过强化学习（RL）算法解决了非线性多代理系统（MAS）的自适应优化共识跟踪控制问题。具体来说，非线性高阶 MAS 采用典型形式，同时考虑了执行器效力损失和时变偏差故障。首先，利用神经网络（NN）来逼近未知的非线性动力学，并建立了基于 NN 的状态识别器和故障估计器，这两者分别对评估状态信息和偏差故障至关重要。其次，为了实现高阶典型动态共识并提高共识控制策略的效率，我们采用了滑模机制来调节跟踪误差。此外，我们还将滑模机制与行为批判结构化 RL 算法相结合，开发了一种基于 NN 的自适应容错优化控制方法。事实证明，MAS 的输出与所需的参考信号精确一致，同时确保所有闭环信号的有界性。最后，通过一个仿真实例验证了所提出的控制方法的有效性。
{"title":"Adaptive Reinforcement Learning for Fault-Tolerant Optimal Consensus Control of Nonlinear Canonical Multiagent Systems With Actuator Loss of Effectiveness","authors":"Boyan Zhu;Liang Zhang;Ben Niu;Ning Zhao","doi":"10.1109/JSYST.2024.3433023","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3433023","url":null,"abstract":"This article addresses the adaptive optimized consensus tracking control problem of nonlinear multiagent systems (MASs) via a reinforcement learning (RL) algorithm. Specifically, the nonlinear high-order MASs are formulated in a canonical form, with considerations for both actuator effectiveness loss and time-varying bias faults. First, neural networks (NNs) are utilized to approximate unknown nonlinear dynamics, and a state identifier and a fault estimator based on NNs are established, both of which are essential for evaluating state information and bias faults, respectively. Second, to achieve a high-order canonical dynamic consensus and enhance the efficiency of the consensus control strategy, a sliding-mode mechanism is employed to regulate tracking errors. Moreover, we develop an adaptive NN-based fault-tolerant optimal control method by integrating the sliding-mode mechanism with an actor–critic structured RL algorithm. It is proved that the outputs of the MASs precisely align with the desired reference signals, while ensuring the boundedness of all closed-loop signals. Finally, the proposed control methodology's effectiveness is validated through a simulation example.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1681-1692"},"PeriodicalIF":4.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1109/JSYST.2024.3425866
Nadhira Azizah Suwanda;Hye Yeong Lee;Soo Young Shin
This study proposes multiple reconfigurable intelligent surface (RIS)-based index modulation (IM), called MRB-IM, to improve channel capacity by activating one or more RISs within multiple RIS (MRIS). Generally, MRIS suffers from the secondary reflection that, in imperfect condition, can be considered as inter-RIS interference (IRI) within MRIS. On the other hand, the proposed MRB-IM has a larger capacity but less of an effect on IRI because it only happens between the active MRIS. The optimal combination algorithm is applied to select the best possible RIS combinations for the activation bits in IM. The simulation is done to support the analytical channel capacity based on mutual information and bit error rate. The results show that the proposed system offers higher capacity and a lower bit error rate compared to the conventional MRIS. The complexity of the system is mentioned as another benefit of MRB-IM.
本研究提出了基于多重可重构智能表面（RIS）的索引调制（IM），称为 MRB-IM，通过激活多重 RIS（MRIS）中的一个或多个 RIS 来提高信道容量。一般来说，MRIS 会受到二次反射的影响，在不完善的条件下，二次反射可被视为 MRIS 内的 RIS 间干扰（IRI）。另一方面，拟议的 MRB-IM 容量更大，但对 IRI 的影响较小，因为 IRI 只发生在活动的 MRIS 之间。优化组合算法用于为 IM 中的激活比特选择最佳可能的 RIS 组合。仿真支持基于互信息和误码率的分析信道容量。结果表明，与传统的 MRIS 相比，提议的系统具有更高的容量和更低的误码率。系统的复杂性是 MRB-IM 的另一个优点。
{"title":"Multiple Reconfigurable Intelligent Surface-Based Index Modulation With Optimal Combination","authors":"Nadhira Azizah Suwanda;Hye Yeong Lee;Soo Young Shin","doi":"10.1109/JSYST.2024.3425866","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3425866","url":null,"abstract":"This study proposes multiple reconfigurable intelligent surface (RIS)-based index modulation (IM), called MRB-IM, to improve channel capacity by activating one or more RISs within multiple RIS (MRIS). Generally, MRIS suffers from the secondary reflection that, in imperfect condition, can be considered as inter-RIS interference (IRI) within MRIS. On the other hand, the proposed MRB-IM has a larger capacity but less of an effect on IRI because it only happens between the active MRIS. The optimal combination algorithm is applied to select the best possible RIS combinations for the activation bits in IM. The simulation is done to support the analytical channel capacity based on mutual information and bit error rate. The results show that the proposed system offers higher capacity and a lower bit error rate compared to the conventional MRIS. The complexity of the system is mentioned as another benefit of MRB-IM.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1651-1657"},"PeriodicalIF":4.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The achievement of sustainable development goals requires implementing a design approach to the industrial product development aimed to reduce waste, and to increase value, in accordance to principles of the “circular economy.” To mitigate the product obsolescence rate, the design activity aims at making the product multifunctional, its life longer and its maintainability more effective. Moreover, the product must be easily reparable, adaptable to operating conditions, friendly updatable, and reusable after decommissioning. Those targets affect the design methodology, and require some suitable tools. This article investigates how the “model-based systems engineering” is applied to the “circular design,” to provide a sustainable product life, and to regenerate the system, while decommissioning. Particularly, the direct experience of machine designer of industrial product, being the result of material processing and manufacturing, is considered. Are matter of discussion the identification of some issues related to sustainability and decommissioning, the methodologic tools useful to integrate the two approaches, the impact on the metamodeling activity, and the interoperable tool chain exploited. An industrial test case, as the automated guided vehicle, is preliminarily discussed to describe the implementation of the above-mentioned concepts and to identify any potential critical issues.
{"title":"A Roadmap to the Integration Between Systems Engineering and Circular Design to Develop Sustainable Industrial Product","authors":"Eugenio Brusa;Chiara Gastaldi;Cristiana Delprete;Lorenzo Giorio","doi":"10.1109/JSYST.2024.3435025","DOIUrl":"10.1109/JSYST.2024.3435025","url":null,"abstract":"The achievement of sustainable development goals requires implementing a design approach to the industrial product development aimed to reduce waste, and to increase value, in accordance to principles of the “circular economy.” To mitigate the product obsolescence rate, the design activity aims at making the product multifunctional, its life longer and its maintainability more effective. Moreover, the product must be easily reparable, adaptable to operating conditions, friendly updatable, and reusable after decommissioning. Those targets affect the design methodology, and require some suitable tools. This article investigates how the “model-based systems engineering” is applied to the “circular design,” to provide a sustainable product life, and to regenerate the system, while decommissioning. Particularly, the direct experience of machine designer of industrial product, being the result of material processing and manufacturing, is considered. Are matter of discussion the identification of some issues related to sustainability and decommissioning, the methodologic tools useful to integrate the two approaches, the impact on the metamodeling activity, and the interoperable tool chain exploited. An industrial test case, as the automated guided vehicle, is preliminarily discussed to describe the implementation of the above-mentioned concepts and to identify any potential critical issues.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1693-1704"},"PeriodicalIF":4.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1109/JSYST.2024.3432796
Zhiqiang Xu;Dengxiu Yu;Yan-Jun Liu;Zhen Wang
In this article, we propose an approximate optimal control strategy for a class of nonlinear multiagent system pursuit–evasion games. Herein, multiple pursuers aim to capture multiple evaders trying to evade capture. Under the competitive framework, agents not only pursue their individual goals but also consider coordination with their teammates to achieve collective objectives. However, maintaining cohesion with teammates in existing distributed control methods has always been a challenge. To enhance team coordination, we employ a graph-theoretic approach to represent the relationships between agents. Based on this, we design a dynamic target graph algorithm to enhance the coordination among pursuers. The approximate optimal strategies for each agent are solved by utilizing the Hamilton–Jacobi–Isaacs equations of the system. As solving these equations becomes computationally intensive in multiagent scenarios, we propose a value-based single network adaptive critic network architecture. In addition, we consider scenarios where the numbers of agents on both sides are inconsistent and address the phenomenon of input saturation. Moreover, we provide sufficient conditions to prove the system's stability. Finally, simulations conducted in two representative scenarios, multiple-pursuer-one-evader and multiple-pursuer-multiple-evader, demonstrate the effectiveness of our proposed algorithm.
{"title":"Approximate Optimal Strategy for Multiagent System Pursuit–Evasion Game","authors":"Zhiqiang Xu;Dengxiu Yu;Yan-Jun Liu;Zhen Wang","doi":"10.1109/JSYST.2024.3432796","DOIUrl":"10.1109/JSYST.2024.3432796","url":null,"abstract":"In this article, we propose an approximate optimal control strategy for a class of nonlinear multiagent system pursuit–evasion games. Herein, multiple pursuers aim to capture multiple evaders trying to evade capture. Under the competitive framework, agents not only pursue their individual goals but also consider coordination with their teammates to achieve collective objectives. However, maintaining cohesion with teammates in existing distributed control methods has always been a challenge. To enhance team coordination, we employ a graph-theoretic approach to represent the relationships between agents. Based on this, we design a dynamic target graph algorithm to enhance the coordination among pursuers. The approximate optimal strategies for each agent are solved by utilizing the Hamilton–Jacobi–Isaacs equations of the system. As solving these equations becomes computationally intensive in multiagent scenarios, we propose a value-based single network adaptive critic network architecture. In addition, we consider scenarios where the numbers of agents on both sides are inconsistent and address the phenomenon of input saturation. Moreover, we provide sufficient conditions to prove the system's stability. Finally, simulations conducted in two representative scenarios, multiple-pursuer-one-evader and multiple-pursuer-multiple-evader, demonstrate the effectiveness of our proposed algorithm.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1669-1680"},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1109/JSYST.2024.3432449
Dadmehr Rahbari;Foisal Ahmed;Maksim Jenihhin;Muhammad Mahtab Alam;Yannick Le Moullec
The rapid advancement of autonomous and heterogeneous unmanned aerial vehicle (UAV) swarms necessitates efficient computation offloading (CO) strategies to optimize their performance in industries, e.g., disaster management, surveillance, and environmental monitoring. UAVs face constraints such as limited energy, latency requirements, and failure risks, making robust CO approaches essential. Current CO methods often fall short due to high energy consumption, increased latency, and reliability issues in challenging conditions. This work introduces a novel collaborative CO strategy to address these deficiencies. Our approach utilizes a Bayesian network for failure mode effect analysis, considering communication bit error probabilities among multiantenna UAVs. We further employ rating-based federated deep learning to optimize decision-making, determining the best CO destination for each UAV based on factors like positions and resource capacities. Our strategy significantly outperforms existing benchmarks and state-of-the-art methods. It decreases the average probability of critical task failure by 43% and reduces energy consumption by 15% on average ensuring UAV swarms can meet strict constraints in harsh environments. These improvements demonstrate the utility of our approach in enhancing the operational reliability and efficiency of UAV swarms across diverse applications.
{"title":"Reliability-Critical Computation Offloading in UAV Swarms","authors":"Dadmehr Rahbari;Foisal Ahmed;Maksim Jenihhin;Muhammad Mahtab Alam;Yannick Le Moullec","doi":"10.1109/JSYST.2024.3432449","DOIUrl":"10.1109/JSYST.2024.3432449","url":null,"abstract":"The rapid advancement of autonomous and heterogeneous unmanned aerial vehicle (UAV) swarms necessitates efficient computation offloading (CO) strategies to optimize their performance in industries, e.g., disaster management, surveillance, and environmental monitoring. UAVs face constraints such as limited energy, latency requirements, and failure risks, making robust CO approaches essential. Current CO methods often fall short due to high energy consumption, increased latency, and reliability issues in challenging conditions. This work introduces a novel collaborative CO strategy to address these deficiencies. Our approach utilizes a Bayesian network for failure mode effect analysis, considering communication bit error probabilities among multiantenna UAVs. We further employ rating-based federated deep learning to optimize decision-making, determining the best CO destination for each UAV based on factors like positions and resource capacities. Our strategy significantly outperforms existing benchmarks and state-of-the-art methods. It decreases the average probability of critical task failure by 43% and reduces energy consumption by 15% on average ensuring UAV swarms can meet strict constraints in harsh environments. These improvements demonstrate the utility of our approach in enhancing the operational reliability and efficiency of UAV swarms across diverse applications.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1871-1882"},"PeriodicalIF":4.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/JSYST.2024.3422284
Yi Chen;Tianyi Wu;Xiaobing Ma;Jingjing Wang;Rui Peng;Li Yang
Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.
{"title":"System Maintenance Optimization Under Structural Dependency: A Dynamic Grouping Approach","authors":"Yi Chen;Tianyi Wu;Xiaobing Ma;Jingjing Wang;Rui Peng;Li Yang","doi":"10.1109/JSYST.2024.3422284","DOIUrl":"10.1109/JSYST.2024.3422284","url":null,"abstract":"Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1605-1616"},"PeriodicalIF":4.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1109/JSYST.2024.3409231
Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell
Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.
任务工程（ME）是一种新兴的方法，用于设计和分析用于完成一项或多项任务的系统配置（SoS）。任务工程旨在灵活利用 SoS 的能力，动态调整其配置，以满足不断变化的任务需求。然而，如今的 SoS 配置仍然是静态的，是为完成任务而精心设计的。我们在本文中要解决的问题是开发建模和分析技术，以便灵活集成和自适应选择潜在的 SoS 配置，从而在执行空间中以总体敏捷性完成多种任务。我们为 ME 提出了一个基础框架，其中包含表示 ME 设计空间（MEDS）的语义和语法，以及一套逻辑和数学建模方法，可将 MEDS 借用于稳健的 SoS 分析方法。具体来说，该框架建议开发一种以任务为重点的本体论和特定领域语言，以实现 MEDS 的一致语义表述，然后使用基于集合的设计原则和艾伦区间代数对其进行逻辑评估，以确定在形成 SoS 配置时的空间和时间一致性。然后，利用图论和多属性效用理论对由此产生的可行 SoS 配置进行任务成功率评估。该框架的应用在一个简化和概念化的感知-决定-效应问题上进行了演示，该问题旨在利用 SoS 灵活完成多个任务。
{"title":"Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis","authors":"Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell","doi":"10.1109/JSYST.2024.3409231","DOIUrl":"10.1109/JSYST.2024.3409231","url":null,"abstract":"Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1549-1559"},"PeriodicalIF":4.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/JSYST.2024.3426096
Yijiu Li;Dang Van Huynh;Van-Linh Nguyen;Dac-Binh Ha;Hans-Jürgen Zepernick;Trung Q. Duong
In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.
{"title":"Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach","authors":"Yijiu Li;Dang Van Huynh;Van-Linh Nguyen;Dac-Binh Ha;Hans-Jürgen Zepernick;Trung Q. Duong","doi":"10.1109/JSYST.2024.3426096","DOIUrl":"10.1109/JSYST.2024.3426096","url":null,"abstract":"In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1828-1838"},"PeriodicalIF":4.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1109/JSYST.2024.3420237
Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed
This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.
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Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
{"title":"DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows","authors":"Abdullah Lakhan;Mazin Abed Mohammed;Dilovan Asaad Zebar;Karrar Hameed Abdulkareem;Muhammet Deveci;Haydar Abdulameer Marhoon;Jan Nedoma;Radek Martinek","doi":"10.1109/JSYST.2024.3424259","DOIUrl":"10.1109/JSYST.2024.3424259","url":null,"abstract":"Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1883-1892"},"PeriodicalIF":4.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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