Pub Date : 2025-01-14DOI: 10.1109/JSYST.2024.3524624
Weiyi Ni;Hailin Xiao;Xiaolan Liu;Anthony Theodore Chronopoulos;Petros A. Ioannou
A large number of devices communicating through the backhaul links in heterogeneous cellular networks (HetCNets) will incur severe traffic congestion, high delay, and huge energy consumption. Enabling caching capabilities has been regarded as a promising way to reduce the high-delay pressure of file delivery on the backhaul links. However, file delivery has different requirements on quality-of-service, and the increased densification of small base stations will consume more energy, thus it is essential to solve the problem of delay and energy in cache-enabled HetCNets. In this article, we employ a utility function with the weighted sum of energy consumption and delay cost to formulate the delay-energy tradeoff optimization problem that is a kind of multiobjective optimization problem. Considering a jointly performed mechanism comprising power control, user association, and content caching, the optimization problem is further decomposed into the primal problem and the main problem by generalized Benders decomposition method. Here, the primal problem is related to power-aware user association that dynamically associates with the base station, and the main problem is related to content caching. Furthermore, an iterative power-aware user association and content caching algorithm (PAUA-CC) is proposed. Finally, we present numerical simulation results to demonstrate the effectiveness of our proposed approach on the optimal delay-energy tradeoff.
{"title":"Power-Aware User Association and Content Caching Algorithm for Delay-Energy Tradeoff in Cache-Enabled Heterogeneous Cellular Networks","authors":"Weiyi Ni;Hailin Xiao;Xiaolan Liu;Anthony Theodore Chronopoulos;Petros A. Ioannou","doi":"10.1109/JSYST.2024.3524624","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3524624","url":null,"abstract":"A large number of devices communicating through the backhaul links in heterogeneous cellular networks (HetCNets) will incur severe traffic congestion, high delay, and huge energy consumption. Enabling caching capabilities has been regarded as a promising way to reduce the high-delay pressure of file delivery on the backhaul links. However, file delivery has different requirements on quality-of-service, and the increased densification of small base stations will consume more energy, thus it is essential to solve the problem of delay and energy in cache-enabled HetCNets. In this article, we employ a utility function with the weighted sum of energy consumption and delay cost to formulate the delay-energy tradeoff optimization problem that is a kind of multiobjective optimization problem. Considering a jointly performed mechanism comprising power control, user association, and content caching, the optimization problem is further decomposed into the primal problem and the main problem by generalized Benders decomposition method. Here, the primal problem is related to power-aware user association that dynamically associates with the base station, and the main problem is related to content caching. Furthermore, an iterative power-aware user association and content caching algorithm (PAUA-CC) is proposed. Finally, we present numerical simulation results to demonstrate the effectiveness of our proposed approach on the optimal delay-energy tradeoff.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"246-257"},"PeriodicalIF":4.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645230","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 : 2025-01-10DOI: 10.1109/JSYST.2024.3523485
Zirui Liao;Jian Shi;Yuwei Zhang;Shaoping Wang;Rentong Chen;Zhiyong Sun
This article addresses the resilient rendezvous problem for leader–follower multiagent systems (MASs) in the presence of adversarial attacks. A novel leader–follower attack-tolerant (LFAT) algorithm is developed to ensure that the healthy followers reach rendezvous on the reference value propagated by healthy leaders. Compared with the existing weighted mean-subsequence-reduced algorithm, the proposed LFAT algorithm includes a necessary state initialization step for leader and follower agents and an improved threat elimination step, so that more effective information can be retained for state updates. The necessary and sufficient condition on the network topology is further derived to ensure resilient rendezvous for leader–follower MASs. Compared with the existing resilient algorithms, the proposed LFAT algorithm enables MASs to achieve leader–follower resilient rendezvous under relaxed graph robustness conditions, so that the network redundancy is mitigated. Several numerical examples are given to illustrate the superior performance of the LFAT algorithm and the scalability to larger-scale and time-varying networks.
{"title":"A Leader–Follower Attack-Tolerant Algorithm for Resilient Rendezvous With Reduced Network Redundancy","authors":"Zirui Liao;Jian Shi;Yuwei Zhang;Shaoping Wang;Rentong Chen;Zhiyong Sun","doi":"10.1109/JSYST.2024.3523485","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3523485","url":null,"abstract":"This article addresses the resilient rendezvous problem for leader–follower multiagent systems (MASs) in the presence of adversarial attacks. A novel leader–follower attack-tolerant (LFAT) algorithm is developed to ensure that the healthy followers reach rendezvous on the reference value propagated by healthy leaders. Compared with the existing weighted mean-subsequence-reduced algorithm, the proposed LFAT algorithm includes a necessary state initialization step for leader and follower agents and an improved threat elimination step, so that more effective information can be retained for state updates. The necessary and sufficient condition on the network topology is further derived to ensure resilient rendezvous for leader–follower MASs. Compared with the existing resilient algorithms, the proposed LFAT algorithm enables MASs to achieve leader–follower resilient rendezvous under relaxed graph robustness conditions, so that the network redundancy is mitigated. Several numerical examples are given to illustrate the superior performance of the LFAT algorithm and the scalability to larger-scale and time-varying networks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"212-223"},"PeriodicalIF":4.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637950","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 : 2025-01-10DOI: 10.1109/JSYST.2024.3524246
Biwei Li;Dong Liu;Junyuan Fang;Xi Zhang;Chi K. Tse
Cascading failure, characterized by the widespread propagation of failure events, is a common cause for severe blackouts in power networks. Strengthening critical branches in a power network is crucial for mitigating the risk of blackouts resulting from cascading failures. In this article, we propose a time-efficient greedy search method to identify critical branches in a power network. We address the challenge of computational constraints by using a failure propagation graph, which accurately captures the critical failure propagation patterns based on cascading failure simulation. Our approach minimizes cascading failure risk while strategically reinforcing a limited number of branches. The failure-propagation-graph greedy-search (FPG-GS) algorithm selects candidate branches based on cascading failure simulation and iteratively identifies the most crucial branches. Our experimental results on different power systems demonstrate the superior performance and efficiency of the FPG-GS algorithm compared to existing methods. In addition, our study highlights the importance of strategic branch selection, showing that reinforcing one-fifth of the branches can achieve a mitigation rate exceeding 80%.
{"title":"Failure Propagation Graphs for Studying Cascading Failure Propagation in Power Networks","authors":"Biwei Li;Dong Liu;Junyuan Fang;Xi Zhang;Chi K. Tse","doi":"10.1109/JSYST.2024.3524246","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3524246","url":null,"abstract":"Cascading failure, characterized by the widespread propagation of failure events, is a common cause for severe blackouts in power networks. Strengthening critical branches in a power network is crucial for mitigating the risk of blackouts resulting from cascading failures. In this article, we propose a time-efficient greedy search method to identify critical branches in a power network. We address the challenge of computational constraints by using a failure propagation graph, which accurately captures the critical failure propagation patterns based on cascading failure simulation. Our approach minimizes cascading failure risk while strategically reinforcing a limited number of branches. The failure-propagation-graph greedy-search (FPG-GS) algorithm selects candidate branches based on cascading failure simulation and iteratively identifies the most crucial branches. Our experimental results on different power systems demonstrate the superior performance and efficiency of the FPG-GS algorithm compared to existing methods. In addition, our study highlights the importance of strategic branch selection, showing that reinforcing one-fifth of the branches can achieve a mitigation rate exceeding 80%.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"258-269"},"PeriodicalIF":4.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645240","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 : 2025-01-06DOI: 10.1109/JSYST.2024.3521477
R. A. Haugen;S. Kokkula;A. Ghaderi;G. Muller;E. Syverud
This article applies the design of experiments approach for detecting detrimental weak emergent behavior of an autonomous surface vessel operating in a dynamic environment on a search and rescue mission. The research utilizes orthogonal arrays in combination with regression analysis to systematically test the parameter space of an engineered system function. We explored the parameter space of interest and detected where the system model does not comply with a defined measure of effectiveness. The findings from this case study suggest that these methods enable a systematic exploration of the system's parameter space, allowing for the effective detection of detrimental weak emergent behavior. This approach potentially enhances test coverage, expands system operating knowledge and facilitates mitigation efforts more efficiently.
{"title":"Detection of Detrimental Weak Emergent Behavior Considering Operational Factors: A Case Study in Search and Rescue","authors":"R. A. Haugen;S. Kokkula;A. Ghaderi;G. Muller;E. Syverud","doi":"10.1109/JSYST.2024.3521477","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3521477","url":null,"abstract":"This article applies the design of experiments approach for detecting detrimental weak emergent behavior of an autonomous surface vessel operating in a dynamic environment on a search and rescue mission. The research utilizes orthogonal arrays in combination with regression analysis to systematically test the parameter space of an engineered system function. We explored the parameter space of interest and detected where the system model does not comply with a defined measure of effectiveness. The findings from this case study suggest that these methods enable a systematic exploration of the system's parameter space, allowing for the effective detection of detrimental weak emergent behavior. This approach potentially enhances test coverage, expands system operating knowledge and facilitates mitigation efforts more efficiently.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"43-54"},"PeriodicalIF":4.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637896","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}
In this article, a game-theoretic joint coalition formation and power allocation (JCFPA) strategy is investigated for multitarget tracking (MTT) in a distributed radar network. The main objective of the presented strategy is to minimize the total transmit power consumption and enhance the target tracking accuracy concurrently, while adhering to predefined requirements on the MTT performance and system illumination resource budgets, thus improving the low probability of intercept performance. To achieve this, a utility function is developed to evaluate the coalition structure, transmit power consumption, and tracking accuracy. Then, by formulating the cooperative interactions among radars as a coalition game, we establish an optimization model to optimize the coalition structure and power allocation for the distributed radar network. The existence of the Nash equilibrium solution for the game is proven mathematically. To address the optimization model, an iterative three-step algorithm is developed based on the sequential quadratic programming. Numerical results reveal that the presented JCFPA strategy obtains superior system performance compared to other benchmarks.
{"title":"Game-Theoretic Joint Coalition Formation and Power Allocation Strategy for Multitarget Tracking in Distributed Radar Network","authors":"Chenguang Shi;Xuezhang Sun;Xiangrong Dai;Jianjiang Zhou","doi":"10.1109/JSYST.2024.3522100","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3522100","url":null,"abstract":"In this article, a game-theoretic joint coalition formation and power allocation (JCFPA) strategy is investigated for multitarget tracking (MTT) in a distributed radar network. The main objective of the presented strategy is to minimize the total transmit power consumption and enhance the target tracking accuracy concurrently, while adhering to predefined requirements on the MTT performance and system illumination resource budgets, thus improving the low probability of intercept performance. To achieve this, a utility function is developed to evaluate the coalition structure, transmit power consumption, and tracking accuracy. Then, by formulating the cooperative interactions among radars as a coalition game, we establish an optimization model to optimize the coalition structure and power allocation for the distributed radar network. The existence of the Nash equilibrium solution for the game is proven mathematically. To address the optimization model, an iterative three-step algorithm is developed based on the sequential quadratic programming. Numerical results reveal that the presented JCFPA strategy obtains superior system performance compared to other benchmarks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"234-245"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637948","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 : 2024-12-31DOI: 10.1109/JSYST.2024.3519887
Anna Gaydamaka;Mahmoud T. Kabir;Andrey Samuylov;Dmitri Moltchanov;Bo Tan;Yevgeni Koucheryavy
Numerous mission-critical unmanned aerial vehicle (UAV) operations, such as rescue and surveillance missions, are conducted in areas lacking access to external infrastructure providing precise positioning information. To enhance situational awareness in such scenarios, millimeter wave (mmWave, $text{30}-text{300}$ GHz) and subterahertz (sub-THz, $text{100}-text{300}$ GHz) range or Doppler radars promising large resolution can be employed. However, the small antenna apertures in these bands naturally call for the use of massive antenna arrays to achieve reasonable detection distances. By employing directional antenna arrays, these radars need to exhaustively scan the surroundings to ensure situational awareness of a UAV in a swarm. The aim of this article is to characterize the system-level performance of mmWave/sub-THz range and Doppler radars as a function of system parameters by accounting for the propagation specifics of the considered bands. To this aim, we combine the tools of stochastic geometry and antenna simulations to determine the optimal half-power beamwidth that minimizes the full scanning time while maximizing the detection probability of all UAVs in a swarm. Our results demonstrate that both types of radars are characterized by qualitatively similar performance. Detection performance is highly sensitive to both UAV density and coverage radius, as the increase in these parameters leads to an abrupt drop in the detection performance. At small distances, $leq!text{50}$ m, antenna arrays with a smaller number of elements result in the best tradeoff between scanning time and detection probability. Specifically, both types of radars provide perfect knowledge of the surroundings (with detection probability higher than 0.99) within a 50-m radius with a scanning time of less than 1 ms. At greater distances, $geq! text{100}$ m, the only option to improve performance is a drastic increase in the emitted power or receiver sensitivity.
{"title":"System-Level Analysis of the Directional Radar Coverage for UAV Localization in Dynamic Swarms","authors":"Anna Gaydamaka;Mahmoud T. Kabir;Andrey Samuylov;Dmitri Moltchanov;Bo Tan;Yevgeni Koucheryavy","doi":"10.1109/JSYST.2024.3519887","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3519887","url":null,"abstract":"Numerous mission-critical unmanned aerial vehicle (UAV) operations, such as rescue and surveillance missions, are conducted in areas lacking access to external infrastructure providing precise positioning information. To enhance situational awareness in such scenarios, millimeter wave (mmWave, <inline-formula><tex-math>$text{30}-text{300}$</tex-math></inline-formula> GHz) and subterahertz (sub-THz, <inline-formula><tex-math>$text{100}-text{300}$</tex-math></inline-formula> GHz) range or Doppler radars promising large resolution can be employed. However, the small antenna apertures in these bands naturally call for the use of massive antenna arrays to achieve reasonable detection distances. By employing directional antenna arrays, these radars need to exhaustively scan the surroundings to ensure situational awareness of a UAV in a swarm. The aim of this article is to characterize the system-level performance of mmWave/sub-THz range and Doppler radars as a function of system parameters by accounting for the propagation specifics of the considered bands. To this aim, we combine the tools of stochastic geometry and antenna simulations to determine the optimal half-power beamwidth that minimizes the full scanning time while maximizing the detection probability of all UAVs in a swarm. Our results demonstrate that both types of radars are characterized by qualitatively similar performance. Detection performance is highly sensitive to both UAV density and coverage radius, as the increase in these parameters leads to an abrupt drop in the detection performance. At small distances, <inline-formula><tex-math>$leq!text{50}$</tex-math></inline-formula> m, antenna arrays with a smaller number of elements result in the best tradeoff between scanning time and detection probability. Specifically, both types of radars provide perfect knowledge of the surroundings (with detection probability higher than 0.99) within a 50-m radius with a scanning time of less than 1 ms. At greater distances, <inline-formula><tex-math>$geq! text{100}$</tex-math></inline-formula> m, the only option to improve performance is a drastic increase in the emitted power or receiver sensitivity.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"164-175"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637897","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 : 2024-12-31DOI: 10.1109/JSYST.2024.3519516
Lei Zhang;Ting Wu;Jianwei Liu;Zhenyu Guan;Xiaodong Yin
The advancement of wireless network technology has propelled wireless medical sensor networks (WMSNs) to transform healthcare, offering efficient communication for enhanced quality of life. These networks employ sensitive and resource-efficient sensors to monitor and transmit patients' vital health data to medical professionals through wireless channels. However, the openness of these channels risks unauthorized access and data tampering, jeopardizing patient privacy and treatment efficacy. Ensuring the integrity and confidentiality of health data is crucial. Current authentication and key agreement (AKA) protocols have limitations, including susceptibility to sensor information disclosure and security flaws due to excessive user authority and mismatched pseudorandom identities. Given the resource constraints of WMSNs, traditional cryptographic methods are not always suitable. To overcome these challenges, a lightweight AKA protocol with self-adaptive synchronization and authority management is proposed. Formal verification through the real-or-random model, BAN logic, and ProVerif tool confirms its security and availability, while informal analysis demonstrates its robust security features. Comparative analysis with recent schemes also highlights its superiority and fitness for WMSNs.
{"title":"An Adaptive Synchronous Lightweight AKA Protocol With Authority Management for Wireless Medical Sensor Networks","authors":"Lei Zhang;Ting Wu;Jianwei Liu;Zhenyu Guan;Xiaodong Yin","doi":"10.1109/JSYST.2024.3519516","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3519516","url":null,"abstract":"The advancement of wireless network technology has propelled wireless medical sensor networks (WMSNs) to transform healthcare, offering efficient communication for enhanced quality of life. These networks employ sensitive and resource-efficient sensors to monitor and transmit patients' vital health data to medical professionals through wireless channels. However, the openness of these channels risks unauthorized access and data tampering, jeopardizing patient privacy and treatment efficacy. Ensuring the integrity and confidentiality of health data is crucial. Current authentication and key agreement (AKA) protocols have limitations, including susceptibility to sensor information disclosure and security flaws due to excessive user authority and mismatched pseudorandom identities. Given the resource constraints of WMSNs, traditional cryptographic methods are not always suitable. To overcome these challenges, a lightweight AKA protocol with self-adaptive synchronization and authority management is proposed. Formal verification through the real-or-random model, BAN logic, and ProVerif tool confirms its security and availability, while informal analysis demonstrates its robust security features. Comparative analysis with recent schemes also highlights its superiority and fitness for WMSNs.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"200-211"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637952","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 : 2024-12-27DOI: 10.1109/JSYST.2024.3519536
Jingqing Wang;Wenchi Cheng;Wei Zhang
The age of information (AoI) has recently gained recognition as a critical quality-of-service (QoS) metric for quantifying the freshness of status updates, playing a crucial role in supporting massive ultrareliable and low-latency communications (mURLLCs). In mURLLC scenarios, status updates generally involve the transmission through applying finite blocklength coding (FBC) to efficiently encode small update packets while meeting stringent error-rate and latency-bounded QoS constraints. However, due to inherent system dynamics and varying environmental conditions, optimizing AoI under such multi-QoS constraints often results in nonconvex and computationally intractable problems. Motivated by the demonstrated efficacy of deep reinforcement learning (DRL) in addressing large-scale networking challenges, this work aims to apply DRL techniques to derive optimal resource allocation solutions in real time. Despite its potential, the effective integration of FBC in DRL-based AoI optimization remains underexplored, especially in addressing the challenge of simultaneously upper bounding both delay and error rate. To address these challenges, we propose a DRL-based framework for AoI-aware optimal resource allocation in mURLLC-driven multi-QoS schemes, leveraging AoI as a core metric within the finite blocklength regime. First, we design a wireless communication architecture and AoI-based modeling framework that incorporates FBC. Second, we proceed by deriving upper bounded peak AoI and delay violation probabilities using stochastic network calculus. Subsequently, we formulate an optimization problem aimed at minimizing the peak AoI violation probability through FBC. Third, we develop DRL algorithms to determine optimal resource allocation policies that meet statistical delay and error-rate requirements for mURLLC. Finally, to validate the effectiveness of the developed schemes, we have executed a series of simulations.
{"title":"AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning","authors":"Jingqing Wang;Wenchi Cheng;Wei Zhang","doi":"10.1109/JSYST.2024.3519536","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3519536","url":null,"abstract":"The age of information (AoI) has recently gained recognition as a critical quality-of-service (QoS) metric for quantifying the freshness of status updates, playing a crucial role in supporting massive ultrareliable and low-latency communications (mURLLCs). In mURLLC scenarios, status updates generally involve the transmission through applying finite blocklength coding (FBC) to efficiently encode small update packets while meeting stringent error-rate and latency-bounded QoS constraints. However, due to inherent system dynamics and varying environmental conditions, optimizing AoI under such multi-QoS constraints often results in nonconvex and computationally intractable problems. Motivated by the demonstrated efficacy of deep reinforcement learning (DRL) in addressing large-scale networking challenges, this work aims to apply DRL techniques to derive optimal resource allocation solutions in real time. Despite its potential, the effective integration of FBC in DRL-based AoI optimization remains underexplored, especially in addressing the challenge of simultaneously upper bounding both delay and error rate. To address these challenges, we propose a DRL-based framework for AoI-aware optimal resource allocation in mURLLC-driven multi-QoS schemes, leveraging AoI as a core metric within the finite blocklength regime. First, we design a wireless communication architecture and AoI-based modeling framework that incorporates FBC. Second, we proceed by deriving upper bounded peak AoI and delay violation probabilities using stochastic network calculus. Subsequently, we formulate an optimization problem aimed at minimizing the peak AoI violation probability through FBC. Third, we develop DRL algorithms to determine optimal resource allocation policies that meet statistical delay and error-rate requirements for mURLLC. Finally, to validate the effectiveness of the developed schemes, we have executed a series of simulations.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"305-316"},"PeriodicalIF":4.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645183","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}
Mobile edge computing (MEC) plays a pivotal role in the Internet of Vehicles and the Internet of Things. Edge server deployment is the initial step in establishing edge computing systems, which impact the overall system performance significantly. Besides, the performance of an edge computing system is also contingent upon the type of service deployed on servers, in the case of the same server deployment, different deployment of services will bring different profits. Most current studies concentrate solely on the former aspect, neglecting the optimization of service deployment in MEC system. In this article, we proposed a two-step method KPSOP for edge server and edge service deployment, aiming to reduce time delay, balance load, and improve the profit of MEC system, and KPSOP includes clustering algorithm and heuristic algorithm. We considered the location distribution of base stations, the task requests of vehicle users, the resource limitations of edge servers, etc. First, the edge server deployment was completed with the goal of minimizing time delay and load balancing. Second, the service deployment was completed with the goal of maximizing edge server profit. The experiments were based on real world base station information. The simulation results validate that our algorithm is more stable and converges faster. In addition, compared to other algorithms, it performs better in load balance and increasing profit.
{"title":"Edge Server and Service Deployment Considering Profit With Improved PSO in IoV","authors":"Junhui Zhao;Yuwen Huang;Qingmiao Zhang;Dongming Wang;Wei Xu","doi":"10.1109/JSYST.2024.3512871","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3512871","url":null,"abstract":"Mobile edge computing (MEC) plays a pivotal role in the Internet of Vehicles and the Internet of Things. Edge server deployment is the initial step in establishing edge computing systems, which impact the overall system performance significantly. Besides, the performance of an edge computing system is also contingent upon the type of service deployed on servers, in the case of the same server deployment, different deployment of services will bring different profits. Most current studies concentrate solely on the former aspect, neglecting the optimization of service deployment in MEC system. In this article, we proposed a two-step method KPSOP for edge server and edge service deployment, aiming to reduce time delay, balance load, and improve the profit of MEC system, and KPSOP includes clustering algorithm and heuristic algorithm. We considered the location distribution of base stations, the task requests of vehicle users, the resource limitations of edge servers, etc. First, the edge server deployment was completed with the goal of minimizing time delay and load balancing. Second, the service deployment was completed with the goal of maximizing edge server profit. The experiments were based on real world base station information. The simulation results validate that our algorithm is more stable and converges faster. In addition, compared to other algorithms, it performs better in load balance and increasing profit.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"55-64"},"PeriodicalIF":4.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637894","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 : 2024-12-17DOI: 10.1109/JSYST.2024.3511914
Jiayi Cai;Chengbo Yi;Xue Luo;Canrong Xiao
This article explores the adaptive event-triggered output consensus control problem for a class of switched stochastic multiagent systems (MASs) with unmeasured states and unknown nonlinearity. First, in order to overcome the limitations facing the average dwell-time method of consensus for switched MASs proposed in the existing works, the adaptive control protocol within the framework of mode-dependent average dwell time (MDADT) is introduced to expand the scope of applications. Furthermore, through a novel gain-scheduled state observer, the fuzzy logic systems are applied to approximate the unknown nonlinear functions. The dynamic surface design method is used to remove the need for derivative calculations of the constructed virtual controls, significantly the complexity of calculations. In addition, under the framework of backstepping design, the switching threshold event-triggered control strategy is developed to effectively decrease the communication load and balance the performance of MASs. The proposed control protocol ensures that all signals within the closed-loop systems are ultimately bounded under the MDADT switching property. Finally, the simulation results are obtained to validate the proposed control mechanism.
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