In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.
{"title":"Toward a Human-Cyber-Physical System for Real-Time Anomaly Detection","authors":"Bojana Bajic;Aleksandar Rikalovic;Nikola Suzic;Vincenzo Piuri","doi":"10.1109/JSYST.2024.3402978","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3402978","url":null,"abstract":"In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1308-1319"},"PeriodicalIF":4.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435544","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 : 2024-06-12DOI: 10.1109/JSYST.2024.3403500
Khaled Alanezi;Shivakant Mishra
Modern Internet of Things (IoT) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This article presents the design, implementation, and evaluation of a fog-based architecture that utilizes the concept of a digital twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. This article presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques, such as multilayer perceptron.
{"title":"An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario","authors":"Khaled Alanezi;Shivakant Mishra","doi":"10.1109/JSYST.2024.3403500","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3403500","url":null,"abstract":"Modern Internet of Things (IoT) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This article presents the design, implementation, and evaluation of a fog-based architecture that utilizes the concept of a digital twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. This article presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques, such as multilayer perceptron.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1224-1235"},"PeriodicalIF":4.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435546","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 : 2024-06-07DOI: 10.1109/JSYST.2024.3406800
Rongxing Guan;Huabo Liu;Keke Huang;Haisheng Yu
This article is concerned with the $H_infty$ performance problems for large-scale networked systems comprising many subsystems. The connections among these subsystems with different dynamics are arbitrary and linear time-invariant. Necessary and sufficient conditions have been derived for $H_infty$ performance, in which the system structure is sufficiently utilized and higher computational efficiency is obtained. Furthermore, several analysis conditions that rely solely on individual subsystem parameters are obtained. The effectiveness and ascendancy of the derived conditions are verified by some numerical simulations.
{"title":"$H_infty$ Performance Analysis of Large-Scale Networked Systems","authors":"Rongxing Guan;Huabo Liu;Keke Huang;Haisheng Yu","doi":"10.1109/JSYST.2024.3406800","DOIUrl":"10.1109/JSYST.2024.3406800","url":null,"abstract":"This article is concerned with the \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance problems for large-scale networked systems comprising many subsystems. The connections among these subsystems with different dynamics are arbitrary and linear time-invariant. Necessary and sufficient conditions have been derived for \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance, in which the system structure is sufficiently utilized and higher computational efficiency is obtained. Furthermore, several analysis conditions that rely solely on individual subsystem parameters are obtained. The effectiveness and ascendancy of the derived conditions are verified by some numerical simulations.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1528-1537"},"PeriodicalIF":4.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932828","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}
This article focuses on covert communication in a distributed network with multiple nonorthogonal multiple access (NOMA) systems, where each NOMA system is consisted of a transmitter, a legitimate public user, a covert user, and a warden. Power allocation for multiple transmitters in such network is a highly tricky problem, since it needs to addresses the issues of complex inter-NOMA system interference, constraints from both public users and covert users, and the optimization of overall network performance. We first conduct a theoretical analysis to depict the inherent relationship between the inter-NOMA system interference and transmit power of transmitters. With the help of the interference analysis, we then develop a theoretical framework for the modeling of detection error probability, covert rate, and public rate in each NOMA system. Based on these results and the constraints from both public users and covert users, we formulate the concerned power allocation problem as a Markov decision process, and further develop multiagent reinforcement learning (RL) algorithms to identify the optimal power allocation among transmitters to maximize the sum-rate of the overall network. Finally, numerical results are provided to illustrate the efficiency of our RL algorithms for power allocation in multi-NOMA networks.
{"title":"RL-Assisted Power Allocation for Covert Communication in Distributed NOMA Networks","authors":"Jiaqing Bai;Ji He;Yanping Chen;Yulong Shen;Xiaohong Jiang","doi":"10.1109/JSYST.2024.3406035","DOIUrl":"10.1109/JSYST.2024.3406035","url":null,"abstract":"This article focuses on covert communication in a distributed network with multiple nonorthogonal multiple access (NOMA) systems, where each NOMA system is consisted of a transmitter, a legitimate public user, a covert user, and a warden. Power allocation for multiple transmitters in such network is a highly tricky problem, since it needs to addresses the issues of complex inter-NOMA system interference, constraints from both public users and covert users, and the optimization of overall network performance. We first conduct a theoretical analysis to depict the inherent relationship between the inter-NOMA system interference and transmit power of transmitters. With the help of the interference analysis, we then develop a theoretical framework for the modeling of detection error probability, covert rate, and public rate in each NOMA system. Based on these results and the constraints from both public users and covert users, we formulate the concerned power allocation problem as a Markov decision process, and further develop multiagent reinforcement learning (RL) algorithms to identify the optimal power allocation among transmitters to maximize the sum-rate of the overall network. Finally, numerical results are provided to illustrate the efficiency of our RL algorithms for power allocation in multi-NOMA networks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1504-1515"},"PeriodicalIF":4.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932720","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 : 2024-06-05DOI: 10.1109/JSYST.2024.3403673
Raj Mohan Singh;Geeta Sikka;Lalit Kumar Awasthi
With the rapid advancement of Internet of Things technology, the field of fog computing has garnered significant attention and hence become a workable processing platform for upcoming applications. However, compared with vast computing capability of the cloud, the fog nodes have resource constraints, are heterogeneous in nature, and highly distributed. Due to the growing demand as well as diversity of applications, the nodes in a fog network become overloaded, which makes load balancing a prime concern. In this work, a load balancing aware task selection and migration approach is proposed comprising two algorithms to select and place tasks from multiple overloaded nodes to suitable destination nodes. The Selection algorithm determines the tasks that should be migrated from overloaded nodes. Placement algorithm focuses on finding a near optimal solution by applying modified binary particle swarm optimization. Specifically, the objective is to minimize execution time and transfer time of tasks. Simulation studies conducted on iFogSim prove that the suggested approach outperforms the existing approaches in terms of task execution time, task transfer time, and makespan.
{"title":"LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment","authors":"Raj Mohan Singh;Geeta Sikka;Lalit Kumar Awasthi","doi":"10.1109/JSYST.2024.3403673","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3403673","url":null,"abstract":"With the rapid advancement of Internet of Things technology, the field of fog computing has garnered significant attention and hence become a workable processing platform for upcoming applications. However, compared with vast computing capability of the cloud, the fog nodes have resource constraints, are heterogeneous in nature, and highly distributed. Due to the growing demand as well as diversity of applications, the nodes in a fog network become overloaded, which makes load balancing a prime concern. In this work, a load balancing aware task selection and migration approach is proposed comprising two algorithms to select and place tasks from multiple overloaded nodes to suitable destination nodes. The Selection algorithm determines the tasks that should be migrated from overloaded nodes. Placement algorithm focuses on finding a near optimal solution by applying modified binary particle swarm optimization. Specifically, the objective is to minimize execution time and transfer time of tasks. Simulation studies conducted on iFogSim prove that the suggested approach outperforms the existing approaches in terms of task execution time, task transfer time, and makespan.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"796-804"},"PeriodicalIF":4.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435213","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 : 2024-06-05DOI: 10.1109/JSYST.2024.3403103
Lili Wang;Shiming Chen
This article gives an investigation to the connectivity preserving consensus (CPC) issue for the second-order heterogeneous multiagent systems (MASs), which are constituted by linear and nonlinear subsystem. First, a consensus algorithm for the system without input constraints is proposed and some sufficient conditions for consensus are obtained. Due to the limited communication distance of each agent, the algorithm maintains network connectivity based on potential function techniques. Then, considering the linear and nonlinear subsystem with input constraints, respectively, the results indicate that as long as certain conditions are met, all agents can be guaranteed to achieve CPC. Furthermore, the proposed algorithm is extended to the entire system with input constraints. Five examples are provided to demonstrate efficiency of theoretical results.
{"title":"Connectivity Preserving Consensus for Second-Order Heterogeneous MASs With Input Constraints","authors":"Lili Wang;Shiming Chen","doi":"10.1109/JSYST.2024.3403103","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3403103","url":null,"abstract":"This article gives an investigation to the connectivity preserving consensus (CPC) issue for the second-order heterogeneous multiagent systems (MASs), which are constituted by linear and nonlinear subsystem. First, a consensus algorithm for the system without input constraints is proposed and some sufficient conditions for consensus are obtained. Due to the limited communication distance of each agent, the algorithm maintains network connectivity based on potential function techniques. Then, considering the linear and nonlinear subsystem with input constraints, respectively, the results indicate that as long as certain conditions are met, all agents can be guaranteed to achieve CPC. Furthermore, the proposed algorithm is extended to the entire system with input constraints. Five examples are provided to demonstrate efficiency of theoretical results.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1471-1480"},"PeriodicalIF":4.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435228","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}
Edge intelligence has recently attracted great interest from industry and academia, and it greatly improves the processing speed at the edge by moving data and artificial intelligence to the edge of the network. However, edge devices have bottlenecks in battery capacity and computing power, making it challenging to perform computing tasks in dynamic and harsh network environments. Especially in disaster scenarios, edge (rescue) devices are more likely to fail due to unreliable wireless communications and scattered rescue requests, which makes it urgent to explore how to provide low-latency, reliable services through edge collaboration. In this article, we investigate the task offloading mechanism in mobile edge computing networks, aiming to ensure fault tolerance and rapid response of computing services in dynamic and harsh scenarios. Specifically, we design a fault-tolerant distributed task offloading scheme, which minimizes task execution time and system energy consumption through the multi-agent proximal policy optimization algorithm. Furthermore, we introduce logarithmic ratio reward functions and action masking to reduce the impact of different task queue lengths while accelerating model convergence. Numerical results show that the proposed algorithm is suitable for service failure scenarios, effectively meeting the reliability requirements of tasks while simultaneously reducing system energy consumption and processing latency.
{"title":"Decentralized and Fault-Tolerant Task Offloading for Enabling Network Edge Intelligence","authors":"Huixiang Zhang;Kaihua Liao;Yu Tai;Wenqiang Ma;Guoyan Cao;Wen Sun;Lexi Xu","doi":"10.1109/JSYST.2024.3403696","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3403696","url":null,"abstract":"Edge intelligence has recently attracted great interest from industry and academia, and it greatly improves the processing speed at the edge by moving data and artificial intelligence to the edge of the network. However, edge devices have bottlenecks in battery capacity and computing power, making it challenging to perform computing tasks in dynamic and harsh network environments. Especially in disaster scenarios, edge (rescue) devices are more likely to fail due to unreliable wireless communications and scattered rescue requests, which makes it urgent to explore how to provide low-latency, reliable services through edge collaboration. In this article, we investigate the task offloading mechanism in mobile edge computing networks, aiming to ensure fault tolerance and rapid response of computing services in dynamic and harsh scenarios. Specifically, we design a fault-tolerant distributed task offloading scheme, which minimizes task execution time and system energy consumption through the multi-agent proximal policy optimization algorithm. Furthermore, we introduce logarithmic ratio reward functions and action masking to reduce the impact of different task queue lengths while accelerating model convergence. Numerical results show that the proposed algorithm is suitable for service failure scenarios, effectively meeting the reliability requirements of tasks while simultaneously reducing system energy consumption and processing latency.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1459-1470"},"PeriodicalIF":4.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435393","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 : 2024-04-30DOI: 10.1109/JSYST.2024.3391766
Haoyue Yang;Hao Zhang;Zhuping Wang;Chao Huang;Huaicheng Yan
In this article, the optimal consensus problem for a class of nonlinear multiagent systems in discrete-time case is investigated under jump faults and false data injection (FDI) attacks. First, a general fault model with coefficients obeying a semi-Markov process is introduced into system dynamics. A joint state and fault observer based on the hidden semi-Markov model is designed to estimate both the agent's state and the fault signals. Sufficient conditions for the existence of observer gains are established by constructing the stochastic Lyapunov function with hidden mode, observed mode, and elapsed time dependencies. Based on the observed states, we reconstruct the local performance metric functions of agents and design a policy-value iteration algorithm to address the multiplayer game problem. Then, an neural network policy-value iteration approximation algorithm is proposed, which obtains an approximate Nash equilibrium solution of the multiplayer games. Further, a secure fault-tolerant optimal consensus controller with fault compensation and attack attenuation terms is designed to achieve optimal tracking control, and the stability of the neighbor tracking error system is rigorously demonstrated. Finally, illustrative example and comparison simulations are provided to verify the validity and applicability of the proposed results.
{"title":"Asynchronous Observer-Based Fault-Tolerant Optimal Control of Multiagent Systems","authors":"Haoyue Yang;Hao Zhang;Zhuping Wang;Chao Huang;Huaicheng Yan","doi":"10.1109/JSYST.2024.3391766","DOIUrl":"10.1109/JSYST.2024.3391766","url":null,"abstract":"In this article, the optimal consensus problem for a class of nonlinear multiagent systems in discrete-time case is investigated under jump faults and false data injection (FDI) attacks. First, a general fault model with coefficients obeying a semi-Markov process is introduced into system dynamics. A joint state and fault observer based on the hidden semi-Markov model is designed to estimate both the agent's state and the fault signals. Sufficient conditions for the existence of observer gains are established by constructing the stochastic Lyapunov function with hidden mode, observed mode, and elapsed time dependencies. Based on the observed states, we reconstruct the local performance metric functions of agents and design a policy-value iteration algorithm to address the multiplayer game problem. Then, an neural network policy-value iteration approximation algorithm is proposed, which obtains an approximate Nash equilibrium solution of the multiplayer games. Further, a secure fault-tolerant optimal consensus controller with fault compensation and attack attenuation terms is designed to achieve optimal tracking control, and the stability of the neighbor tracking error system is rigorously demonstrated. Finally, illustrative example and comparison simulations are provided to verify the validity and applicability of the proposed results.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1402-1413"},"PeriodicalIF":4.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832010","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 : 2024-04-30DOI: 10.1109/JSYST.2024.3390554
Zhiyu Huang;Zhichao Sheng;Ali A. Nasir;Hongwen Yu
A full-duplex unmanned aerial vehicle (UAV)-based communication network is investigated, where the UAV is dispatched to transmit information to multiple downlink users (DLUs) and receive signal from uplink users (ULUs) simultaneously in the existence of malicious jammers. Considering the limited battery power of the UAV and the quality of service required, 3-D trajectory, DLUs scheduling, ULUs scheduling, and uplink/downlink transmit power allocation are jointly optimized to maximize the energy efficiency of the network. However, the formulated optimization problem with high coupling variables and fractional objective function is nonconvex and therefore mathematically intractable. To address the problem, the BCD method is implemented to decompose the optimization problem into four independent subproblems. An iterative algorithm based on Dinkelbach's algorithm and successive convex approximation technique is developed to solve the problem efficiently. Numerical simulation results are presented to evaluate the performance of different schemes and demonstrate the advantages of the proposed algorithm.
{"title":"Energy Efficiency Maximization for UAV-Assisted Full-Duplex Communication in the Presence of Multiple Malicious Jammers","authors":"Zhiyu Huang;Zhichao Sheng;Ali A. Nasir;Hongwen Yu","doi":"10.1109/JSYST.2024.3390554","DOIUrl":"10.1109/JSYST.2024.3390554","url":null,"abstract":"A full-duplex unmanned aerial vehicle (UAV)-based communication network is investigated, where the UAV is dispatched to transmit information to multiple downlink users (DLUs) and receive signal from uplink users (ULUs) simultaneously in the existence of malicious jammers. Considering the limited battery power of the UAV and the quality of service required, 3-D trajectory, DLUs scheduling, ULUs scheduling, and uplink/downlink transmit power allocation are jointly optimized to maximize the energy efficiency of the network. However, the formulated optimization problem with high coupling variables and fractional objective function is nonconvex and therefore mathematically intractable. To address the problem, the BCD method is implemented to decompose the optimization problem into four independent subproblems. An iterative algorithm based on Dinkelbach's algorithm and successive convex approximation technique is developed to solve the problem efficiently. Numerical simulation results are presented to evaluate the performance of different schemes and demonstrate the advantages of the proposed algorithm.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1257-1268"},"PeriodicalIF":4.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831984","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 : 2024-04-29DOI: 10.1109/JSYST.2024.3391811
Tianqing Zhou;Dong Qin;Xuefang Nie;Xuan Li;Nan Jiang;Chunguo Li
In this article, the orthogonal frequency-division multiple access (OFDMA) integrated with frequency spectrum (band) partitioning and equal bandwidth allocation is first introduced to mitigate the complicated, severe, and average network interferences in ultradense mobile edge computing (MEC) networks. Then, under such OFDMA, the system energy consumed by all users [mobile devices (MDs)] and base stations (BSs) is minimized to reduce the huge energy consumed by ultradense small BSs (SBSs) and prolong the standby time of MDs, jointly optimizing the spectrum partitioning factor, local and remote computation capacities, local power, and binary offloading decision. According to the coupling form of optimization parameters in the formulated problem, this problem is first cut into a joint power control and resource (frequency spectrum) partitioning (PCRP) subproblem, a joint user association, and a computation capacity optimization (UACCO) subproblem. Then, we try to design an effective iteration algorithm to attain the solutions to these problems using convex optimization methods. As for this algorithm, we give some detailed convergence, computation complexity, and simulation analyses. The simulation results show that it may achieve a guaranteed offloading performance and lower energy consumption than other existing algorithms.
{"title":"Joint Computation Offloading and Resource Optimization for Minimizing Network-Wide Energy Consumption in Ultradense MEC Networks","authors":"Tianqing Zhou;Dong Qin;Xuefang Nie;Xuan Li;Nan Jiang;Chunguo Li","doi":"10.1109/JSYST.2024.3391811","DOIUrl":"10.1109/JSYST.2024.3391811","url":null,"abstract":"In this article, the orthogonal frequency-division multiple access (OFDMA) integrated with frequency spectrum (band) partitioning and equal bandwidth allocation is first introduced to mitigate the complicated, severe, and average network interferences in ultradense mobile edge computing (MEC) networks. Then, under such OFDMA, the system energy consumed by all users [mobile devices (MDs)] and base stations (BSs) is minimized to reduce the huge energy consumed by ultradense small BSs (SBSs) and prolong the standby time of MDs, jointly optimizing the spectrum partitioning factor, local and remote computation capacities, local power, and binary offloading decision. According to the coupling form of optimization parameters in the formulated problem, this problem is first cut into a joint power control and resource (frequency spectrum) partitioning (PCRP) subproblem, a joint user association, and a computation capacity optimization (UACCO) subproblem. Then, we try to design an effective iteration algorithm to attain the solutions to these problems using convex optimization methods. As for this algorithm, we give some detailed convergence, computation complexity, and simulation analyses. The simulation results show that it may achieve a guaranteed offloading performance and lower energy consumption than other existing algorithms.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 2","pages":"1115-1126"},"PeriodicalIF":4.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831847","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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