Article
Automation & Control Systems
Teresa Pamula, Renata Zochowska
Summary: In this article, a new method for predicting OD matrix based on traffic data using deep learning is proposed. The method eliminates the need for complex data acquisition and processing, and achieves high accuracy and resistance to missing data. A case study conducted in a medium-sized city in Poland demonstrates the practical implementation potential in real-time traffic assignment systems. The method does not require questionnaire research or detailed spatial development information.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Xuexin Bao, Dan Jiang, Xuefeng Yang, Hongmei Wang
Summary: This paper presents an approach to accurate traffic prediction under poor weather conditions by improving the deep belief network (DBN) and integrating support vector regression (SVR). Experimental results demonstrate that the improved DBN effectively controls prediction errors and maintains robustness.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Junyu Lai, Zhiyong Chen, Junhong Zhu, Wanyi Ma, Lianqiang Gan, Siyu Xie, Gun Li
Summary: This paper proposes a linear feature enhanced convolutional long short-term memory (ConvLSTM) model based network traffic prediction (NTP) method for accurately predicting the background traffic matrix (TM) in a local area network (LAN). By optimizing and enhancing the model, this method achieves excellent prediction accuracy and plays a crucial role in traffic synchronization in digital twin networks (DTNs).
COGNITIVE COMPUTATION
(2023)
Article
Chemistry, Analytical
Nagaiah Mohanan Balamurugan, Malaiyalathan Adimoolam, Mohammed H. Alsharif, Peerapong Uthansakul
Summary: Network traffic pattern identification and analysis are crucial for meeting different needs. This study introduces a new method based on an enhanced deep reinforcement learning algorithm to improve the accuracy and precision of network traffic analysis and prediction. Experimental results show that the proposed algorithm outperforms traditional convolutional neural network algorithms in terms of false positive and negative rates.
Article
Computer Science, Artificial Intelligence
Gen Chen, Jiawan Zhang
Summary: This study discusses the feasibility and efficiency of adopting Artificial Intelligence (AI) Deep Learning in smart city scenarios. A traffic flow prediction model based on the Deep Belief Network (DBN) algorithm is constructed and compared with other models. The results show that the proposed algorithm has higher prediction accuracy and better performance in traffic congestion evacuation, providing experimental references for the construction of smart cities.
APPLIED SOFT COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
Weiwei Jiang
Summary: Cellular networks are vital for the success of modern communication systems, and with the advancement of artificial intelligence, they are becoming smarter. This survey reviews the relevant studies on cellular traffic prediction, categorizing the prediction problems and models. It also summarizes various applications based on cellular traffic prediction and points out future research directions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Environmental Sciences
P. Vasanthkumar, N. Senthilkumar, Koppula Srinivas Rao, Ahmed Sayed Mohammed Metwally, Islam Mr Fattah, T. Shaafi, V. Sakthi Murugan
Summary: This article proposes a Modified Wild Horse Optimization with Deep Learning approach for Energy Consumption Prediction (MWHODL-ECP) model in residential buildings. The model combines data preprocessing steps with a deep belief network (DBN) and achieves accurate prediction of energy consumption.
Article
Engineering, Civil
Shen Fang, Veronique Prinet, Jianlong Chang, Michael Werman, Chunxia Zhang, Shiming Xiang, Chunhong Pan
Summary: The article introduces a new model for predicting urban traffic flow, which can consider the complex spatio-temporal dependencies in traffic networks and utilizes multi-source data for prediction. In various experiments, the model performs well on different types of traffic networks, particularly showing significant results in handling large-scale traffic networks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Mathematics
Yanbing Li, Wei Zhao, Huilong Fan
Summary: The accuracy of short-term traffic flow prediction is crucial for the construction of smart cities. This paper proposes a dynamic perceptual graph neural network model that effectively analyzes the relationship between the temporal and spatial dimensions of traffic data flow, resulting in more accurate predictions of future traffic speeds.
Article
Physics, Multidisciplinary
Linbo Zhai, Yong Yang, Shudian Song, Shuyue Ma, Xiumin Zhu, Feng Yang
Summary: Traffic is a broad concept that involves transportation, travel, trade, and internet networks. Forecasting traffic data accurately is a challenging issue due to its non-stationary and highly nonlinear nature. The proposed STPWNet model shows improved performance with fewer parameters and faster inference speed compared to traditional neural networks.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ying Gao, Jinlong Li, Zhigang Xu, Zhangqi Liu, Xiangmo Zhao, Jianhua Chen
Summary: This study proposes a new image-based traffic congestion estimation method, which first defines the traffic congestion accurately and integrates a traffic parameter layer into a CNN model. By training and testing with a large dataset of traffic images, the proposed method shows better efficiency and stability in various traffic conditions and weather scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Hao Huang, Zhiqun Hu, Yueting Wang, Zhaoming Lu, Xiangming Wen, Bin Fu
Summary: In this paper, we propose F-STTP-Net, a Spatial-Temporal Traffic Prediction Network based on federated learning. By dividing the road network into sub-areas and using GAT and LSTM, we build local training models for each sub-area and aggregate them through federated learning to form a powerful central model. Experimental results show that F-STTP-Net achieves excellent prediction performance without the need for sub-area raw data and has strong generalization ability.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Marine
Tengze Fan, Deshan Chen, Chen Huang, Chi Tian, Xinping Yan
Summary: Accurate vessel travel time estimation is essential for optimizing port operations and ensuring port safety. Current prediction models overlook the complex nature of vessel travel time, limiting their accuracy and applicability. We propose a novel context-aware deep learning approach that considers specific traffic contexts and yields improved accuracy in inland vessel travel time prediction.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Civil
Mujahid I. Ashqer, Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Marwan Bikdash
Summary: This study introduces a novel approach using probe vehicle data for traffic density estimation, and validates it using datasets from intersections in Greece and Germany. The results show that even with low market penetration rate, relying solely on probe vehicle data can effectively predict traffic density, and having signal phase and timing information is not necessarily important for accuracy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Wenhao Yu, Shangyou Wu, Mengqiu Huang
Summary: Traffic prediction is a challenging task due to the complex urban road network and dynamic traffic data. In this study, we propose a novel framework, named MmgFra, to merge multi-scale information for high-precision urban traffic flow prediction. The experimental results show that our model outperforms state-of-the-art neural network models and GCN-based variant models in terms of predictive accuracy.
EARTH SCIENCE INFORMATICS
(2023)
Article
Computer Science, Information Systems
Yucheng Dong, Qin Ran, Xiangrui Chao, Congcong Li, Shui Yu
Summary: In this article, we propose a continual personalized individual semantics learning model to support consensus-reaching in large-scale linguistic group decision making. The model derives personalized numerical scales from linguistic preference data, performs clustering ensemble method for group division and consensus management, and demonstrates its effectiveness through a case study on intelligent route optimization.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Computer Science, Theory & Methods
Zhiyi Tian, Lei Cui, Jie Liang, Shui Yu
Summary: The prosperity of machine learning has led to an increase in attacks on the training process, with poisoning attacks emerging as a significant threat. Defending against these attacks is challenging, and a systematic review from a unified perspective is lacking. This survey provides a comprehensive overview of poisoning attacks and countermeasures in both centralized and federated learning, categorizing attack methods based on goals and analyzing their differences and connections. Countermeasures in different learning frameworks are presented, along with a discussion of the feasibility of poisoning attacks and potential research directions.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Electrical & Electronic
Yuepeng Li, Deze Zeng, Lin Gu, Andong Zhu, Quan Chen, Shui Yu
Summary: This article addresses the security issues of offloading tasks to edge servers and proposes a Priority-aware Secure Task Offloading (PASTO) algorithm based on TrustZone. Experimental results show that PASTO effectively reduces the total task completion time compared to other approaches.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Telecommunications
Xiao-Wei Tang, Yi Huang, Yunmei Shi, Xin-Lin Huang, Shui Yu
Summary: The article focuses on the UAV placement problem in UAV-assisted VR systems, aiming to minimize transmission delay while meeting the image resolution requirement. An efficient algorithm is proposed to obtain a high-quality solution, and experimental results demonstrate that the proposed scheme can reduce transmission delay compared to the conventional scheme.
IEEE COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Information Systems
Aleteng Tian, Bohao Feng, Huachun Zhou, Yunxue Huang, Keshav Sood, Shui Yu, Hongke Zhang
Summary: In this paper, an efficient cooperative caching (FDDL) framework is proposed to address key issues in mobile edge networks. Machine learning techniques are used to improve content placement and reduce computation complexity and communication costs. The framework includes a cache admission algorithm, a lightweight eviction algorithm for fine-grained replacements, and a federated learning-based parameter sharing mechanism. Experimental results show that the proposed FDDL achieves higher cache hit ratio and traffic offloading rate, and effectively reduces communication costs and training time.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Kaiyue Zhang, Zipei Fan, Xuan Song, Shui Yu
Summary: This article proposes a DLGMP algorithm based on deep leakage from gradients and mobility prior knowledge to solve the problem of trajectory data attacks. The algorithm utilizes spatiotemporal structural information as prior mobility knowledge, greatly reducing the difficulty of recovery, and improves the accuracy and reasonableness of trajectory recovery by adding an easily extensible regularization term and an adversarial loss of Wasserstein GAN.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Tian Liu, Jun Xia, Zhiwei Ling, Xin Fu, Shui Yu, Mingsong Chen
Summary: This article presents a distillation-based federated learning (DFL) method that efficiently and accurately handles federated learning for AIoT applications by using knowledge distillation and local model gradients for aggregation and dispatching.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Chenhan Zhang, Shuyu Zhang, Xiexin Zou, Shui Yu, James J. Q. Yu
Summary: This article proposes a network partitioning approach to improve the performance of graph convolutional network-based predictors on large-scale transportation networks. The approach uses both topological features and traffic speed observations for partitioning, and employs a data-parallel training strategy for parallel training. Case studies on real-world datasets show that the proposed approach can improve the accuracy and training efficiency of the predictors.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Zhenzhen Han, Chuan Xu, Shiwei Ma, Yu Hu, Guofeng Zhao, Shui Yu
Summary: In this study, a reliable routing algorithm based on dynamic topology evolution is proposed, which improves transmission reliability through constructing a topology evolution model and a link interaction model. The DTE-RR algorithm is designed to optimize routing paths. Experimental results demonstrate that DTE-RR outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Xiaoping Wu, Panjun Sun, Haiping Zhou, Zongda Wu, Shui Yu
Summary: This study proposes a signaling game approach for privacy preservation in edge-computing-based IoT networks. It addresses the issue of malicious IoT nodes requesting private data from an IoT cloud storage system across edge nodes. The optimal privacy preservation strategies for edge nodes are derived and a signaling Q-learning algorithm is designed to achieve convergent equilibrium and game parameters. Simulation results show that the proposed algorithm effectively decreases the optimal probability of malicious requests, enhancing privacy preservation in edge-computing-based IoT cloud storage systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Guorui Li, Yajun Wu, Cong Wang, Sancheng Peng, Jianwei Niu, Shui Yu
Summary: In this article, a similarity-based relevance vector machine (SRVM) is proposed to address the challenges of insufficient failure data and low confidence in remaining useful lifetime (RUL) prediction results. The relationship among latent variables is learned adaptively through similarity computations to fully utilize the limited degradation data, and the internal variables in SRVM are treated as time-varying variables and re-estimated dynamically to provide reliable confidence for RUL prediction. Experimental results demonstrate that SRVM achieves higher prediction accuracy compared to other baseline methods.
IEEE INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Weibei Fan, Fu Xiao, Hui Cai, Xiaobai Chen, Shui Yu
Summary: This paper studies fault-tolerant routings in BCube data center network. A fault-tolerant routing algorithm based on node disjoint multi-paths is proposed, which has stronger fault tolerance. An effective fault-tolerant routing algorithm based on routing capabilities for BCube is investigated, which has higher fault tolerance and success rate. An adaptive path finding algorithm is presented to establish virtual links in BCube, which can shorten the diameter. Extensive simulations show that the proposed routing scheme outperforms existing algorithms, achieving significant improvements in throughput, packet arrival rate, and latency.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Engineering, Electrical & Electronic
Youxiang Duan, Yuxi Lu, Shigen Shen, Shui Yu, Peiying Zhang, Wei Zhang, Kostromitin Konstantin Igorevich
Summary: This paper proposes an SFC path optimization strategy based on the clustering of network functional layouts. By designing a topological network identification algorithm and a multi-headed attention mechanism, different types of SFC layouts can be optimized in a targeted manner. The experimental results demonstrate the effectiveness of this strategy in optimizing SFC paths and its ability to optimize service function chain paths in future complex network situations.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Guowen Wu, Hui Wang, Hong Zhang, Yuhan Zhao, Shui Yu, Shigen Shen
Summary: In the scenario of Industry 4.0, mobile smart devices face challenges in processing massive amounts of data. To address this issue, a software-defined network-based mobile edge computing system is proposed to offload computation tasks to edge servers, reducing processing latency and energy consumption. A stochastic game-based computation offloading model is established, demonstrating the achievement of Nash Equilibrium. The proposed stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) outperforms other algorithms in reducing processing delay and energy consumption, even in large-scale MEC systems.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Weiqi Wang, Zhiyi Tian, Chenhan Zhang, An Liu, Shui Yu
Summary: As the right to be forgotten is legislated worldwide, there is a need for machine unlearning mechanisms in federated learning scenarios. This paper proposes a Bayesian federated unlearning approach that allows data erasure from a trained model in federated learning without sharing raw data with the server. The proposed approach considers the trade-off between forgetting erased data and preserving the original global model, and mitigates accuracy degradation caused by unlearning.
PROCEEDINGS OF THE 2023 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, ASIA CCS 2023
(2023)
Article
Computer Science, Hardware & Architecture
Zihang Zhen, Xiaoding Wang, Hui Lin, Sahil Garg, Prabhat Kumar, M. Shamim Hossain
Summary: In this paper, a blockchain architecture based on dynamic state sharding (DSSBD) is proposed to solve the problems caused by cross-shard transactions and reconfiguration. By utilizing deep reinforcement learning, the number of shards, block spacing, and block size can be dynamically adjusted to improve the performance of the blockchain. The experimental results show that the crowdsourcing system with DSSBD has better performance in terms of throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, while ensuring security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Gabriel F. C. de Queiroz, Jose F. de Rezende, Valmir C. Barbosa
Summary: Multi-access Edge Computing (MEC) is a technology that enables faster task processing at the network edge by deploying servers closer to end users. This paper proposes the FlexDO algorithm to solve the DAG application partitioning and offloading problem, and compares it with other solutions to demonstrate its superior performance in various test scenarios.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shahid Latif, Wadii Boulila, Anis Koubaa, Zhuo Zou, Jawad Ahmad
Summary: In the field of Industrial Internet of Things (IIoT), networks are increasingly vulnerable to cyberattacks. This research introduces an optimized Intrusion Detection System based on Deep Transfer Learning (DTL) for heterogeneous IIoT networks, combining Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and ensemble techniques. Through rigorous evaluation, the framework achieves exceptional performance and accurate detection of various cyberattacks.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Rongji Liao, Yuan Zhang, Jinyao Yan, Yang Cai, Narisu Tao
Summary: This paper proposes a joint control approach called STOP to guarantee user-perceived deadline using curriculum-guided deep reinforcement learning. Experimental results show that the STOP scheme achieves a significantly higher average arrival ratio in NS-3.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Miguel Rodriguez-Perez, Sergio Herreria-Alonso, J. Carlos Lopez-Ardao, Raul F. Rodriguez-Rubio
Summary: This paper presents an implementation of an active queue management (AQM) algorithm for the Named-Data Networking (NDN) architecture and its application in congestion control protocols. By utilizing the congestion mark field in NDN packets, information about each transmission queue is encoded to achieve a scalable AQM solution.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Angel Canete, Mercedes Amor, Lidia Fuentes
Summary: This paper proposes an energy-aware placement of service function chains of Virtual Network Functions (VNFs) and a resource-allocation solution for heterogeneous edge infrastructures. The solution has been integrated with an open source management and orchestration project and has been successfully applied to augmented reality services, achieving significant reduction in power consumption and ensuring quality of service compliance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Sachin Kadam, Kaustubh S. Bhargao, Gaurav S. Kasbekar
Summary: This paper discusses the problem of estimating the node cardinality of each node type in a heterogeneous wireless network. Two schemes, HSRC-M1 and HSRC-M2, are proposed to rapidly estimate the number of nodes of each type. The accuracy and efficiency of these schemes are proven through mathematical analysis and simulation experiments.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Jean Nestor M. Dahj, Kingsley A. Ogudo, Leandro Boonzaaier
Summary: The launch of commercial 5G networks has opened up opportunities for heavy data users and highspeed applications, but traditional monitoring and evaluation techniques have limitations in the 5G networks. This paper presents a cost-effective hybrid analytical approach for detecting and evaluating user experience in real-time 5G networks, using statistical methods to calculate the user quality index.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O. Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Summary: The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. This study introduces a resource allocation framework that leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to improve energy efficiency. Through the use of a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG), the proposed approach optimizes user association, cache design, and transmission power control, resulting in enhanced energy efficiency and reduced time delays compared to existing methods.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Wu Chen, Jiayi Zhu, Jiajia Liu, Hongzhi Guo
Summary: With advancements in technology, large-scale drone swarms will be widely used in commercial and military fields. Current application methods are mainly divided into autonomous methods and controlled methods. This paper proposes a new framework for global coordination through local interaction.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Peiying Zhang, Zhihu Luo, Neeraj Kumar, Mohsen Guizani, Hongxia Zhang, Jian Wang
Summary: With the development of Industry 5.0, the demand for network access devices is increasing, especially in areas such as financial transactions, drone control, and telemedicine where low latency is crucial. However, traditional network architectures limit the construction of low-latency networks due to the tight coupling of control and data forwarding functions. To overcome this problem, researchers propose a constraint escalation virtual network embedding algorithm assisted by Graph Convolutional Networks (GCN), which automatically extracts network features and accelerates the learning process to improve network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Review
Computer Science, Hardware & Architecture
P. Anitha, H. S. Vimala, J. Shreyas
Summary: Congestion control is crucial for maintaining network stability, reliability, and performance in IoT. It ensures that critical applications can operate seamlessly and that IoT devices can communicate efficiently without overwhelming the network. Congestion control algorithms ensure that the network operates within its capacity, preventing network overload and maintaining network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shunmugapriya Ramanathan, Abhishek Bhattacharyya, Koteswararao Kondepu, Andrea Fumagalli
Summary: This article presents an experiment that achieves live migration of a containerized 5G Central Unit module using modified open-source migration software. By comparing different migration techniques, it is found that the hybrid migration technique can reduce end-user service recovery time by 36% compared to the traditional cold migration technique.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Fatma Foad Ashrif, Elankovan A. Sundararajan, Rami Ahmad, Mohammad Kamrul Hasan, Elaheh Yadegaridehkordi
Summary: This article introduces the development and current status of authentication protocols in 6LoWPAN, and proposes an innovative perspective to fill the research gap. The article comprehensively surveys and evaluates AKA protocols, analyzing their suitability in wireless sensor networks and the Internet of Things, and proposes future research directions and issues.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Pranjal Kumar Nandi, Md. Rejaul Islam Reaj, Sujan Sarker, Md. Abdur Razzaque, Md. Mamun-or-Rashid, Palash Roy
Summary: This paper proposes a task offloading policy for IoT devices to a mobile edge computing system, aiming to balance device utility and execution cost. A meta heuristic approach is developed to solve the offloading problem, and the results show its potential in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)