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Artykuły w czasopismach na temat „Prediction routing”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Memon, Ambreen, Sardar M. N. Islam, Muhammad Nadeem Ali, and Byung-Seo Kim. "Enhancing Energy Efficiency of Sensors and Communication Devices in Opportunistic Networks Through Human Mobility Interaction Prediction." Sensors 25, no. 5 (2025): 1414. https://doi.org/10.3390/s25051414.

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The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for

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Nunez Segura, Gustavo A., and Cintia Borges Margi. "Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking." Energies 14, no. 17 (2021): 5379. http://dx.doi.org/10.3390/en14175379.

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Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing pr

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3

Pavithra, H., and G. N Srinivasan. "An Approach for Traffic Prediction in SDN for Performing Routing." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 428–29. http://dx.doi.org/10.21275/sr22604123214.

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4

Wu Xiaohua, and Li Jianping. "Routing Algorithm based on Mobility Prediction." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 2 (2012): 218–26. http://dx.doi.org/10.4156/aiss.vol4.issue2.27.

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5

BOVET, DANIEL P., MIRIAM DI IANNI, and PIERLUIGI CRESCENZI. "DEADLOCK PREDICTION IN THE CASE OF DYNAMIC ROUTING." International Journal of Foundations of Computer Science 01, no. 03 (1990): 185–99. http://dx.doi.org/10.1142/s012905419000014x.

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One of the main issues in flow control problems is deadlock of messages caused by a limited amount of resources. In this paper, the problem of predicting whether a deadlock will necessarily occur in a Store-and-Forward Network is analyzed. We show that, in the case of dynamic routing, the deadlock prediction problem can be decided in polynomial time if tokens are allowed to transit more than once through the same vertex, in contrast with an NP-completeness result in the case where they are allowed to transit at most once.

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Rashid, Sami AbdulJabbar, Mustafa Maad Hamdi, Aymen Jalil AbdulElah, Yasir Jasim Ahmed Rajab, and Khalid AbdulHakeem Zaaile. "Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 215–21. http://dx.doi.org/10.11591/eei.v13i1.5222.

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Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)-VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CR-VANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering m

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7

Kim, Kwangsoo, Minseok Kwon, Jaegeun Park, and Yongsoon Eun. "Dynamic Vehicular Route Guidance Using Traffic Prediction Information." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3727865.

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We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonl

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8

DI IANNI, MIRIAM. "WORMHOLE DEADLOCK PREDICTION." Parallel Processing Letters 10, no. 04 (2000): 295–303. http://dx.doi.org/10.1142/s0129626400000287.

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Deadlock prevention is usually realized by forbidding transmissions that could eventually cause a deadlock to occur. However, an optimal routing algorithm with respect to channel utilization forbids transmissions only when they would bring the network into a configuration that will necessarily evolve into a deadlock. Hence, optimal deadlock prevention is closely related to deadlock prediction. In this paper it is shown that wormhole deadlock and livelock prediction is a hard problem for both oblivious and adaptive routing.

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9

Xu, Xiaoling, and Wei Cui. "Research on AdHoc network routing algorithm for link stability prediction." Journal of Physics: Conference Series 2807, no. 1 (2024): 012023. http://dx.doi.org/10.1088/1742-6596/2807/1/012023.

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Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link s

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10

Anita, X., M. A. Bhagyaveni, and J. Martin Leo Manickam. "Fuzzy-Based Trust Prediction Model for Routing in WSNs." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/480202.

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The cooperative nature of multihop wireless sensor networks (WSNs) makes it vulnerable to varied types of attacks. The sensitive application environments and resource constraints of WSNs mandate the requirement of lightweight security scheme. The earlier security solutions were based on historical behavior of neighbor but the security can be enhanced by predicting the future behavior of the nodes in the network. In this paper, we proposed a fuzzy-based trust prediction model for routing (FTPR) in WSNs with minimal overhead in regard to memory and energy consumption. FTPR incorporates a trust p

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11

Saeedi, Mehdi, Morteza Saheb Zamani, and Ali Jahanian. "Evaluation, prediction and reduction of routing congestion." Microelectronics Journal 38, no. 8-9 (2007): 942–58. http://dx.doi.org/10.1016/j.mejo.2007.07.122.

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12

Deng, Xiaolong, Jufeng Sun, and Junwen Lu. "Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing." Sensors 23, no. 10 (2023): 4936. http://dx.doi.org/10.3390/s23104936.

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Link prediction is critical to completing the missing links in a network or to predicting the generation of new links according to current network structure information, which is vital for analyzing the evolution of a network, such as the logical architecture construction of MEC (mobile edge computing) routing links of a 5G/6G access network. Link prediction can provide throughput guidance for MEC and select appropriate c nodes through the MEC routing links of 5G/6G access networks. Traditional link prediction algorithms are always based on node similarity, which needs predefined similarity fu

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13

Zou, Peijun, Ming Zhao, Jia Wu, and Leilei Wang. "Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks." Information 10, no. 2 (2019): 49. http://dx.doi.org/10.3390/info10020049.

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Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility charact

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14

Cadger, Fraser, Kevin Curran, Jose Santos, and Sandra Moffet. "Opportunistic Neighbour Prediction Using an Artificial Neural Network." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 2 (2015): 38–50. http://dx.doi.org/10.4018/ijapuc.2015040104.

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Device mobility is an issue that affects both MANETs and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. Location prediction in combination with geographic routing has been explored in previous literature. Most of these l

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15

PENG, Min, Pei-Lin HONG, Kai-Ping XUE, and Han-Cheng LU. "Delivery Probability Prediction Based Efficient Routing in DTN." Chinese Journal of Computers 34, no. 1 (2011): 174–81. http://dx.doi.org/10.3724/sp.j.1016.2011.00174.

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16

Szufel, Przemysław, Bartosz Pankratz, Anna Szczurek, Bogumił Kamiński, and Paweł Prałat. "Vehicle Routing Simulation for Prediction of Commuter’s Behaviour." Journal of Advanced Transportation 2022 (April 25, 2022): 1–17. http://dx.doi.org/10.1155/2022/1604303.

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We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in

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17

Szufel, Przemysław, Bartosz Pankratz, Anna Szczurek, Bogumił Kamiński, and Paweł Prałat. "Vehicle Routing Simulation for Prediction of Commuter’s Behaviour." Journal of Advanced Transportation 2022 (April 25, 2022): 1–17. http://dx.doi.org/10.1155/2022/1604303.

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We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in

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18

Wu, Qihui, Min Zhang, Chao Dong, et al. "Routing protocol for heterogeneous FANETs with mobility prediction." China Communications 19, no. 1 (2022): 186–201. http://dx.doi.org/10.23919/jcc.2022.01.014.

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19

SEO, M., W. CHOI, Y. S. KIM, and J. PARK. "Lifetime Prediction Routing Protocol for Wireless Sensor Networks." IEICE Transactions on Communications E90-B, no. 12 (2007): 3680–81. http://dx.doi.org/10.1093/ietcom/e90-b.12.3680.

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20

Namboodiri, V., and Lixin Gao. "Prediction-Based Routing for Vehicular Ad Hoc Networks." IEEE Transactions on Vehicular Technology 56, no. 4 (2007): 2332–45. http://dx.doi.org/10.1109/tvt.2007.897656.

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21

Tang, Jian, Guoliang Xue, and Weiyi Zhang. "Reliable Ad Hoc Routing Based on Mobility Prediction." Journal of Combinatorial Optimization 11, no. 1 (2006): 71–85. http://dx.doi.org/10.1007/s10878-006-5978-7.

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22

Su, William, Sung-Ju Lee, and Mario Gerla. "Mobility prediction and routing inad hoc wireless networks." International Journal of Network Management 11, no. 1 (2001): 3–30. http://dx.doi.org/10.1002/nem.386.

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23

Marín-Tordera, Eva, Xavier Masip-Bruin, Sergio Sánchez-Lopez, Josep Solé-Pareta, and Jordi Domingo-Pascual. "The prediction-based routing in optical transport networks." Computer Communications 29, no. 7 (2006): 865–78. http://dx.doi.org/10.1016/j.comcom.2005.08.008.

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24

Huijbregts, E. P., and J. A. G. Jess. "General gate array routing using a k-terminal net routing algorithm with failure prediction." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 1, no. 4 (1993): 473–81. http://dx.doi.org/10.1109/92.250195.

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25

Jyothi M Shirur, Dr Yasha, and Nagavalli S. Nagavalli S. "Implementation of Machine Learning In VLSI Global Routing." International Journal of Engineering and Science Invention 13, no. 10 (2024): 09–20. http://dx.doi.org/10.35629/6734-13100920.

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Machine learning (ML) plays a transformative role in VLSI design by automating complex decision-making processes, which traditionally rely on heuristic or rule-based methods. In particular, ML helps address challenges in optimizing parameters like wire length, signal delay, and congestion, which are critical for enhancing design efficiency. Without ML, global routing in VLSI becomes time-consuming and less adaptive to modern design complexity, leading to suboptimal results. In this paper, we have discussed the implementation of ML for congestion prediction using congestion datasets from Circui

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26

Singh, Rachee, David Tench, Phillipa Gill, and Andrew McGregor. "PredictRoute: A Network Path Prediction Toolkit." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (2022): 21–22. http://dx.doi.org/10.1145/3543516.3460107.

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Accurate prediction of network paths between arbitrary hosts on the Internet is of vital importance for network operators, cloud providers, and academic researchers. We present PredictRoute, a system that predicts network paths between hosts on the Internet using historical knowledge of the data and control plane. In addition to feeding on freely available traceroutes and BGP routing tables, PredictRoute optimally explores network paths towards chosen BGP prefixes. PredictRoute's strategy for exploring network paths discovers 4X more autonomous system (AS) hops than other well-known strategies

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Singh, Rachee, David Tench, Phillipa Gill, and Andrew McGregor. "PredictRoute: A Network Path Prediction Toolkit." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3460090.

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Accurate prediction of network paths between arbitrary hosts on the Internet is of vital importance for network operators, cloud providers, and academic researchers. We present PredictRoute, a system that predicts network paths between hosts on the Internet using historical knowledge of the data and control plane. In addition to feeding on freely available traceroutes and BGP routing tables, PredictRoute optimally explores network paths towards chosen BGP prefixes. PredictRoute's strategy for exploring network paths discovers 4X more autonomous system (AS) hops than other well-known strategies

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28

Wang, Yu, Shu Xu, Zenghui Ding, Cong Liu, and Xianjun Yang. "Link Predictions with Bi-Level Routing Attention." AI 6, no. 7 (2025): 156. https://doi.org/10.3390/ai6070156.

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Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this

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29

Chen, Bangyuan, and Lingna Chen. "NPLP: An Improved Routing-Forwarding Strategy Utilizing Node Profile and Location Prediction for Opportunistic Networks." Information 10, no. 10 (2019): 306. http://dx.doi.org/10.3390/info10100306.

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Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and

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30

Sun, Ke, Mingyong Liu, Chuan Yin, and Qian Wang. "Adaptive Extended Kalman Prediction-Based SDN-FANET Segmented Hybrid Routing Scheme." Sensors 25, no. 5 (2025): 1417. https://doi.org/10.3390/s25051417.

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Recently, with the advantages of easy deployment, flexibility, diverse functions, and low cost, flying ad hoc network (FANET) has captured great attention for its huge potential in military and civilian applications, whereas the high-speed movement and limited node energy of unmanned aerial vehicles (UAVs) leads to high dynamic topology and high packet loss rate in FANET. Thus, we introduce the software-defined networking (SDN) architecture into FANET and investigate routing scheme in an SDN-FANET to harvest the advantages of SDN centralized control. Firstly, a FANET segmented routing scheme b

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31

Goswami, Pingakshya, and Dinesh Bhatia. "Congestion Prediction in FPGA Using Regression Based Learning Methods." Electronics 10, no. 16 (2021): 1995. http://dx.doi.org/10.3390/electronics10161995.

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Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, train

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32

N. Udo, Edward, Etebong B. Isong, and Emmanuel E. Nyoho. "Intelligent Software-Aided Contact Tracing Framework: Towards Real-Time Model-Driven Prediction of Covid-19 Cases in Nigeria." International Journal of Software Engineering and Computer Systems 7, no. 1 (2021): 67–76. http://dx.doi.org/10.15282/ijsecs.7.1.2021.7.0083.

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As many countries around the world are trying to live with the deadly coronavirus by adhering to the safety measures put in place by their government as regulated by World Health Organization (WHO), it becomes very vital to continuously trace patients with COVID-19 symptoms for isolation, quarantine and treatment. In this work, an intelligent software-aided contact tracing for real-time model-driven prediction of COVID-19 cases is proposed utilizing COVID-19 dataset from kaggle.com. The dataset is preprocessed using One-Hot encoding and Principal Component Analysis. Isolation Forest algorithm

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33

Zhu, Lan, Tao Ding, and Zhuofan Liu. "Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand." Sustainability 16, no. 7 (2024): 2996. http://dx.doi.org/10.3390/su16072996.

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With the development of the healthcare industry, the demand for medical services and protective equipment is boosted, causing the generation rate of infectious medical waste to increase rapidly. Therefore, it is of utmost importance for decision makers to effectively predict the potential risks and propose corresponding solutions. This paper investigates the reverse logistics network optimization for medical waste under the conditions of an uncertain proposal demand. Firstly, a prediction model of medical waste based on the SEIR epidemiological dynamics is constructed, in which both routine an

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34

Hidayat, Muhammad Arief, Aghus Sofwan, and Agung Budi Prasetijo. "Gaussian Prediction Method to Enhance Energy Efficient in Energy Aware AODV." E3S Web of Conferences 125 (2019): 23005. http://dx.doi.org/10.1051/e3sconf/201912523005.

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One of the concerns in the development routing protocol in Mobile Ad Hoc Network is energy consumption which is very influential on package delivery. Energy Delay Aware Ad hoc Demand on Distance Vector (EA-AODV) is one of the routing protocols for the development of Ad hoc Demand on Vector (AODV) that considers energy when receiving packets stored in the routing table. The routing protocol applies the Dijkstra algorithm to determine the shortest route by considering the sequence number and hop count. In the development carried out on EA-AODV is a modification of the Dijkstra algorithm by addin

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35

Satyananda, D., and A. Abdullah. "Deep Learning to Handle Congestion in Vehicle Routing Problem: A Review." Journal of Physics: Conference Series 2129, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/2129/1/012023.

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Abstract This paper reviews the implementation design of Deep Learning in Vehicle Routing Problem. Congestion and traffic condition are usually avoided in Vehicle Routing Problem due to its modeling complexity, and even the benchmark datasets only cover essential conditions. In the real situation, the traffic condition is varied, and congestion is the worst part. To model the real life, the delivery route must consider these situations. The vehicle needs information on traffic prediction in future time to avoid congestion. The prediction needs historical traffic data, which is very large. Deep

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Wang, Shixian, Lina Zhang, Runtao Yang, and Yujiao Zhao. "A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites." Biomolecules 12, no. 12 (2022): 1854. http://dx.doi.org/10.3390/biom12121854.

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By denaturing proteins and promoting the formation of multiprotein complexes, protein phosphorylation has important effects on the activity of protein functional molecules and cell signaling. The regulation of protein phosphorylation allows microbes to respond rapidly and reversibly to specific environmental stimuli or niches, which is closely related to the molecular mechanisms of bacterial drug resistance. Accurate prediction of phosphorylation sites (p-site) of prokaryotes can contribute to addressing bacterial resistance and providing new perspectives for developing novel antibacterial dru

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Rajendiran, R. Manoharan, and NAGADIVYA S. "Grey Prediction-based Energy-Aware Opportunistic Routing in WSN." International Journal of Communication Networks and Distributed Systems 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijcnds.2023.10045929.

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SASA, Kenji, Kaichi TAKEUCHI, Masahiko TAMURA, and Masao MITSUI. "OPTIMAL ROUTING WITH TWO-STEP PREDICTION OF MOORING CRITERIA." Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering) 73, no. 2 (2017): I_1459—I_1464. http://dx.doi.org/10.2208/kaigan.73.i_1459.

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Kim, Moonseong, Matt W. Mutka, Dae-Jun Hwang, and Hyunseung Choo. "On minimum cost multicast routing based on cost prediction." Journal of Communications and Networks 11, no. 5 (2009): 500–508. http://dx.doi.org/10.1109/jcn.2009.6388394.

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Santhi, G., and Nachiappan Alamelu. "Adaptive QoS Multicast Routing with Mobility Prediction in MANETs." International Journal of Ad hoc, Sensor & Ubiquitous Computing 1, no. 3 (2010): 59–70. http://dx.doi.org/10.5121/ijasuc.2010.1305.

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Yuan, Quan, Ionut Cardei, and Jie Wu. "An Efficient Prediction-Based Routing in Disruption-Tolerant Networks." IEEE Transactions on Parallel and Distributed Systems 23, no. 1 (2012): 19–31. http://dx.doi.org/10.1109/tpds.2011.140.

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LI, Zhi-yuan, Ru-chuan WANG, Zhi-jie HAN, Jun-lei BI, and Chong HAN. "Traffic prediction-based routing algorithm over structured P2P networks." Journal of China Universities of Posts and Telecommunications 18 (September 2011): 23–27. http://dx.doi.org/10.1016/s1005-8885(10)60215-4.

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Tall, Hamadoun, Gérard Chalhoub, Nadir Hakem, and Michel Misson. "Load balancing routing with queue overflow prediction for WSNs." Wireless Networks 25, no. 1 (2017): 229–39. http://dx.doi.org/10.1007/s11276-017-1554-6.

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Gao, X. M., and J. H. Xia. "Mobility Prediction-Based Link Stability Routing Protocol for MANET." Applied Mechanics and Materials 556-562 (May 2014): 5926–30. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5926.

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In mobile ad hoc networks, node mobility causes frequent link failures, thus invalidating the routes containing those links. Once a link is detected broken, an alternate route has to be discovered. That will impose significant affect on the route stability and the overall performance of the network. We suggest discovering contained stable links routes based on fuzzy control. When mobile node S wishes to send a packet to a destination mobile node D, node S transmits a ROUTE REQUEST (RREQ) message which contains its velocity and direction as a single local broadcast packet. When one mobile node

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Sharmiladevi, S., R. Sakthipriya, and S. Dhiviya. "A NEW MOBILITY PREDICTION METHOD FOR ROUTING IN VANET." International Journal of Advanced Research 4, no. 6 (2016): 1971–76. http://dx.doi.org/10.21474/ijar01/795.

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Nagadivya, S., and R. Manoharan. "Grey prediction-based energy-aware opportunistic routing in WSN." International Journal of Communication Networks and Distributed Systems 29, no. 1 (2023): 95. http://dx.doi.org/10.1504/ijcnds.2023.127479.

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Zhang, Lichen, Xiaoming Wang, Junling Lu, Meirui Ren, Zhuojun Duan, and Zhipeng Cai. "A novel contact prediction-based routing scheme for DTNs." Transactions on Emerging Telecommunications Technologies 28, no. 1 (2014): e2889. http://dx.doi.org/10.1002/ett.2889.

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Puype, Bart, Eva Marín-Tordera, Didier Colle, et al. "Prediction-based routing as RWA in multilayer traffic engineering." Photonic Network Communications 23, no. 2 (2011): 172–82. http://dx.doi.org/10.1007/s11107-011-0348-5.

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Li, Jinke, Jiahui Hu, Yue Wu, and Xiaoyan Yang. "Pre-Routing Slack Prediction Based on Graph Attention Network." Automation 6, no. 2 (2025): 20. https://doi.org/10.3390/automation6020020.

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Static Timing Analysis (STA) plays a crucial role in realizing timing convergence of integrated circuits. In recent years, there has been growing research on pre-routing timing prediction using Graph Neural Networks (GNNs). However, existing approaches struggle with scalability on large graphs and lack generalizability to new designs, limiting their applicability to large-scale, complex circuit problems. To address this issue, this paper proposes a timing engine based on Graph Attention Network (GAT) to predict the slack of timing endpoints. Firstly, our model computes net embeddings for each

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Vu, Trung Kien, and Sungoh Kwon. "Mobility-Assisted on-Demand Routing Algorithm for MANETs in the Presence of Location Errors." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/790103.

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We propose a mobility-assisted on-demand routing algorithm for mobile ad hoc networks in the presence of location errors. Location awareness enables mobile nodes to predict their mobility and enhances routing performance by estimating link duration and selecting reliable routes. However, measured locations intrinsically include errors in measurement. Such errors degrade mobility prediction and have been ignored in previous work. To mitigate the impact of location errors on routing, we propose an on-demand routing algorithm taking into account location errors. To that end, we adopt the Kalman f

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