A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments
Real-time tele-haptic applications require capturing, compressing, transmitting, and displaying haptic information, which includes tactile and kinesthetic information. To achieve a high quality of service (QoS), real-time haptic data stream synchronization between local and remote environments is re...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9391703/ |
| _version_ | 1818667949118980096 |
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| author | Kian Meng Yap Tiam Hee Tee Alan Marshall Kok Seng Eu Yoon Ket Lee Tsung-Han Lee Pei Hsin Lim Yvonne Chook |
| author_facet | Kian Meng Yap Tiam Hee Tee Alan Marshall Kok Seng Eu Yoon Ket Lee Tsung-Han Lee Pei Hsin Lim Yvonne Chook |
| author_sort | Kian Meng Yap |
| collection | DOAJ |
| description | Real-time tele-haptic applications require capturing, compressing, transmitting, and displaying haptic information, which includes tactile and kinesthetic information. To achieve a high quality of service (QoS), real-time haptic data stream synchronization between local and remote environments is required. However, transmission of data over a computer network is often affected by network impairments, such as network delay, jitter, and packet loss, thus leading to system instability and poor performance. Current prediction algorithms for networked haptics comprise perceptual data reduction, traffic prioritization approaches, congestion control approaches, and radio resource allocation. However, the mentioned prediction algorithms either do not consider packet loss and time-varying delays (i.e., jitter) in their experimental setup, or only consider packet loss or delays. In real-world network environments, both packet loss and delays often occur simultaneously. In this work, a network adaptive Trust Strategy Prediction (TSP) algorithm was modified to work under both network impairments. The objective of the TSP is to maintain real-time haptic synchronization (haptic data stream synchronization) between the haptic interactive environments, by compensating network impairments using selective and specific prediction strategies, according to changes in the network’s characteristics. The experimental results demonstrate that TSP offers greater accuracy and smaller inconsistencies in terms of the predicted position, compared to the dead reckoning prediction and velocity estimation, which is often employed with filtering techniques. |
| first_indexed | 2024-12-17T06:28:33Z |
| format | Article |
| id | doaj.art-8f46141739fe4d559a9a6edde7e2f92d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:28:33Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8f46141739fe4d559a9a6edde7e2f92d2022-12-21T22:00:12ZengIEEEIEEE Access2169-35362021-01-019526725268310.1109/ACCESS.2021.30700639391703A Network-Adaptive Prediction Algorithm for Haptic Data Under Network ImpairmentsKian Meng Yap0https://orcid.org/0000-0001-6795-9847Tiam Hee Tee1Alan Marshall2https://orcid.org/0000-0002-8058-5242Kok Seng Eu3https://orcid.org/0000-0002-8628-1573Yoon Ket Lee4Tsung-Han Lee5Pei Hsin Lim6https://orcid.org/0000-0002-6156-7217Yvonne Chook7Department of Computing and Information Systems, School of Engineering and Technology, Research Centre for Human-Machine Collaboration (HUMAC), Sunway University, Petaling, Jaya, MalaysiaDepartment of Computing and Information Systems, School of Engineering and Technology, Research Centre for Human-Machine Collaboration (HUMAC), Sunway University, Petaling, Jaya, MalaysiaSchool of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool, U.K.Department of Computing and Information Systems, School of Engineering and Technology, Research Centre for Human-Machine Collaboration (HUMAC), Sunway University, Petaling, Jaya, MalaysiaFaculty of Engineering and Built Environment, Tunku Abdul Rahman University College, Kuala Lumpur, MalaysiaDepartment of Computer and Information Science, National Taichung University of Education, Taichung, TaiwanDepartment of Computing and Information Systems, School of Engineering and Technology, Research Centre for Human-Machine Collaboration (HUMAC), Sunway University, Petaling, Jaya, MalaysiaDepartment of Computing and Information Systems, School of Engineering and Technology, Research Centre for Human-Machine Collaboration (HUMAC), Sunway University, Petaling, Jaya, MalaysiaReal-time tele-haptic applications require capturing, compressing, transmitting, and displaying haptic information, which includes tactile and kinesthetic information. To achieve a high quality of service (QoS), real-time haptic data stream synchronization between local and remote environments is required. However, transmission of data over a computer network is often affected by network impairments, such as network delay, jitter, and packet loss, thus leading to system instability and poor performance. Current prediction algorithms for networked haptics comprise perceptual data reduction, traffic prioritization approaches, congestion control approaches, and radio resource allocation. However, the mentioned prediction algorithms either do not consider packet loss and time-varying delays (i.e., jitter) in their experimental setup, or only consider packet loss or delays. In real-world network environments, both packet loss and delays often occur simultaneously. In this work, a network adaptive Trust Strategy Prediction (TSP) algorithm was modified to work under both network impairments. The objective of the TSP is to maintain real-time haptic synchronization (haptic data stream synchronization) between the haptic interactive environments, by compensating network impairments using selective and specific prediction strategies, according to changes in the network’s characteristics. The experimental results demonstrate that TSP offers greater accuracy and smaller inconsistencies in terms of the predicted position, compared to the dead reckoning prediction and velocity estimation, which is often employed with filtering techniques.https://ieeexplore.ieee.org/document/9391703/Communication networkhaptic data prediction algorithmtele-hapticsTrust Strategy Prediction |
| spellingShingle | Kian Meng Yap Tiam Hee Tee Alan Marshall Kok Seng Eu Yoon Ket Lee Tsung-Han Lee Pei Hsin Lim Yvonne Chook A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments IEEE Access Communication network haptic data prediction algorithm tele-haptics Trust Strategy Prediction |
| title | A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments |
| title_full | A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments |
| title_fullStr | A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments |
| title_full_unstemmed | A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments |
| title_short | A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments |
| title_sort | network adaptive prediction algorithm for haptic data under network impairments |
| topic | Communication network haptic data prediction algorithm tele-haptics Trust Strategy Prediction |
| url | https://ieeexplore.ieee.org/document/9391703/ |
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