A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments
| Autor: | Alan G. Marshall, Yoon Ket Lee, Tiam Hee Tee, Yvonne Chook, Kok Seng Eu, Kian Meng Yap, Tsung-Han Lee, Pei Hsin Lim |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Quality of service 020208 electrical & electronic engineering Network delay General Engineering tele-haptics 020206 networking & telecommunications 02 engineering and technology Network congestion Transmission (telecommunications) Packet loss Communication network Synchronization (computer science) 0202 electrical engineering electronic engineering information engineering haptic data prediction algorithm General Materials Science Trust Strategy Prediction lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 Algorithm Jitter Haptic technology |
| Zdroj: | IEEE Access, Vol 9, Pp 52672-52683 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3070063 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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