A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments

Abstract

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.

Blind Spots of Objective Measures: Exploiting Imperceivable Errors for Immersive Tactile Internet

“…Hence, existing works employ standard reconstruction methods at the receiver. The works in [6], [9], [4] employ zero-order-hold reconstruction, whereas the works in [10], [11], [12] employ linear extrapolation based on the last two received samples. In these works, while extrapolation is done between sample arrivals, the samples are played out with oneshot corrections when received.…”