Opportunistic adaptive haptic sampling on forward channel in telehaptic communication
We propose a network-based opportunistic improvisation to adaptive sampling for the forward channel telehaptic data stream on a time-varying network. The algorithm explores real-time tuning of the perceptual deadband parameter to minimize network underutilization, and consequently improves the quality of telehaptic communication. We describe in detail the rationale behind the design choices of the proposed sampling scheme. We perform both real-time telehaptic experiments and simulations to test the proof of concept. The reconstructed haptic signals reveal a substantial improvement in average SNR of 3.57 dB, suggesting that the proposed method outperforms the conventional adaptive sampling technique to a large extent. In addition to satisfying the telehaptic Quality of Service (QoS) requirements, we also demonstrate that our method does not overwhelm the network or penalize the concurrent traffic streams.