Abstract
The next generation of 5G wireless communications will provide very high data-rates combined with low latency. Several applications in different research fields are planning to exploit 5G wireless communications to achieve benefits for own aims. Mainly, Teleoperations Systems (TS) expect to use this technology in order to obtain very advantages for own architecture. The goal of these systems, as implied by their name, is to provide to the user (human operator) the feeling of presence in the remote environment where the teleoperator (robot) exists. This aim can be achieved thanks to the exchange of a thousand or more haptic data packets per second to be transmitted between the master and the slave devices. Since Teleoperation Systems are very sensitive to delays and data loss, TS challenge is to obtain low latency and high reliability in order to improve Quality of Experience (QoE) for the users in real-time communications. For this reason a data compression and reduction are required to ensure good system stability. A Predictive-Perceptive compression model based on prediction error and human psychophysical limits has been adopted to reduce data size. However, the big amount of Haptic Data to be processed requires very large times to execute their compression. Due to this issue a parallel strategy and implementation have been proposed with related experimental results to confirm the gain of performance in time terms.
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De Luca, P., Formisano, A. (2020). Haptic Data Accelerated Prediction via Multicore Implementation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_8
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