eprintid: 10190355
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/19/03/55
datestamp: 2024-04-09 12:00:53
lastmod: 2024-04-09 12:00:53
status_changed: 2024-04-09 12:00:53
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Liang, K
creators_name: Guo, W
creators_name: Li, Z
creators_name: Li, C
creators_name: Ma, C
creators_name: Wong, KK
creators_name: Chae, CB
title: Customizable and Robust Internet of Robots Based on Network Slicing and Digital Twin
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
keywords: Robots
,
Artificial intelligence
,
Computer architecture
,
Network slicing
,
Task analysis
,
Resource management
,
Ultra reliable low latency communication
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: The Internet of Robots (IoR) is proficient in handling complex tasks in challenging environments, yet it encounters challenges related to service and scenario diversity, risk reduction, and ultra-low latency requirements. To address these challenges, we propose an integrated architecture that enhances the IoR’s adaptability, flexibility, robustness, and low latency. This is achieved through the introduction of network slicing, service-based architecture, and digital twin (DT). We have developed an open-source experimental platform to showcase the customizability of the proposed architecture. Slices with different requirements are set up in WiFi and cellular scenarios to demonstrate its versatility. Additionally, we present a DT-assisted deep reinforcement learning (DRL) approach for the IoR to improve DRL performance and mitigate risks associated with undesirable actions. The DT is employed to predict rewards and dynamic state transitions in the physical environment. Furthermore, we introduce a resource allocation method that combines data processing queue preemption and spectrum puncturing. This is designed to accommodate coexisting services, specifically enhanced mobile broadband (eMBB) and bursty ultra-reliable low latency communications (URLLC). Experimental and numerical results validate the effectiveness of our proposed methods, showing improvements in customizability, robustness, latency, and outage probability in IoR.
date: 2024-03-21
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: http://dx.doi.org/10.1109/mnet.2024.3375503
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2265707
doi: 10.1109/MNET.2024.3375503
lyricists_name: Wong, Kai-Kit
lyricists_id: KWONG98
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: IEEE Network
citation:        Liang, K;    Guo, W;    Li, Z;    Li, C;    Ma, C;    Wong, KK;    Chae, CB;      (2024)    Customizable and Robust Internet of Robots Based on Network Slicing and Digital Twin.                   IEEE Network        10.1109/MNET.2024.3375503 <https://doi.org/10.1109/MNET.2024.3375503>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10190355/1/Customizable_and_Robust_Internet_of_Robots_Based_on_Network_Slicing_and_Digital_Twin.pdf