NeTS: Small: Reliable Task Offloading in Mobile Autonomous Systems Through Semantic MU-MIMO Control

Synopsis

Mobile autonomous systems (MASs) such as self-driving vehicles and drones have a pivotal role in critical applications such as urban mobility, precision agriculture and remote surveillance. To achieve their tasks, MASs increasingly rely on high-throughput low-latency streaming of computer vision tasks (e.g., object detection) to edge servers. However, ephemeral environmental factors such as blockages, congestion and fading may erratically interrupt the flow of tasks to the edge servers. Existing work has addressed computation and communication issues of task offloading by MASs separately, which necessarily leads to suboptimal solutions. Task accuracy, indeed, is inevitably tied to the quality of the multimedia data being sent to the edge, which in turns depends on the adopted wireless strategy. However, the wireless parameters being used depend on the quality of data being sent (the more compression, the higher the latency), which ultimately impacts the desired task accuracy. Thus, to achieve applications that are “resilient-by-design” without compromising task accuracy, the semantics of the multimedia data must be holistically and fundamentally intertwined with real-time optimization of wireless transmissions. The core advance of this project is the design and experimental evaluation of fundamentally novel techniques for hardware based semantic-driven joint optimization of multimedia compression strategies and MU-MIMO transmissions in the context of resource-limited wireless systems. The PIs will leverage the support of this project to involve minority and underrepresented students in research and outreach activities. As part of the project, graduate students will develop unique expertise at the crossroads of machine learning, embedded systems and wireless networks.

Real-time object Detection with Edge Task offloading

The key technical efforts of this project will focus on the design of novel deep reinforcement learning (DRL)-based strategies that will control how the acquired data stream is compressed and wirelessly transmitted to the edge servers through MU-MIMO. The PIs will utilize techniques based on split computing to avoid increasing computational overhead due to the compression and MU-MIMO channel state information (CSI) feedback, while keeping the task accuracy close to the original. A full-fledged drone-based prototype based on customized software-defined radio (SDR) interfaces based on FPGA real-time processing and edge computing will be developed as part of the project. Large-scale data collection campaigns will be performed with a 64-antenna SDR testbed at Northeastern, a drone experimental testbed at UC Irvine, and the AERPAW PAWR platform to (i) collect the necessary wireless/multimedia data to train our algorithms; (ii) perform extensive testing and performance evaluation.

Personnel

Principal Investigator: Francesco Restuccia (Northeastern)
Co-Principal Investigator: Marco Levorato (UC Irvine)
Graduate Research Assistant: Foysal Haque (Northeastern)
Graduate Research Assistant: Sharon Ladrón de Guevara (UC Irvine)

Publications

All the publications below are available under the “Publication” tab.

Y. Wu, C. Chiasserini, F. Malandrino and M. Levorato, “Invited Paper: Enhancing Privacy in Federated Learning via Early Exit”, Proceedings of ACM Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems (ACM ApPLIED), 2023.

F. Malandrino, G. Di Giacomo, A. Karamzade, M. Levorato, and C. Chiasserini, “Matching DNN Compression and Cooperative Training with Resources and Data Availability”, Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

A. Alsoliman, F. Abkenar, and M. Levorato, “State-Recovery Protocol for URLLC Applications in 5G Systems”, IEEE Topical Conference on Wireless Sensors and Sensor Networks (IEEE WiSNet), 2023.

N. Bahadori, Y. Matsubara, M. Levorato and F. Restuccia, “SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing”, Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), 2023.

A. Coletta, F. Giorgi, G. Maselli, M. Prata, D. Silvestri, J. Ashdown and F. Restuccia, “AA-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems,” Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

M. Cominelli and F. Gringoli and F. Restuccia, “Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations,” Proc. of IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom), 2023.

K. Foysal Haque, F. Meneghello, and F. Restuccia, “Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices,” Proc. of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (ACM WINTECH), 2023.

Y. Matsubara, M. Levorato and F. Restuccia, “Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges,” ACM Computing Surveys (CSUR), Vol. 55, Is. 5, Art. No.: 90, pp. 130, December 2022.

F. Meneghello, C. Chen, C. Cordeiro and F. Restuccia, “Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks,” IEEE Communications Magazine (IEEE COMMAG), Vol. 61, Is. 7, pp. 128 – 133, July 2023.

A. Pinto, A. Ashdown, T. Hassan, H. Cheng, F. Esposito, L. Bonati, S. D’Oro, T. Melodia, F. Restuccia, “Hercules: An Emulation-Based Framework for Transport Layer Measurements over 5G Wireless Networks,” Proceedings of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (ACM WINTECH), 2023.

C. Puligheddu, J. Ashdown, C. F. Chiasserini, and F. Restuccia, “SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems,” Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM), 2023.

F. Raviglione, C. Casetti and F. Restuccia, “Edge-V: Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands“, Proceedings of IEEE Vehicular Technology Conference (VTC2023-Spring), 2023.

F. Restuccia, E. Blasch, A. Ashdown, J. Ashdown, and K. Turck, “3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems,” Proceedings of IEEE Military Communications Conference (IEEE MILCOM), pp. 19-24, 2022.

R. Rusca, F. Raviglione, C. Casetti, P. Giaccone, and F. Restuccia, “Mobile RF Scenario Design for Massive-Scale Wireless Channel Emulators,” Proceedings of Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2023.

Y. Matsubara, D. Callegaro, S. Singh, M. Levorato, and F. Restuccia, “BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022.

D. Callegaro, M. Levorato and F. Restuccia, “SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022.

N. Bahadori, J. Ashdown and F. Restuccia, “ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning,” Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM), 2022. Best Paper Award.

F. Meneghello, M. Rossi and F. Restuccia, “DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning,” Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), 2022.

P. Tehrani, F. Restuccia, and M. Levorato, “Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks,” Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (IEEE DySPAN), December 2021.

Patents

F. Restuccia, K. F. Haque and  M. Zhang, “Method and Apparatus for Wi-Fi Sensing Through MU-MIMO Beamforming Feedback Learning,” PCT. Application No.: PCT/US23/31651, filed on August 31, 2023.

F. Meneghello, M. Rossi and F. Restuccia, “System and Method for Identifying a Remote Device,” U.S. No.: 17/934,802, filed on September 23, 2022.

N. Bahadori and F. Restuccia, “System and Method for Sensing an Environment,” U.S. No.: 17/822,313, Filed on August 25, 2022.

F. Raviglione, F. Restuccia and C. Casetti, “Vehicular  Edge Intelligence in Unlicensed Spectrum  Bands”, PCT/US2023/064848, filed on March 23, 2023

N. Bahadori, Y. Matsubara, M. Levorato and F. Restuccia, “Beamforming in Wireless Networks Via Split Computing”, PCT Application No. PCT/US23/65300, filed on April 4, 2023.

C. Puligheddu, F. Restuccia, C.F. Chiasserini, “SEM-O-RAN: Semantic NextG O-RAN Slicing for Data-Driven Edge-Assisted Mobile Applications”, PCT Application No.: PCT/US2023/064901, filed on March 24, 2023. 

F. Restuccia, K. F. Haque and  M. Zhang, “SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals,” U.S. Application No.: 63/380,254,  filed on October 21, 2022.

S. Rifat, J. Ashdown, K. Turck and F. Restuccia, “DAZDA: Domain-Aware Zero-Shot Dynamic Adaptation of Neural Networks in Edge Computing,” U.S. Application No.: 63/580,492,  filed on Sept 5, 2023.

Educational Activities and Outreach

Master students involvement: a team of 6 master students is expanding the evaluation of our split computing algorithms to a wider set of autonomous platforms (e.g., rovers) to enable a comprehensive evaluation of data representation strategies in response to different contexts.

Undergrad and Junior students involvement: several undergraduate students (of which 5from minorities) and senior high-school student has been involved in the development of autonomous navigation algorithms.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134973&HistoricalAwards=false