Award Abstract # 2028740
Collaborative Research: PPoSS: Planning:S3-IoT: Design and Deployment of Scalable, Secure, and Smart Mission-Critical IoT Systems

NSF Org: CCF
Division of Computing and Communication Foundations
Recipient: SYRACUSE UNIVERSITY
Initial Amendment Date: August 25, 2020
Latest Amendment Date: October 14, 2020
Award Number: 2028740
Award Instrument: Standard Grant
Program Manager: Wei Ding
CCF
 Division of Computing and Communication Foundations
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2020
End Date: September 30, 2022 (Estimated)
Total Intended Award Amount: $50,000.00
Total Awarded Amount to Date: $50,000.00
Funds Obligated to Date: FY 2020 = $50,000.00
History of Investigator:
  • Fanxin Kong (Principal Investigator)
    fkong@nd.edu
Recipient Sponsored Research Office: Syracuse University
900 S CROUSE AVE
SYRACUSE
NY  US  13244
(315)443-2807
Sponsor Congressional District: 22
Primary Place of Performance: Syracuse University
Syracuse
NY  US  13244-1200
Primary Place of Performance
Congressional District:
22
Unique Entity Identifier (UEI): C4BXLBC11LC6
Parent UEI:
NSF Program(s): IIS Special Projects
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 026Z
Program Element Code(s): 748400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The growing capabilities of sensing, computing and communication devices are leading to an explosion of Internet of Things (IoT) infrastructures. In the meantime, advances in technologies such as autonomous systems and artificial intelligence promise enormous economic and societal benefits. Naturally, it is desirable to deploy these technologies in IoT infrastructures. However, such deployments present daunting changes for increasingly scaled-up IoT infrastructures in mission-critical applications such as medical, energy, transportation, and industrial-automation systems. These challenges stem from several major aspects in terms of scalability. First, the number of edge devices can be enormous, often in the order of billions, which makes centralized management infeasible. Second, there are multiple layers of heterogeneity. An IoT system can consist of heterogeneous computing subsystems; each subsystem can have heterogeneous computing devices; and each single device can be composed of different kinds of computing components. Third, mission-critical applications have stringent requirements in correctness, resilience, timeliness, security and safety. It is difficult for a large-scale IoT system to satisfy these requirements due to increasing opportunities for adversarial activity.

To tackle these challenges, this project aims to develop a cross-layer and full hardware/software stack solution, referred to as the S3-IoT framework, for the design and deployment of scalable, secure, and smart mission-critical IoT systems. The S3-IoT framework will span three different computation layers, including data centers, gateways/aggregators, and edge devices, and cover four research foci, i.e., resource management, security and privacy, computer architecture/systems, and algorithms. In this planning project, an initial version of the S3-IoT framework will be developed. The S3-IoT framework will (i) leverage a layered structure - data centers, gateways/aggregators, and edge devices to accommodate the huge number of edge devices; (ii) develop cross-layer techniques to deal with the heterogeneity among these layers; and (iii) propose hardware and software co-design approaches that embrace the heterogeneity among computing components to improve the performance of all components within an individual layer. The S3-IoT framework will be evaluated by developing simulators with the layered structure as well as a small scale, comprehensive experimental testbed. The success of this planning project will lead to a convincing path to effective deployment of mission-critical IoT systems and infrastructures, particularly in terms of improving resilience to environmental uncertainties, system internal errors and faults, and malicious attacks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Akowuah, Francis and Kong, Fanxin "Physical Invariant Based Attack Detection for Autonomous Vehicles: Survey, Vision, and Challenges" 2021 Fourth International Conference on Connected and Autonomous Driving (MetroCAD) , 2021 https://doi.org/10.1109/MetroCAD51599.2021.00014 Citation Details
Akowuah, Francis and Kong, Fanxin "Real-Time Adaptive Sensor Attack Detection in Autonomous Cyber-Physical Systems" 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS) , 2021 https://doi.org/10.1109/RTAS52030.2021.00027 Citation Details
Akowuah, Francis and Prasad, Romesh and Espinoza, Carlos Omar and Kong, Fanxin "Recovery-by-Learning: Restoring Autonomous Cyber-physical Systems from Sensor Attacks" 2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) , 2021 https://doi.org/10.1109/RTCSA52859.2021.00015 Citation Details
Guo, Xiaolong and Han, Song and Hu, X. Sharon and Jiao, Xun and Jin, Yier and Kong, Fanxin and Lemmon, Michael "Towards scalable, secure, and smart mission-critical IoT systems: review and vision" International Conference on Embedded Software (EMSOFT) , 2021 https://doi.org/10.1145/3477244.3477624 Citation Details
Zhang, Lin and Chen, Xin and Kong, Fanxin and Cardenas, Alvaro A. "Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations" IEEE Real-Time Systems Symposium (RTSS) , 2020 https://doi.org/10.1109/RTSS49844.2020.00028 Citation Details
Zhang, Lin and Lu, Pengyuan and Kong, Fanxin and Chen, Xin and Sokolsky, Oleg and Lee, Insup "Real-time Attack-recovery for Cyber-physical Systems Using Linear-quadratic Regulator" ACM Transactions on Embedded Computing Systems , v.20 , 2021 https://doi.org/10.1145/3477010 Citation Details
Zhang, Lin and Wang, Zifan and Liu, Mengyu and Kong, Fanxin "Adaptive window-based sensor attack detection for cyber-physical systems" Proceedings of the 59th ACM/IEEE Design Automation Conference , 2022 https://doi.org/10.1145/3489517.3530555 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project has generated innovations for designing scalable, secure and smart IoT systems in the following aspects.

(1) Resource management: We have developed several network resource management techniques for Industrial IoT (IIoT) systems.  These techniques successfully tackle several fundamental challenges in such systems, including (i) ensuring real-time Quality of Services (QoS), especially in the presence of unexpected external (i.e., application-side) and internal (i.e., network-side) disturbances, (ii) removing an unrealistic assumption used in many existing works, i.e., all wireless links are reliable, and (iii) supporting scalable deployment of the network resource management techniques.

(2) Security & privacy: We have developed novel attack detection and recovery methods to defend against sensor attacks in distributed devices in the field. These methods provide real-time and resilient defense through improving both the detection delay and accuracy, and safely recovering the devices to desired states before serious consequences occur.

(3) Computer architecture/systems: We have designed energy-efficient brain-inspired hyperdimensional computing architecture using voltage scaling. By leveraging the inherent error tolerance of hyperdimensional computing models, we are able to reduce the energy by 60%. We have also designed in-memory computing methods based on emerging memories to accelerate emerging applications. 

We have developed simulators and extended experimental testbeds, and used them to evaluate and show the effectiveness of the proposed methods.

Through the support of this project, we have organized a workshop on Scalable Design of Resilient Mission-Critical IoT Systems. The workshop has allowed the PIs, other researchers and students to present their research results and initiate new collaborations.

 


Last Modified: 01/18/2023
Modified by: Fanxin Kong

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