CAREER: Advancing Network Configuration and Runtime Adaptation Methods for Industrial Wireless Sensor-Actuator Networks

Team

Primary Investigator: Mo Sha, Associate Professor, Knight Foundation School of Computing and Information Sciences, Florida International University.

PhD Student: Aitian Ma

Alumni: Xia Cheng, Junyang Shi, Di Mu, Jean Tonday Rodriguez, Yitian Chen, Xingjian Chen

Project Period

3/12/2021 - 2/28/2026

nsfThis project is sponsored by the National Science Foundation (NSF) through grant CNS-2150010 (replacing CNS-2046538) [NSF award abstract].

Project Abstract

A decade of real-world deployments of industrial wireless standards, such as WirelessHART and ISA100, has demonstrated the feasibility of using IEEE 802.15.4-based wireless sensor-actuator networks (WSANs) to achieve reliable and real-time wireless communication in industrial environments. Although WSANs work satisfactorily most of the time thanks to years of research, they are often difficult to configure as configuring a WSAN is a complex process, which involves theoretical computation, simulation, field testing, among other tasks. To support new services that require high data rates and mobile platforms, industrial WSANs are adopting wireless technologies such as 5G and LoRa and becoming increasingly hierarchical, heterogeneous, and complex, which significantly increases the network configuration difficulty. This CAREER project aims to advance network configuration and runtime adaptation methods for industrial WSANs. Research outcomes from this project will significantly enhance the resilience and agility of industrial WSANs and reduce human involvement in network management, leading to a significant improvement in industrial efficiency and a remarkable reduction of operating costs. By providing more advanced WSANs, the research outcomes from this project will significantly spur the installation of WSANs in process industries and enable a broad range of new wireless-based applications, which affects economics, security, and quality of life. This project enhances lectures and course project materials, supports curriculum developments, creates research opportunities for undergraduate and graduate students, and establishes outreach programs for K-12 students.

Different from traditional methods that rely largely on experience and rules of thumb that involve a coarse-grained analysis of network load or dynamics during a few field trials, this project develops a rigorous methodology that leverages advanced machine learning techniques to configure and adapt WSANs by harvesting the valuable resources (e.g., theoretical models and simulation methods) accumulated by the wireless research community. This project develops new methods that leverage wireless simulations and deep learning to relate high-level network performance to low-level network configurations and efficiently adapt the network at runtime to satisfy the performance requirements specified by industrial applications. This project demonstrates the performance of WSANs that are equipped with those new methods through testbed experimentation, case study, and real-world validation. The research outcomes from this project affects not only industrial WSANs but other complex wireless networks as this project creates a replicable template for novel network configuration and runtime adaptation strategies that advance the state of the art of wireless network management.

Publications

[C] Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, and Dongsheng Luo, Parametric Augmentation for Time Series Contrastive Learning, International Conference on Learning Representations (ICLR), May 2024, acceptance ratio: 31%.

[J] Junyang Shi, Aitian Ma, Xia Cheng, Mo Sha, and Xi Peng, Adapting Wireless Network Configuration from Simulation to Reality via Deep Learning based Domain Adaptation, IEEE/ACM Transactions on Networking, accepted for publication. [source code and data]

[J] Xia Cheng, Junyang Shi, and Mo Sha, and Linke Guo, Revealing Smart Selective Jamming Attacks in WirelessHART Networks, IEEE/ACM Transactions on Networking,  Vol. 31, Issue 4, pp. 1611-1625, August, 2023. [source code and data]

[C] Xia Cheng and Mo Sha, Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks, IEEE/ACM International Symposium on Quality of Service (IWQoS), June 2023, acceptance ratio: 62/264 = 23.5% [source code and data].

[C] Di Mu, Yitian Chen, Xingjian Chen, Junyang Shi, and Mo Sha, Enabling Direct Message Dissemination in Industrial Wireless Networks via Cross-Technology Communication, IEEE International Conference on Computer Communications (INFOCOM), May 2023, acceptance ratio: 252/1312 = 19.2%. [source code and data]

[J] Xia Cheng and Mo Sha, Autonomous Traffic-Aware Scheduling for Industrial Wireless Sensor-Actuator Networks, ACM Transactions on Sensor Networks, Vol. 19, Issue 2, pp. 38:1-38:25, February 2023. [source code and data]

[C] Qi Li, Keyang Yu, Dong Chen, Mo Sha, and Long Cheng, TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference, IEEE Conference on Communications and Network Security (CNS), October 2022, acceptance ratio: 43/122 = 35.2%.

[C] Junyang Shi and Mo Sha, Localizing Campus Shuttles from One Single Base Station Using LoRa Link Characteristics, IEEE International Conference on Computer Communications and Networks (ICCCN), July 2022, acceptance ratio: 39/130=30.0%. [source code and data]

[J] Junyang Shi, Xingjian Chen, and Mo Sha, Enabling Cross-technology Communication from LoRa to ZigBee in the 2.4 GHz Band, ACM Transactions on Sensor Networks, Vol. 18, Issue 2, pp. 21:1-21:23, May 2022. [source code and data]

[J] Junyang Shi, Di Mu, and Mo Sha, Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands, ACM Transactions on Sensor Networks, Vol. 18, Issue 1, pp. 6:1-6:26, February 2022. [source code and data]

[C] Xia Cheng and Mo Sha, ATRIA: Autonomous Traffic-Aware Transmission Scheduling for Industrial Wireless Sensor-Actuator Networks, IEEE International Conference on Network Protocols (ICNP), November 2021, acceptance ratio: 38/154 = 24.6%. [source code and data]

[C] Junyang Shi, Mo Sha, and Xi Peng, Adapting Wireless Mesh Network Configuration from Simulation to Reality via Deep Learning based Domain Adaptation, USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2021, acceptance ratio (fall deadline): 40/255 = 15.6%. [source code and data]