CRII: NeTS: Self-Adaptation in Industrial Wireless Sensor-Actuator Networks

Team

Primary Investigator: Mo Sha, Assistant Professor, Department of Computer Science, Binghamton University - State University of New York.

PhD Student: Xia Cheng

Alumni: Yunpeng Ge (MS, 2017), Bin Wang (MS, 2017), Jiangyun Liu (MS, 2017), Fang Li (MS, 2018), Yitian Chen (BS, 2018, MS, 2019), Xingjian Chen (MS, 2020), Junyang Shi (PhD, 2021), Di Mu (PhD, 2021)


Project Period

5/1/2017 - 4/30/2020

nsfThis project is sponsored by the National Science Foundation (NSF) through grant CRII-1657275 (NeTS) [NSF award abstract].


Project Abstract

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as steel mills, oil refineries, and chemical plants. Recent years have witnessed an increased interest in adopting wireless sensor-actuator network (WSAN) technology for industrial networks. This project will develop highly self-adaptive WSANs, enabling a broad range of industrial process applications, which affect economics, security, and quality of life. Successful completion of this project will significantly spur the installation of WSANs with the potential of greatly improving industrial efficiency, leading to a significant reduction of the operating costs, which can help create more jobs. The end objective of this project is to incorporate the project outcomes into the next generation of industrial WSAN standards and real-world products. Project findings will be presented at major international conferences and published in their proceedings and high-impact journals and also used for enriching education and outreach.

IEEE 802.15.4 based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal for industrial process applications where energy consumption and costs are important. However, the stringent and diverse quality of service (QoS) requirements and dynamic industrial environments make managing WSANs a daunting task. A key missing piece of the WSAN management puzzle is a self-adaptation component, which allows WSANs to adapt themselves to consistently satisfy the dynamic QoS requirements in uncertain environments. Industry consequently has shown a marked reluctance to embrace WSAN technology. The overarching goal of this project is to accomplish runtime parameter self-adaptation for industrial WSANs in uncertain environment. This project will develop rigorous scientific methods for equipping industrial WSANs with capabilities of optimally configuring themselves based on specific QoS requirements and adapting the configurations at runtime to consistently satisfy the dynamic requirements in uncertain environments. This project aims to advance the state of the art of industrial WSANs through creating a new paradigm of parameter self-adaptation, resulting in improved network performance and better network resource management. This project will also accomplish an increased understanding of the performance tradeoffs that exist in WSANs and enable the development of new solutions to inform users with accurate user-appropriate information on network performance tradeoffs and configuration choices.


Publications

[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: 29%.

[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.

[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.

[C] Xia Cheng, Junyang Shi, Mo Sha, and Linke Guo, Launching Smart Selective Jamming Attacks in WirelessHART Networks, IEEE International Conference on Computer Communications (INFOCOM), May 2021, acceptance ratio: 252/1266 = 19.9%.

[C] Di Mu, Yitian Chen, Junyang Shi, and Mo Sha, Runtime Control of LoRa Spreading Factor for Campus Shuttle Monitoring, IEEE International Conference on Network Protocols (ICNP), October 2020, acceptance ratio: 30/180 = 16.7%.

[J] Di Mu, Mo Sha, Kyoung-Don Kang, and Hyungdae Yi, Radio Selection and Data Partitioning for Energy-Efficient Wireless Data Transfer in Real-Time IoT Applications, Ad Hoc Networks,  Special Issue on Algorithms, Systems and Applications for Distributed Sensing, Vol. 107, pp. 1-11, October 2020. [Source code] and [data].

[J] Junyang Shi and Mo Sha, Parameter Self-Adaptation for Industrial Wireless Sensor-Actuator Networks, ACM Transactions on Internet Technology, Special Issue on Evolution of IoT Networking Architectures, Vol. 20, Issue 3, pp. 28:1-28:25, September 2020. [Source code and data].

[J] Xia Cheng, Junyang Shi, and Mo Sha, Cracking Channel Hopping Sequences and Graph Routes in Industrial TSCH Networks, ACM Transactions on Internet Technology, SpecialIssue on Evolution of IoT Networking Architectures, Vol. 20, Issue 3, pp. 23:1-23:28, September 2020. [Source code and data].

[C] Junyang Shi, Xingjian Chen, and Mo Sha, Enabling Direct Messaging from LoRa to ZigBee in the 2.4 GHz Band for Industrial Wireless Networks, IEEE International Conference on Industrial Internet (ICII), November 2019, acceptance ratio: 23/138 = 16.7%. [Source code and data].

[C] Junyang Shi, Di Mu, and Mo Sha, LoRaBee: Cross-Technology Communication from LoRa to ZigBee via Payload Encoding, IEEE International Conference on Network Protocols (ICNP), October 2019, acceptance ratio: 30/210 = 14.2%. [Source code and data].

[J] Junyang Shi, Mo Sha, and Zhicheng Yang, Distributed Graph Routing and Scheduling for Industrial Wireless Sensor-Actuator Networks, IEEE/ACM Transactions on Networking, Vol. 27, Issue 4, pp. 1669-1682, August 2019. [Source code and data].

[J] Di Mu, Yunpeng Ge, Mo Sha, Steve Paul, Niranjan Ravichandra, and Souma Chowdhury, Robust Optimal Selection of Radio Type and Transmission Power for Internet of Things, ACM Transactions on Sensor Networks, Vol. 15, Issue 4, pp. 39:1-39:25, July 2019.  [Source code] and [data].

[C] Di Mu, Mo Sha, Kyoung-Don Kang, and Hyungdae Yi, Energy-Efficient Radio Selection and Data Partitioning for Real-Time Data Transfer, IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), May 2019, acceptance ratio: 20/79 = 25.3% (Best Paper Award Nominee).  [Source code] and [data].

[C] Junyang Shi and Mo Sha, Parameter Self-Configuration and Self-Adaptation in Industrial Wireless Sensor-Actuator Networks, IEEE International Conference on Computer Communications (INFOCOM), April 2019, acceptance ratio: 288/1464 = 19.7%. [Source code and data].

[C] Xiaonan Zhang, Pei Huang, Linke Guo, and Mo Sha, Incentivizing Relay Participation for Securing IoT Communication, IEEE International Conference on Computer Communications (INFOCOM), April 2019, acceptance ratio: 288/1464 = 19.7%.

[C] Xia Cheng, Junyang Shi, and Mo Sha, Cracking the Channel Hopping Sequences in IEEE 802.15.4e-Based Industrial TSCH Networks, ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI), April 2019, acceptance ratio: 20/71 = 28.1%. [Source code and data].

[C] Zhicheng Yang, Parth H Pathak, Mo Sha, Tingting Zhu, Junai Gan, Pengfei Hu, and Prasant Mohapatra, On The Feasibility of Estimating Soluble Sugar Content using Millimeter-wave, ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI), April 2019, acceptance ratio: 20/71 = 28.1%.

[C] Zhicheng Yang, Parth H Pathak, Jianli Pan, Mo Sha, and Prasant Mohapatra, Sense and Deploy: Blockage-aware Deployment of Reliable 60 GHz mmWave WLANs, IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), October 2018acceptance ratio: 42/145 = 28.9%.

[C] Junyang Shi, Mo Sha, and Zhicheng Yang, DiGS: Distributed Graph Routing and Scheduling for Industrial Wireless Sensor-Actuator Networks, IEEE International Conference on Distributed Computing Systems (ICDCS) research tracks, July 2018, acceptance ratio: 78/378 = 20.6%. [Source code and data].

[C] Chengjie Wu, Dolvara Gunatilaka, Mo Sha, and Chenyang Lu, Real-Time Wireless Routing for Industrial Internet of Things, ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI), April, 2018, acceptance ratio: (21+4)/89 = 28.1%.

[J] Kyoung-Don Kang, Liehuo Chen, Hyungdae Yi, Bin Wang, and Mo Sha, Real-Time Information Derivation from Big Sensor Data via Edge Computing, Big Data and Cognitive Computing, Special Issue on Cognitive Services Integrating with Big Data, Clouds and IoT, Vol. 1, Issue 5, pp. 1-24, October, 2017.

[C] Di Mu, Yunpeng Ge, Mo Sha, Steve Paul, Niranjan Ravichandra, and Souma Chowdhury, Adaptive Radio and Transmission Power Selection for Internet of Things, ACM/IEEE International Symposium on Quality of Service (IWQoS), June 2017, acceptance ratio: 29/146 = 19.9%. [Source code] and [data].

[J] Mo Sha, Dolvara Gunatilaka, Chengjie Wu, and Chenyang Lu, Empirical Study and Enhancements of Industrial Wireless Sensor-Actuator Network Protocols, IEEE Internet of Things Journal, Vol. 4, Issue 3, pp. 696-704, June 2017.

[C] Dolvara Gunatilaka, Mo Sha, and Chenyang Lu, Impacts of Channel Selection on Industrial Wireless Sensor-Actuator Networks, IEEE International Conference on Computer Communications (INFOCOM), May 2017, acceptance ratio: 292/1395 = 20.9%.

Testbed

Industrial Wireless Sensor-Actuator Network Testbed