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 Students: Di Mu, Junyang Shi, Xia Cheng

MS Students: Yitian Chen

Alumni: Yunpeng Ge (MS, 2017), Bin Wang (MS, 2017), Lu Lu (MS, 2017), Stephen Molaro (MS, 2018), Fang Li (MS, 2018)


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] 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'19), May 2019, acceptance ratio: 20/79 = 25.3% (Best Paper Award Nominee).

[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'19), April 2019, acceptance ratio: 288/1464 = 19.7%.

[C] Xiaonan Zhang, Pei Huang, Linke Guo, and Mo Sha, Incentivizing Relay Participation for Securing IoT Communication, IEEE International Conference on Computer Communications (INFOCOM'19), 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'19), April 2019.

[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'19), April 2019.

[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'18), 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'18) research tracks, July 2018, acceptance ratio: 78/378 = 20.6%.

[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'18), 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'17), June 2017, acceptance ratio: 29/146 = 19.9%.

[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'17), May 2017, acceptance ratio: 292/1395 = 20.9%.

[C] Xia Cheng and Mo Sha, POSTER: Cracking the TSCH Channel Hopping in IEEE 802.15.4e, ACM Conference on Computer and Communications Security  (CCS'18), October 2018.

Testbed

Wireless Embedded System Testbed at Binghamton University