Resilient Data Collection in Oil and Gas Refinery Sensor Networks - - PowerPoint PPT Presentation

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Apr 01, 2024 465 likes •581 views

Resilient Data Collection in Oil and Gas Refinery Sensor Networks Klara Nahrstedt (Presenter) Joined work with Tianyuan Liu University of Illinois at Urbana-Champaign CREDC Industry Workshop March 27-29, 2017 Funded by the U.S. Department of

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Funded by the U.S. Department of Energy and the U.S. Department of Homeland Security | cred-c.org

Resilient Data Collection in Oil and Gas Refinery Sensor Networks

Klara Nahrstedt (Presenter) Joined work with Tianyuan Liu University of Illinois at Urbana-Champaign CREDC Industry Workshop March 27-29, 2017

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Motivating Refinery Resiliency

Explosion of Italy’s biggest refinery
Our approach
Wireless sensor networks
Resilient data collection
Fast connectivity recovery

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Problem Statement

3D sensor placement
Short-range v.s. long-range

communication

Multi-tree topology
Large scale failures

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Problem Statement

Root Nodes with Long Range Communication Tree Edges Sensor Nodes with Short Range Communication

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Problem Statement- Failure

Failed Nodes Disconnected Nodes

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Problem Statement – Recovery Goal

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Recovery Solution

Step 1: construct data collection trees
Centralized planning
Data collection time optimization
Key management and data integrity check embedded
Step 2: recover connectivity under failures
Distributed self-healing protocol
Heuristic approach to re-construct backup data collection paths

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Results

Simulation with up to 500+ sensors
High success rate of recovery (> 91%)
Low data collection time overhead (< 7%)
Prototype on Raspberry Pi 3
Low CPU utilization (< 2%)
Fast path recovery (< 5s)

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Next Steps - Questions

Interest in partnering with Industry Collaborator who can work with us
n this problem and provide realistic use cases in terms of
Sensor topologies
Actual sensor capabilities
Realistic communication capabilities between sensors
Protocols among sensors and between sensors and control center (e.g., DNP3 and

Modbus, IEC 61850 that are used in smart grid)

Interest in simulated or emulated experiments on real world refinery

sensor topology and with real use cases to develop a planning tool

Interest in integration of different sensors at one place (array of things)

in refineries to enable richer contextual information and provide smarter and faster recovery protocols/algorithms

Consider heterogeneous measurement data (temperature, pressure, etc.)
Consider multi-level cyber-physical system security issues

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http://cred-c.org @credcresearch facebook.com/credcresearch/

Funded by the U.S. Department of Energy and the U.S. Department of Homeland Security