In-network generalized trustworthy data collection for event detection in cyber-physical systems

PeerJ Comput Sci. 2021 May 4:7:e504. doi: 10.7717/peerj-cs.504. eCollection 2021.


Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

Keywords: Cyber-physical system; Data collection; Data dependability; Data quality; Data trustworthiness; Event monitoring; Fire detection; Security and privacy.

Grants and funding

This work was supported by the National Key Research and Development Program of China under Grant 2020YFB1005804, the National Natural Science Foundation of China under Grant 61632009 and 61872097, the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.