Abstract
Data trustworthiness (i.e., the data is free from error, up to date, and originate from a reputable source) is always preferred. However, due to environmental influence (i.e., equipment faults, noises, or security attacks) and technology limitation a wireless sensor/sensors module can neither store/process all raw data locally nor reliably forward the data to a destination in heterogeneous IoT environment. As a result, the sensing data collected by IoT/Cyber-Physical System (CPS) is inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities/cloud. Therefore, it is highly recommended and desirable to identify trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for collaborative IoT environment. The key idea of IGTDC is to allow a sensor’s module to check whether or not the raw/sense data is reliable before routing to the sink/edge node. It also identifies whether the received data is trustworthy or not before aggregation at sink/edge node. Besides, IGTDC facilitates to identify a faulty sensor. For a reliable event detection in real-time without waiting for a trust report from the end entities/cloud, we use collaborative IoT, and gate-level modeling with Verilog User Defined Primitive (UDP) to make sure that the collected data/event information is reliable before sending to end entities/cloud. We use BCD 8421 (Binary Coded Decimal) in gate-level modeling as a flag which assists in identifying a faulty or compromise sensor. Through simulations with Verilog Icarus, we demonstrate that the collected data in IGTDC is trustworthy that can make trustworthy data aggregation for event detection and help to identify a faulty sensor.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61632009 and 61872097, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.
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Rahman, H.u., Wang, G., Bhuiyan, M.Z.A., Chen, J. (2019). Towards In-Network Generalized Trustworthy Data Collection for Trustworthy Cyber-Physical Systems. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-15-1304-6_5
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