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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References
Cam-Winget, N., Sadeghi, A.-R., Jin, Y.: Can IoT be secured: emerging challenges in connecting the unconnected. In: Proceedings of 53rd ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6 (2016)
Cisco, Internet of Things (IoT) - Cisco IoT Product Portfolio - Cisco. http://www.cisco.com/c/en/us/solutions/internet-ofthings/iot-products.html. Accessed 23 May 2019
ur Rahman, H., Azzedin, F., Shawahna, A., Sajjad, F., Abdulrahman, A.S.: Performance evaluation of VDI environment. In: 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pp. 104–109. IEEE, August 2016
Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., Marrs, A.: Disruptive technologies: advances that will transform life, business, and the al economy, vol. 12. McKinsey Global Institute San Francisco, CA (2013)
Andrew, M.: How the Internet of Things will affect security and privacy (2016). http://www.businessinsider.com/internet-of-things-security-privacy-2016-8?IR=T
Makhdoom, I., et al.: Anatomy of threats to the internet of things. IEEE Commun. Surv. Tutorials 21(2), 1636–1675 (2018)
ur Rahman, H., Wang, G., Chen, J., Jiang, H.: Performance evaluation of hypervisors and the effect of virtual CPU on performance. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 772–779. IEEE, October 2018
Karkouch, A., Mousannif, H., Al Moatassime, H., Noel, T.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016)
Bhuiyan, M.Z.A., Wu, J.: Trustworthy and protected data collection for event detection using networked sensing systems. In: 2016 IEEE 37th Sarnoff Symposium. IEEE (2016)
Bhuiyan, M.Z.A., Wang, G., Choo, K.K.R.: Secured data collection for a cloud-enabled structural health monitoring system. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE (2016)
Javadpour, A., Abharian, S.K., Wang, G.: Feature selection and intrusion detection in cloud environment based on machine learning algorithms. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 1417–1421. IEEE, December 2017
Arif, M., Wang, G., Wang, T., Peng, T.: SDN-based secure VANETs communication with fog computing. In: Wang, G., Chen, J., Yang, L. (eds.) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. LNCS, vol. 11342, pp. 46–59. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05345-1_4
Elahi, H., Wang, G., Li, X.: Smartphone bloatware: an overlooked privacy problem. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10656, pp. 169–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72389-1_15
Li, J., Deng, H., Jiang, W.: Secure vibration control of flexible arms based on operators’ behaviors. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10656, pp. 420–431. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72389-1_34
Haron, N., Jaafar, J., Aziz, I.A., Hassan, M.H., Shapiai, M.I.: Data trustworthiness in internet of things: a taxonomy and future directions. In: 2017 IEEE Conference on Big Data and Analytics (ICBDA), pp. 25–30. Kuching (2017)
Sharma, A.B., Golubchik, L., Govindan, R.: Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans. Sens. Netw. (TOSN) 6(3), 23 (2010)
Tolle, G., et al.: A macroscope in the redwoods. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 51–63. ACM, November 2005
Kamal, A.R.M., Bleakley, C., Dobson, S.: Packet-level attestation (PLA): a framework for in-network sensor data reliability. ACM Trans. Sens. Netw. (TOSN) 9(2), 19 (2013)
Samuel, M.: Intel lab data, June 2004. http://db.csail.mit.edu/labdata/labdata.html
Li, W., Song, H., Zeng, F.: Policy-based secure and trustworthy sensing for internet of things in smart cities. IEEE Internet Things J. 5(2), 716–723 (2018)
Tang, L.-A., et al.: Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79(3), 383–401 (2013)
Tang, L.-A., et al.: Tru-alarm: trustworthiness analysis of sensor networks in cyber-physical systems. In: 2010 IEEE International Conference on Data Mining. IEEE (2010)
Ciletti, M.D.: Modeling, Synthesis, and Rapid Prototyping with the Verilog HDL. Prentice Hall, Upper Saddle River (1999)
Han, G., Jiang, J., Shu, L., Niu, J., Chao, H.-C.: Management and applications of trust in wireless sensor networks: a survey. J. Comput. Syst. Sci. 80(3), 602–617 (2014)
Luo, T., Nagarajan, S.G.: Distributed anomaly detection using autoencoder neural networks in WSN for IoT. In: 2018 IEEE International Conference on Communications (ICC). IEEE (2018)
Xie, M., Hu, J., Han, S., Chen, H.-H.: Scalable hypergrid k-NN based online anomaly detection in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 24(8), 1661–1670 (2013)
Zhang, Y., Meratnia, N., Havinga, P.J.: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. Ad hoc Netw. 11(3), 1062–1074 (2013)
Bailey, D.L., et al.: Combined PET/MRI: global warming–summary report of the 6th International Workshop on PET/MRI, 27–29 March 2017, Tübingen, Germany. Mol. Imag. Biol. 20(1), 4–20 (2018)
Amazon rainforest fires, Everything we know and how you can help. https://www.cnet.com/how-to/amazon-rainforest-fire-whats-happening-now-and-how-you-can-help-update-indigeous-tribes/. Accessed 17 Aug 2019
Aguiar, A.P.D., et al.: Modeling the spatial and temporal heterogeneity of deforestation-driven carbon emissions: the INPE-EM framework applied to the Brazilian Amazon. Glob. Change Biol. 18(11), 3346–3366 (2012)
Saeed, F., et al.: IoT-based intelligent modeling of smart home environment for fire prevention and safety.’ J. Sens. Actuator Netw. 7(1), 11 (2018)
Adriano, D.B., Wahyu, A.C.B.: Iot-based integrated home security and monitoring system. J. Phys. Conf. Ser. 1140(1), 012006 (2018)
Mehta, R., et al.: Delivering high performance result with efficient use of K-map. Int. J. Control Autom. 9(2), 307–312 (2016)
Gai-ning, H.A.N., Qing-lin, X.U., Qun, D.U.A.N.: Research and application of digital circuit base on Iverilog and GTKWave. J. Xianyang Normal Univ. 4, 58–61 (2009)
Yadav, P., Abdul Rajak, A.R., Fathima, A.: VLSI implementation of gold sequence by novel method. Int. J. Adv. Stud. Comput. Sci. Eng. 2(2), 32 (2013)
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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