EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0
Abstract
:1. Introduction
2. Literature Review
Ref. | Contribution | Limitation |
---|---|---|
[30] | Integration of database that contains feedback of the nodes. | Decentralized databases can cause integrity challenges. |
[31] | Utilization of Fuzzy rule to classify trustworthy and malicious nodes. | Performance needed to be evaluated in the IoT Environment. |
[33] | Hierarchical blockchain protocol that also supports mobility. | Not suitable for nodes with less computational capabilities due to complexity. |
[34] | The utilization of Jasang’s Subjective Logic (JSL) to examine the ambiguity of an entity. | Optimization at MAC Layer is required to overcome adequate energy consumption. |
[37] | Hyper-plane model along with middleware implements the decision function. | Unable to specify the framework of trust management. |
[38] | Fusion of MRC and SC that will help to maintain reliability. | Transmission of trust computation between multiple layers may raise integrity challenges. |
3. Proposed EdgeTrust Approach
Algorithm 1 EdgeTrust trust computation process |
|
3.1. Data Centers and Edge Clouds
3.2. IoT Edge Nodes
3.3. Trust Management Computations
3.4. Trust Aggregation and Development
3.5. Trust-Based Decision-Making
3.6. Recommendation-Based Indirect Trust
4. Results and Discussion
4.1. Aggregated Trust Evaluation
4.2. Honest and Dishonest Trust Accuracy
4.3. On-Off Attack
4.4. Self Promoting Attack
4.5. Good and Bad Mouthing Attacks
4.6. Energy Consumption Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Din, I.U.; Asmat, H.; Guizani, M. A review of information centric network-based internet of things: Communication architectures, design issues, and research opportunities. Multimed. Tools Appl. 2019, 78, 30241–30256. [Google Scholar] [CrossRef]
- Din, I.U.; Guizani, M.; Rodrigues, J.J.; Hassan, S.; Korotaev, V.V. Machine learning in the Internet of Things: Designed techniques for smart cities. Future Gener. Comput. Syst. 2019, 100, 826–843. [Google Scholar] [CrossRef]
- Gulzar, M.; Abbas, G. Internet of Things security: A survey and taxonomy. In Proceedings of the 2019 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 21–22 February 2019; pp. 1–6. [Google Scholar]
- Yan, Z.; Zhang, P.; Vasilakos, A.V. A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 2014, 42, 120–134. [Google Scholar] [CrossRef]
- Qu, C.; Tao, M.; Yuan, R. A hypergraph-based blockchain model and application in Internet of Things-enabled smart homes. Sensors 2018, 18, 2784. [Google Scholar]
- Guo, Y.; Wang, N.; Xu, Z.Y.; Wu, K. The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mech. Syst. Signal Process. 2020, 142, 106630. [Google Scholar] [CrossRef]
- Shafique, K.; Khawaja, B.A.; Sabir, F.; Qazi, S.; Mustaqim, M. Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access 2020, 8, 23022–23040. [Google Scholar] [CrossRef]
- Diène, B.; Rodrigues, J.J.; Diallo, O.; Ndoye, E.H.M.; Korotaev, V.V. Data management techniques for Internet of Things. Mech. Syst. Signal Process. 2020, 138, 106564. [Google Scholar] [CrossRef]
- Tseng, L.; Yao, X.; Otoum, S.; Aloqaily, M.; Jararweh, Y. Blockchain-based database in an IoT environment: Challenges, opportunities, and analysis. Clust. Comput. 2020, 23, 2151–2165. [Google Scholar] [CrossRef]
- Ephzibah, E.; Dharinya, S.S.; Remya, L. Decision Making Models Through AI for Internet of Things. In Internet of Things for Industry 4.0; Springer: Berlin/Heidelberg, Germany, 2020; pp. 57–72. [Google Scholar]
- Hui, H.; Zhou, C.; Xu, S.; Lin, F. A novel secure data transmission scheme in industrial internet of things. China Commun. 2020, 17, 73–88. [Google Scholar] [CrossRef]
- Sicari, S.; Rizzardi, A.; Coen-Porisini, A. 5G in the Internet of Things era: An overview on security and privacy challenges. Comput. Netw. 2020, 179, 107345. [Google Scholar] [CrossRef]
- Sharif, A.; Ouyang, J.; Yang, F.; Chattha, H.T.; Imran, M.A.; Alomainy, A.; Abbasi, Q.H. Low-cost inkjet-printed UHF RFID tag-based system for internet of things applications using characteristic modes. IEEE Internet Things J. 2019, 6, 3962–3975. [Google Scholar] [CrossRef]
- Wu, F.; Wu, T.; Yuce, M.R. An internet-of-things (IoT) network system for connected safety and health monitoring applications. Sensors 2019, 19, 21. [Google Scholar] [CrossRef] [Green Version]
- Singh, B. The Internet of Things: A Vision for Smart World. In Advances in Signal Processing and Communication; Springer: Berlin/Heidelberg, Germany, 2019; pp. 165–172. [Google Scholar]
- Janjua, K.; Shah, M.A.; Almogren, A.; Khattak, H.A.; Maple, C.; Din, I.U. Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies. Electronics 2020, 9, 1172. [Google Scholar] [CrossRef]
- Khan, M.A.; Din, I.U.; Jadoon, S.U.; Khan, M.K.; Guizani, M.; Awan, K.A. G-RAT| A novel graphical randomized authentication technique for consumer smart devices. IEEE Trans. Consum. Electron. 2019, 65, 215–223. [Google Scholar] [CrossRef]
- Gong, X.; Feng, T.; Albettar, M. PEASE: A PUF-Based Efficient Authentication and Session Establishment Protocol for Machine-to-Machine Communication in Industrial IoT. Electronics 2022, 11, 3920. [Google Scholar] [CrossRef]
- Qiu, J.; Tian, Z.; Du, C.; Zuo, Q.; Su, S.; Fang, B. A survey on access control in the age of internet of things. IEEE Internet Things J. 2020, 7, 4682–4696. [Google Scholar] [CrossRef]
- Awan, K.A.; Ud Din, I.; Almogren, A.; Almajed, H. AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things. Sensors 2020, 20, 6174. [Google Scholar] [CrossRef]
- Awan, K.A.; Din, I.U.; Almogren, A.; Almajed, H.; Mohiuddin, I.; Guizani, M. NeuroTrust-Artificial Neural Network-based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things. IEEE Internet Things J. 2020, 8, 15672–15682. [Google Scholar] [CrossRef]
- Almogren, A.; Mohiuddin, I.; Din, I.U.; Al Majed, H.; Guizani, N. FTM-IoMT: Fuzzy-based Trust Management for Preventing Sybil Attacks in Internet of Medical Things. IEEE Internet Things J. 2020, 8, 4485–4497. [Google Scholar] [CrossRef]
- Haseeb, K.; Almogren, A.; Ud Din, I.; Islam, N.; Altameem, A. SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things. Sensors 2020, 20, 2468. [Google Scholar] [CrossRef]
- García-García, L.; Jiménez, J.M.; Abdullah, M.T.A.; Lloret, J. Wireless technologies for IoT in smart cities. Netw. Protoc. Algorithms 2018, 10, 23–64. [Google Scholar] [CrossRef]
- Tortorella, G.L.; Fogliatto, F.S.; Esposto, K.F.; Mac Cawley Vergara, A.; Vassolo, R.; Tlapa Mendoza, D.; Narayanamurthy, G. Measuring the effect of Healthcare 4.0 implementation on hospitals’ performance. Prod. Plan. Control 2022, 33, 386–401. [Google Scholar] [CrossRef]
- Galletly, C.L.; Barreras, J.L.; Lechuga, J.; Glasman, L.R.; Cruz, G.; Dickson-Gomez, J.B.; Brooks, R.A.; Ruelas, D.M.; Stringfield, B.; Espinoza-Madrigal, I. US public charge policy and Latinx immigrants’ thoughts about health and healthcare utilization. Ethn. Health, 2022; 1–18, online ahead of print. [Google Scholar]
- Sony, M.; Antony, J.; McDermott, O. The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review. Hosp. Top. 2022; 1–17, online ahead of print. [Google Scholar]
- Gardas, B.B. Organizational hindrances to Healthcare 4.0 adoption: An multi-criteria decision analysis framework. J. Multi-Criteria Decis. Anal. 2022, 29, 186–195. [Google Scholar] [CrossRef]
- Mahajan, H.B.; Rashid, A.S.; Junnarkar, A.A.; Uke, N.; Deshpande, S.D.; Futane, P.R.; Alkhayyat, A.; Alhayani, B. Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems. Appl. Nanosci. 2022; 1–14, online ahead of print. [Google Scholar]
- Azad, M.A.; Bag, S.; Hao, F.; Shalaginov, A. Decentralized self-enforcing trust management system for social Internet of Things. IEEE Internet Things J. 2020, 7, 2690–2703. [Google Scholar] [CrossRef]
- Esposito, C.; Tamburis, O.; Su, X.; Choi, C. Robust Decentralised Trust Management for the Internet of Things by Using Game Theory. Inf. Process. Manag. 2020, 57, 102308. [Google Scholar] [CrossRef]
- Lloret, J.; Parra, L.; Taha, M.; Tomás, J. An architecture and protocol for smart continuous eHealth monitoring using 5G. Comput. Netw. 2017, 129, 340–351. [Google Scholar] [CrossRef]
- Kouicem, D.E.; Imine, Y.; Bouabdallah, A.; Lakhlef, H. A Decentralized Blockchain-Based Trust Management Protocol for the Internet of Things. IEEE Trans. Dependable Secur. Comput. 2020, 19, 1292–1306. [Google Scholar] [CrossRef]
- Khan, Z.A. Using energy-efficient trust management to protect IoT networks for smart cities. Sustain. Cities Soc. 2018, 40, 1–15. [Google Scholar] [CrossRef]
- Khattak, H.A.; Tehreem, K.; Almogren, A.; Ameer, Z.; Din, I.U.; Adnan, M. Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities. J. Inf. Secur. Appl. 2020, 55, 102615. [Google Scholar] [CrossRef]
- Siddiqua, A.; Shah, M.A.; Khattak, H.A.; Din, I.U.; Guizani, M. ICAFE: Intelligent congestion avoidance and fast emergency services. Future Gener. Comput. Syst. 2019, 99, 365–375. [Google Scholar] [CrossRef]
- Caminha, J.; Perkusich, A.; Perkusich, M. A smart middleware to detect on-off trust attacks in the Internet of Things. In Proceedings of the 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 12–14 January 2018. [Google Scholar]
- Chen, J.I.Z. Embedding the MRC and SC Schemes into Trust Management Algorithm Applied to IoT Security Protection. Wirel. Pers. Commun. 2018, 99, 461–477. [Google Scholar] [CrossRef]
- Mukherjee, S.; Sharma, N. Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technol. 2012, 4, 119–128. [Google Scholar] [CrossRef] [Green Version]
- Rish, I. An empirical study of the naive Bayes classifier. In Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA, 4–6 August 2001; Volume 3, pp. 41–46. [Google Scholar]
- Triantafyllou, A.; Sarigiannidis, P.; Lagkas, T.D. Network protocols, schemes, and mechanisms for internet of things (iot): Features, open challenges, and trends. Wirel. Commun. Mob. Comput. 2018, 2018, 5349894. [Google Scholar] [CrossRef] [Green Version]
- Zinca, D.; Popa, M.O. Development of a ZettaJS driver for the ESP8266 IoT hardware. In Proceedings of the 2018 International Symposium on Electronics and Telecommunications (ISETC), Timișoara, Romania, 8–9 November 2018; pp. 1–4. [Google Scholar]
- Elsayed, K.; Ibrahim, M.A.B.; Hamza, H.S. Service discovery in heterogeneous IoT environments based on OCF/IoTivity. In Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, 24–28 June 2019; pp. 1160–1165. [Google Scholar]
- Gočal, P.; Macko, D. EEMIP: Energy-efficient communication using timing channels and prioritization in ZigBee. Sensors 2019, 19, 2246. [Google Scholar] [CrossRef] [Green Version]
- Qureshi, K.N.; Iftikhar, A.; Bhatti, S.N.; Piccialli, F.; Giampaolo, F.; Jeon, G. Trust management and evaluation for edge intelligence in the Internet of Things. Eng. Appl. Artif. Intell. 2020, 94, 103756. [Google Scholar] [CrossRef]
- Das, R.; Singh, M.; Majumder, K. SGSQoT: A community-based trust management scheme in Internet of Things. In Proceedings of the International Ethical Hacking Conference, Kolkata, India, 31 March–1 April 2018; pp. 209–222. [Google Scholar]
Parameters | Value |
---|---|
Area of Network | 300 (m) |
No. of Nodes | 400∼600 |
Simulation Time | 600∼1100 |
Trust Degree | 0.0-1 |
MAC | IEEE 802.11 |
Transmission Rate | 3∼5 Mbps |
Size of Packet | 20∼30 |
Peak Transmission Range | 323 (m) |
Node Placement | Uniform |
Maximum Connection | 11 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Awan, K.A.; Ud Din, I.; Almogren, A.; Khattak, H.A.; Rodrigues, J.J.P.C. EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0. Electronics 2023, 12, 140. https://doi.org/10.3390/electronics12010140
Awan KA, Ud Din I, Almogren A, Khattak HA, Rodrigues JJPC. EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0. Electronics. 2023; 12(1):140. https://doi.org/10.3390/electronics12010140
Chicago/Turabian StyleAwan, Kamran Ahmad, Ikram Ud Din, Ahmad Almogren, Hasan Ali Khattak, and Joel J. P. C. Rodrigues. 2023. "EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0" Electronics 12, no. 1: 140. https://doi.org/10.3390/electronics12010140