A Novel Resilient and Intelligent Predictive Model for CPS-Enabled E-Health Applications

McKee DW, Clement SJ, Almutairi J, Xu J. Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems. CAAI Trans Intell Technol. 2018;3(2):75–82.

Article  Google Scholar 

AlZubi AA, Al-Maitah M, Alarifi A. Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques. Soft Comput. 2021;25(18):12319–32.

Article  Google Scholar 

Sobb T, Turnbull B, Moustafa N. A holistic review of cyber–physical–social systems: new directions and opportunities. Sensors. 2023;23(17):7391.

Article  Google Scholar 

Pasandideh S, Pereira P, Gomes L. Cyber-physical-social systems: taxonomy, challenges, and opportunities. IEEE Access. 2022;10:42404–19.

Article  Google Scholar 

Abbasian Dehkordi S, Farajzadeh K, Rezazadeh J et al. A survey on data aggregation techniques in IoT sensor networks. Wire Netw. 2020;26:1243–63. https://doi.org/10.1007/s11276-019-02142-z.

Shyama M, Pillai AS, Anpalagan A. Self-healing and optimal fault tolerant routing in wireless sensor networks using genetical swarm optimization. Comput Netw. 2022;217:109359.

Article  Google Scholar 

Elayan H, Aloqaily M, Guizani M. Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 2021;8(23):16749–57.

Article  Google Scholar 

Sworna NS, Islam AM, Shatabda S, Islam S. Towards development of IoT-ML driven healthcare systems: a survey. J Netw Comput Appl. 2021;196:103244.

Article  Google Scholar 

Gope P, Gheraibia Y, Kabir S, Sikdar B. A secure IoT-based modern healthcare system with fault-tolerant decision making process. IEEE J Biomed Health Inform. 2020;25(3):862–73.

Article  Google Scholar 

Rault T, Bouabdallah A, Challal Y. Energy efficiency in wireless sensor networks: a top-down survey. Comput Netw. 2014;67:104–22.

Article  Google Scholar 

Kocakulak M, Butun I. An overview of wireless sensor networks towards internet of things. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA; 2017. p. 1–6. https://doi.org/10.1109/CCWC.2017.7868374.

Beraldi R, Canali C, Lancellotti R, Mattia GP. Distributed load balancing for heterogeneous fog computing infrastructures in smart cities. Pervasive Mob Comput. 2020;67:101221.

Article  Google Scholar 

Kumar KA, Jayaraman K. Irrigation control system-data gathering in WSN using IOT. Int J Commun Syst. 2020;33(16):e4563.

Article  Google Scholar 

Dhungana A, Bulut E. Energy balancing in mobile opportunistic networks with wireless charging: Single and multi-hop approaches. Ad Hoc Netw. 2021;111:102342.

Article  Google Scholar 

Saba T, Haseeb K, Rehman A, Jeon G. Blockchain-enabled intelligent IoT protocol for high-performance and secured big financial data transaction. IEEE Trans Comput Soc Syst. 2024;11(2):1667–74. https://doi.org/10.1109/TCSS.2023.3268592.

Article  Google Scholar 

Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: a review. Sensors. 2018;18(8):2674.

Article  Google Scholar 

Pundir M, Sandhu JK. A systematic review of quality of service in wireless sensor networks using machine learning: recent trend and future vision. J Netw Comput Appl. 2021;188:103084.

Article  Google Scholar 

Ali R, Pal AK, Kumari S, Karuppiah M, Conti M. A secure user authentication and key-agreement scheme using wireless sensor networks for agriculture monitoring. Futur Gener Comput Syst. 2018;84:200–15.

Article  Google Scholar 

Banerjee A, Mitra A, Biswas A. An integrated application of IoT-based WSN in the field of Indian agriculture system using hybrid optimization technique and machine learning. 171–187. https://doi.org/10.1002/9781119769231.ch9.

Alrajeh NA, Khan S, Lloret J, Loo J. Secure routing protocol using cross-layer design and energy harvesting in wireless sensor networks. Int J Distrib Sens Netw. 2013;9(1):374796.

Article  Google Scholar 

Du J, Jiang C, Wang J, Ren Y, Debbah M. Machine learning for 6G wireless networks: carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service. IEEE Veh Technol Mag. 2020;15(4):122–34.

Article  Google Scholar 

Merenda M, Porcaro C, Iero D. Edge machine learning for AI-enabled IoT devices: a review. Sensors. 2020;20(9):2533.

Article  Google Scholar 

Chen F, Tang Y, Wang C, Huang J, Huang C, Xie D, Wang T, Zhao C. Medical cyber–physical systems: a solution to smart health and the state of the art. IEEE Trans Comput Soc Syst. 2021;9(5):1359–86.

Article  Google Scholar 

Gati NJ, Yang LT, Feng J, Nie X, Ren Z, Tarus SK. Differentially private data fusion and deep learning framework for cyber–physical–social systems: state-of-the-art and perspectives. Inf Fusion. 2021;76:298–314.

Article  Google Scholar 

Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P. A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun Surv Tutor. 2019;21(3):2419–65.

Article  Google Scholar 

Haseeb-Ur-Rehman RMA, et al. Sensor cloud frameworks: state-of-the-art, taxonomy, and research issues. IEEE J Sens. 2021;21(20):22347–70. https://doi.org/10.1109/JSEN.2021.3090967.

Snigdh I, Surani SS, Sahu NK. Energy conservation in query driven wireless sensor networks. Microsyst Technol. 2021;27(3):843–51.

Article  Google Scholar 

Abbas G, Mehmood A, Carsten M, Epiphaniou G, Lloret J. Safety, security and privacy in machine learning based Internet of Things. J Sens Actuator Netw. 2022;11(3):38.

Article  Google Scholar 

Saba T, Rehman A, Haseeb K, et al. Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence. Cluster Comput. 2023;26:2921–31. https://doi.org/10.1007/s10586-022-03916-5.

Article  Google Scholar 

Abdellatif AA, Mohamed A, Chiasserini CF, Erbad A, Guizani M. Edge computing for energy-efficient smart health systems: data and application-specific approaches. In: Energy efficiency of medical devices and healthcare applications. Elsevier; 2020. p. 53–67.

Chapter  Google Scholar 

Singh A, Satapathy SC, Roy A, et al. AI-Based mobile edge computing for IoT: applications, challenges, and future scope. Arab J Sci Eng. 2022;47:9801–31. https://doi.org/10.1007/s13369-021-06348-2.

Article  Google Scholar 

Saba T, Rehman A, Haseeb K, Bahaj SA, Jeon G. Energy-efficient edge optimization embedded system using graph theory with 2-tiered security. Electronics. 2022;11(18):2942.

Article  Google Scholar 

Yassien MB, Aljawarneh SA, Eyadat M, et al. Routing protocol for low power and lossy network–load balancing time-based. Int J Mach Learn Cyber. 2021;12:3101–14. https://doi.org/10.1007/s13042-020-01261-w.

Article  Google Scholar 

Seng KP, Ang LM, Ngharamike E. Artificial intelligence Internet of Things: a new paradigm of distributed sensor networks. Int J Distrib Sens Netw. 2022;18(3):15501477211062836.

Article  Google Scholar 

Tabassum M, Perumal S, Kashem SBA, et al. Enhance data availability and network consistency using artificial neural network for IoT. Multimed Tools Appl. 2024;83:3111–31. https://doi.org/10.1007/s11042-022-13337-6.

Article  Google Scholar 

Nguyen DC, Pathirana PN, Ding M, Seneviratne A. Blockchain for secure ehrs sharing of mobile cloud based e-health systems. IEEE Access. 2019;7:66792–806.

Article  Google Scholar 

Natarajan R, Lokesh GH, Flammini F, Premkumar A, Venkatesan VK, Gupta SK. A novel framework on security and energy enhancement based on Internet of medical things for Healthcare 5.0. Infrastructures. 2023;8(2):22.

Article  Google Scholar 

Almalki FA, Ben Othman S, Almalki FA, Sakli H. EERP-DPM: energy efficient routing protocol using dual prediction model for healthcare using IoT. J Healthc Eng. 2021;2021:1–15.

Article  Google Scholar 

Ullah F, Khan MZ, Faisal M, Rehman HU, Abbas S, Mubarek FS. An energy efficient and reliable routing scheme to enhance the stability period in wireless body area networks. Comput Commun. 2021;165:20–32.

Article  Google Scholar 

Glover F. Future paths for integer programming and links to artificial intelligence. Comput Oper Res. 1986;13(5):533–49.

Article  MathSciNet  Google Scholar 

Keränen A, Ott J, Kärkkäinen T. The ONE simulator for DTN protocol evaluation. In Proc. SIMUTools 2nd Int. Conf. Simulat. Tools Techn; 2009, pp. 1–9. https://doi.org/10.4108/ICST.SIMUTOOLS2009.5674.

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