Abstract
Internet of Things (IoT) based wearable healthcare data monitoring is an emerging application of smart medicines. IoT-based communication and information exchange methods make it adaptable for the recent sixth-generation computing systems. The terahertz and large-scale processing of the 6th generation communication and computation technology is assimilated in the wearable sensor data exchange process. In this article, a fault-tolerant data processing method (FTDPM) is proposed to handle uneven sensor data. The non-uniform and unsynchronised observation interval-based sensor data is analyzed for its impact on healthcare recommendations. The reliability in the recommendation ensures the precise need for sensor data processing, mitigating the faults. In this reliability estimation process, a support vector machine classifier is used. This learning classifier differentiates the uniform and non-uniform sensor data traffic for providing reliable recommendations. The data from the uniform process is replicated in the non-uniform sequence for recommendation filling. This is carried out based on marginal classification and near-to-reliable data as classified using SVM. The IoT wearable alliance is exploited using the 6G communication paradigms in handling monitored data. The proposed method's performance is verified using the metrics processing time, recommendation failure, processing complexity, and recommendation response time.
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Manogaran, G., Alazab, M., Saravanan, V., Rawal, B.S., Sundarasekar, R., Nagarajan, S.M., Kadry, S., Montenegro-Marin, C.E.: Machine learning assisted information management scheme in service concentrated IoT. IEEE Trans Ind Inf 17, 2871–2879 (2020)
Yang, G., Jiang, M., Ouyang, W., Ji, G., Xie, H., Rahmani, A.M., et al.: IoT-based remote pain monitoring system: from device to cloud platform. IEEE J Biomed Health Inform 22(6), 1711–1719 (2017)
Manogaran, G., Shakeel, P.M., Fouad, H., Nam, Y., Baskar, S., Chilamkurti, N., Sundarasekar, R.: Wearable IoT smart-log patch: an edge computing-based Bayesian deep learning network system for multi access physical monitoring system. Sensors. 19(13), 3030 (2019)
Guo, X., Lin, H., Wu, Y., Peng, M.: A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur Gener Comput Syst 113, 407–417 (2020)
Taheri, R., Shojafar, M., Alazab, M., Tafazolli, R.: FED-IIoT: a robust federated malware detection architecture in industrial IoT. IEEE Trans Ind Inf. 17, 8442–8452 (2020)
Gheisari, M., Najafabadi, H.E., Alzubi, J.A., Gao, J., Wang, G., Abbasi, A.A., Castiglione, A.: OBPP: an ontology-based framework for privacy-preserving in IoT-based smart city. Futur Gener Comput Syst 123, 1–13 (2021)
Al-Turjman, F., Alturjman, S.: Context-sensitive access in industrial internet of things (IIoT) healthcare applications. IEEE Trans Ind Inf. 14(6), 2736–2744 (2018)
Billah MFRM, Saoda N, Gao J, Campbell B (2021) BLE can see: a reinforcement learning approach for RF-based indoor occupancy detection. In: Proceedings of the 20th international conference on information processing in sensor networks (co-located with CPS-IoT week 2021), pp 132–147.
Al-Turjman, F., Alturjman, S.: 5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications. Multimed Tools Appl 79(13), 8627–8648 (2020)
Hadi, M.S., Lawey, A.Q., El-Gorashi, T.E., Elmirghani, J.M.: Patient-centric HetNets powered by machine learning and big data analytics for 6G networks. IEEE Access 8, 85639–85655 (2020)
Farivar, F., Haghighi, M.S., Jolfaei, A., Alazab, M.: Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT. IEEE Trans Industr Inf 16(4), 2716–2725 (2019)
Liao, H., Zhou, Z., Zhao, X., Zhang, L., Mumtaz, S., Jolfaei, A., Ahmed, S.H., Bashir, A.K.: Learning-based context-aware resource allocation for edge-computing-empowered industrial IoT. IEEE Internet Things J 7(5), 4260–4277 (2019)
Challa, S., Wazid, M., Das, A.K., Kumar, N., Reddy, A.G., Yoon, E.J., Yoo, K.Y.: Secure signature-based authenticated key establishment scheme for future IoT applications. IEEE Access. 5, 3028–3043 (2017)
Alam, M.M., Malik, H., Khan, M.I., Pardy, T., Kuusik, A., Le Moullec, Y.: A survey on the roles of communication technologies in IoT-based personalised healthcare applications. IEEE Access 6, 36611–36631 (2018)
Amin, R., Kumar, N., Biswas, G.P., Iqbal, R., Chang, V.: A light weight authentication protocol for IoT-enabled devices in distributed cloud computing environment. Futur Gener Comput Syst 78, 1005–1019 (2018)
Coulby, G., Clear, A., Jones, O., Young, F., Stuart, S., Godfrey, A.: Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design. Biomed. Eng. Online 19(1), 1–24 (2020)
Nguyen, N.T., Liu, B.H.: The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-hard. IEEE Syst. J. 13(2), 1312–1315 (2018)
Wan, J., Al-awlaqi, M.A., Li, M., O’Grady, M., Gu, X., Wang, J., Cao, N.: Wearable IoT enabled real-time health monitoring system. EURASIP J Wirel Commun Netw 2018(1), 298 (2018)
Ogudo, K.A., Muwawa Jean Nestor, D., Ibrahim Khalaf, O., Daei Kasmaei, H.: A device performance and data analytics concept for smartphones’ IoT services and machine-type communication in cellular networks. Symmetry. 11(4), 593 (2019)
Mukherjee, R., Ghorai, S.K., Gupta, B., Chakravarty, T.: Development of a wearable remote cardiac health monitoring with alerting system. Instrum Exp Tech 63, 273–283 (2020)
Goyal S, Sharma N, Bhushan B, Shankar A, Sagayam M (2020) IoT enabled technology in secured healthcare: applications, challenges and future directions. In: Cognitive internet of medical things for smart healthcare. Springer, Cham, pp 25–48
Liu BH, Nguyen NT (2014) An efficient method for sweep coverage with minimum mobile sensor. In: 2014 Tenth international conference on intelligent information hiding and multimedia signal processing. IEEE, pp 289–292
Lee, U., Han, K., Cho, H., Chung, K.M., Hong, H., Lee, S.J., et al.: Intelligent positive computing with mobile, wearable, and IoT devices: literature review and research directions. Ad Hoc Netw. 83, 8–24 (2019)
Jiang, J., Hu, L.: Decentralised federated learning with adaptive partial gradient aggregation. CAAI Trans Intell Technol 5(3), 230–236 (2020). https://doi.org/10.1049/trit.2020.0082
Lu, W., Fan, F., Chu, J., Jing, P., Yuting, S.: Wearable computing for Internet of things: a discriminant approach for human activity recognition. IEEE Internet Things J 6(2), 2749–2759 (2018)
Haghi, M., Neubert, S., Geissler, A., Fleischer, H., Stoll, N., Stoll, R., Thurow, K.: A flexible and pervasive IoT based healthcare platform for physiological and environmental parameters monitoring. IEEE Internet Things J 7, 5628–5647 (2020)
Sarmah, S.S.: An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. IEEE Access 8, 135784–135797 (2020)
Albahri, O.S., Albahri, A.S., Zaidan, A.A., Zaidan, B.B., Alsalem, M.A., Mohsin, A.H., et al.: Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 7, 50052–50080 (2019)
Khowaja, S.A., Prabono, A.G., Setiawan, F., Yahya, B.N., Lee, S.L.: Contextual activity based Healthcare internet of things, services, and people (HIoTSP): an architectural framework for healthcare monitoring using wearable sensors. Comput Netw 145, 190–206 (2018)
Huifeng, W., Kadry, S.N., Raj, E.D.: Continuous health monitoring of sportsperson using IoT devices based wearable technology. Comput Commun 160, 588–595 (2020)
Fouad, H., Mahmoud, N.M., El Issawi, M.S., Al-Feel, H.: Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system. Comput Commun 151, 257–265 (2020)
Ali, F., Islam, S.R., Kwak, D., Khan, P., Ullah, N., Yoo, S.J., Kwak, K.S.: Type-2 fuzzy ontology-aided recommendation systems for IoT-based healthcare. Comput Commun 119, 138–155 (2018)
Manas, M., Sinha, A., Sharma, S., Mahboob, M.R.: A novel approach for IoT based wearable health monitoring and messaging system. J Ambient Intell Humaniz Comput 10(7), 2817–2828 (2019)
Alfarraj, O., Tolba, A.: Unsynchronised wearable sensor data analytics model for improving the performance of smart healthcare systems. J Ambient Intell Hum Comput 12, 3411–3422 (2020)
Zhou, H., Montenegro-Marin, C.E., Hsu, C.H.: Wearable IoT based cloud assisted framework for swimming persons in health monitoring system. Curr Psychol (2020). https://doi.org/10.1007/s12144-020-00822-0
Wu, T., Wu, F., Qiu, C., Redoute, J.M., Yuce, M.R.: A rigid-flex wearable health monitoring sensor patch for IoT-connected healthcare applications. IEEE Internet Things J 7, 6932–6945 (2020)
Moghadas, E., Rezazadeh, J., Farahbakhsh, R.: An IoT patient monitoring based on fog computing and data mining: cardiac arrhythmia usecase. Internet Things 11, 100251 (2020)
Yacchirema, D., Sarabia-Jácome, D., Palau, C.E., Esteve, M.: System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive Mob Comput 50, 25–40 (2018)
https://archive.ics.uci.edu/ml/datasets/WESAD+%28Wearable+Stress+and+Affect+Detection%29#
Song, J., Zhong, Q., Wang, W., Su, C., Tan, Z., Liu, Y.: FPDP: flexible privacy-preserving data publishing scheme for smart agriculture. IEEE Sensors J 21, 17430 (2020)
Wang, W., Huang, H., Zhang, L., Su, C.: Secure and efficient mutual authentication protocol for smart grid under blockchain. Peer-to-Peer Netw Appl 14, 2681 (2020)
Zhang, L., Zhang, Z., Wang, W., Jin, Z., Su, Y., Chen, H.: Research on a covert communication model realized by using smart contracts in blockchain environment. IEEE Syst J (2021). https://doi.org/10.1109/JSYST.2021.3057333
Zhang, L., Zou, Y., Wang, W., Jin, Z., Su, Y., Chen, H.: Resource allocation and trust computing for blockchain-enabled edge computing system. Comput Secur 105, 102249 (2021)
Wang, W. and Su, C., 2020, September. Ccbrsn: a system with high embedding capacity for covert communication in bitcoin. In: IFIP international conference on ICT systems security and privacy protection. Springer, Cham pp 324–337
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BM: Conception and design of study. KA: acquisition of data. BM: analysis and/or interpretation of data. MRP: Drafting the manuscript.
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Pradhan, M.R., Mago, B. & Ateeq, K. A classification-based sensor data processing method for the internet of things assimilated wearable sensor technology. Cluster Comput 26, 807–822 (2023). https://doi.org/10.1007/s10586-022-03605-3
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DOI: https://doi.org/10.1007/s10586-022-03605-3