A Survey on IoT Smart Healthcare: Emerging Technologies, Applications, Challenges, and Future Trends

Tunc, M. Ali; Güreş, Emre; Shayea, Ibraheem

doi:10.48550/arxiv.2109.02042

Cited by 4 publications

(6 citation statements)

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“…Sworna, Islam, Shatabda and Islam [ 21 ] developed a distinct taxonomy from an IoT-ML context to help researchers in future research directions on HMS. Similarly, Tunc, et al [ 32 ] discussed the recent advances in smart HMS. Specifically, a thorough review of IoT, AI, fog computing, and Blockchain as emerging technologies in smart healthcare is presented.…”

Section: Related Workmentioning

confidence: 99%

“…Part of the contribution of this survey is that it examines the identified concepts highlighted in Table 1 using different cases. Several studies have reported these concepts singly or in pairs [ 15 , 26 , 31 , 32 , 33 , 34 , 40 , 43 , 44 , 52 ]. Nonetheless, this survey combined and examined all these concepts as a distinct taxonomy (See Figure 3 ).…”

Section: Related Workmentioning

confidence: 99%

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Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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“…SDN technology increases the flexible, dynamic and programmable functionality of network systems by providing a global overview and centralised control. The control and data plane are separated in the network [137]. Thanks to its central control feature, the SDN controller can globally monitor the load levels of nodes in the network.…”

Section: Future Directionsmentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

Self Cite

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The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

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“…Step 5: The memory unit 𝑐 𝑡 record the past data up to the existing moment that is evaluated as follows 𝑐 𝑡 = 𝑓 𝑡 ⨀𝑐 𝑡−1 + 𝑖 𝑡 ⨀𝑎 𝑡 (5) Step 6: The candidate state can be computed as follows:…”

Section: Figure 1 Framework Of Lstmmentioning

confidence: 99%

“…Government assistance applications will affect individuals' daily existence and reform the well-being business. For example, brilliant structures will empower their inhabitants to deal with life's day to day tasks while arriving at home naturally and change environment boundaries through somewhat controlled frameworks [5]. A similar thought applies to medical care offices and administrations where Internet-associated clinical gadgets accumulate and convey more significant information and direction.…”

Section: Introductionmentioning

confidence: 99%

Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System

Yuwono

2022

Int. j. commun. netw. inf. secur.

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A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique.

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A Survey on IoT Smart Healthcare: Emerging Technologies, Applications, Challenges, and Future Trends

Cited by 4 publications

References 0 publications

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System

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