In the realm of Health IoT, data analytics plays a crucial role in extracting meaningful insights from connected devices. It enables engineers to optimize healthcare systems and improve patient outcomes. Let’s explore this essential knowledge further.
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IoT Analytics in Healthcare: Empowering Integrated Care
Leveraging IoT analytics, healthcare professionals can monitor patients’ health remotely through smartphone apps. This data enables physicians to assess individual risk profiles and provide proactive treatment. However, there still exists a perception gap between mental health issues and diagnosis rates. The high cost of VR-based medical education and challenges in comprehending data analysis of connected medical devices hinder innovation in patient treatment. By utilizing historical data in hospitals’ data centers, valuable insights can be extracted from IoT platforms.
Reference: Müller, Stephan & Wiener, Patrick & Bürger, Adrian & Nimis, Jens. (2017). IoT for All: Architectural Design of an Extensible and Lightweight IoT Analytics Platform.
Market assessments
According to our latest market assessments, the overall growth of the Internet of Things (IoT) market is being impeded by the chip shortage. However, we anticipate an 8% increase in global IoT connectivity in 2021 and a projected 22.5 billion connections after one year. Furthermore, the market size for IoT solutions and services is expected to grow exponentially, reaching four to five times its current size by 2030.
On the other hand, in the first quarter of 2022, executives highlighted inflation and rising prices as significant concerns. Additionally, topics like Ukraine AI, SaaS, and cloud computing were increasingly discussed. Despite these market distractions, the value of IoT analytics continues to rise due to improvements in data collection efficiency.
Types of IoT Analytics
IoT analytics encompasses various techniques that data scientists employ to tackle different challenges and gain valuable insights. There are primarily four key fields in IoT analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
1. Predictive Analytics:
Predictive analysis leverages the power of machine learning to estimate the probability of future events and predict their outcomes. By utilizing extensive datasets, machine learning models can anticipate upcoming events. This predictive capability enables organizations to proactively improve expected outcomes.
2. Diagnostic Analytics:
Diagnostic analysis uncovers the underlying causes and mechanisms behind various occurrences. It is particularly useful in identifying anomalies, inefficiencies, and emerging trends. For instance, when IoT devices experience performance issues, diagnostic analytics can analyze the data collected from these devices to pinpoint the root causes of failures.
3. Descriptive Analytics:
Descriptive analysis focuses on understanding real-time data obtained from connected devices and networks. It involves measuring and assessing device performance to ensure proper operation. Additionally, descriptive analytics aids in identifying anomalies and gauging internal device usage.
4. Prescriptive Analytics:
Prescriptive analytics complements predictive analysis by providing insights on influencing future outcomes. This analytical approach offers recommendations and actionable guidance based on the predictions derived from predictive analytics. It aims to optimize decision-making processes and helps organizations reduce costs.
By leveraging the power of IoT analytics across these different fields, organizations can unlock valuable insights and optimize their operations for greater efficiency and success.
Challenges and Opportunities
The field of IoT analytics presents both challenges and opportunities. Handling the vast amounts of heterogeneous data generated by IoT devices poses difficulties in storage, management, and analysis. The limited data storage capacity for big files exacerbates these challenges. However, advancements in visualizations and the emergence of platforms like AWS IoT and Azure Stream Analytics offer solutions for predictive maintenance and real-time data-based analytics in IoT applications.
In a study conducted by Amaral et al. (2016), it was found that for the adoption of 5G technology to become widespread, there is a need for a massive system capacity and powerful edge nodes. These edge nodes should be able to offload the traffic from the core network. Additionally, in order to ensure uninterrupted operations of critical functions and scalability on the cloud, IoT applications require proper attention.
The Future of IoT Analytics
Comprehending data analytics within the realm of Health Internet of Things (IoT) is paramount for engineers. This comprehension equips them to unleash the complete potential of IoT devices and transform raw data into actionable business insights. Our exploration extends from the fundamental principles of IoT analytics to delineating various types of analyses, and elucidates its multifaceted applications across diverse industries.
The concept of Healthcare 4.0, which is a recent emergence inspired by Industry 4.0, signifies a notable evolution in the healthcare sector. Healthcare is significantly more digitized than in previous decades. This transformation is evident in various advancements, such as the proliferation of technologies ranging from traditional X-rays and magnetic resonance imaging to more sophisticated techniques like computed tomography and ultrasound scans and the widespread adoption of electronic medical records.
In the coming posts, we will delve into various existing IoT analytics platforms and explore how their data science capabilities drive the future of IoT analytics. Stay tuned for more updates!
Learn more
Amaral, L. A., de Matos, E., Tiburski, R. T., Hessel, F., Lunardi, W. T., & Marczak, S. (2016). Middleware technology for IoT systems: Challenges and perspectives toward 5G. In Modeling and Optimization in Science and Technologies (Vol. 8, pp. 333–367). Springer Verlag. https://doi.org/10.1007/978-3-319-30913-2_15
National University of Sciences and Technology, Islamabad, Pakistan, Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain. Journal of Communications. https://doi.org/10.12720/jcm.12.4.240-247
Jayaraman, P. P., Forkan, A. R. M., Morshed, A., Haghighi, P. D., & Kang, Y.-B. (2020). Healthcare 4.0: A review of frontiers in digital health. WIREs Data Mining and Knowledge Discovery, 10(2), e1350. https://doi.org/10.1002/widm.1350