The Internet of Things (IoT) is a technology that is gaining increasing ground around the world and is involved in most areas of activity. In the healthcare sector, IoT devices play an important role, providing relevant information that can be used to make decisions in real time. As a result, the reliability of the collected, analyzed, and processed data is a vital element to ensuring the reliability of this system used in a field such as healthcare, where the consequences of errors can be fatal. However, the data in this system are often exposed to attacks. Therefore, it is important to find appropriate technology and mechanisms to ensure data security. In this work, our objective is to conduct a study of the existing situation in order to understand how to efectively secure IoT data used in smart health. To achieve this, a bibliometric study is performed by examining 2584 documents. The data were visualized using VOSviewer and Bibliometrix and showed that countries such as India, China and United States remain the undisputed leaders in the field. Chinese institutions maintain the expertise followed by India, but collaborate little with foreign researchers, unlike the United States, which is cooperative. They have developed some very interesting solutions, and it is important to learn from the experience of these countries and contextualize them for efective development in Africa. Technically, blockchain remains the most widely used technology for data protection, but the issues of security and protection of patient data remain a major challenge that involves several other parameters.
survey of what already exists in order to understand how to efectively secure IoT data used in health intelligence. Specifically, it involves: • A systematic bibliometric analysis of the scientific literature on the security of IoT systems applied to healthcare; • A critical qualitative analysis of the major contributions identified; • A specific focus on African challenges and opportunities; • Finally, the proposal of a model for adapting existing solutions to the technical, social and legal realities of African countries.
To meet these objectives, we address the understanding of trends and implications of research in terms of IoT data security in healthcare, to provide a comprehensive overview of the development of the field and future directions. In the remainder of this paper, in session 2 we present the work that has carried out a review of the existing literature, in particular a bibliometric study, on data security in medical IoT systems. In session 3, we present our methodological approach and in session 4 the results obtained in this bibliometric study, followed by a discussion.
A bibliometric study is a study that provides a summary of research reported in scientific publications,
allowing researchers to generate quantitative information from existing data [
Despite the multitude of works that focus on a survey of IoT in healthcare, there is a gap in the literature
in terms of studies that address the security of the generated data. Very few studies focus on the case of
developing countries. In this sense, in [
The methodology adopted for this research is divided into several phases. Firstly, the data for this research was collected from the Scopus database, which includes 2584 published between 2020 and 2025. The data extraction date is 25 April 2025 and the research string is: (("Internet of Things" OR "Connected object" OR IoT OR "smart health" ) AND ( "Data security" OR "Data Protection" OR "Information security" OR "Data Integrity" OR "data confidentiality" OR "data privacy" ) AND ( health OR medical OR sick OR patient )). The data visualization and analysis phase enabled us to explore our database, obtained after standardizing the search terms on Scopus, and to extract the information needed to achieve our objectives for this study. The VOSviewer and RStudio software with the Bibliometrix library were used to view and analyses the data, producing the results presented in the next section.
Bibliometric analysis is a powerful tool that uses statistical approaches to detect descriptive and analytical variations in a research subject. It allows trends, productivity and future directions of research to be assessed in order to draw meaningful conclusions. In this section, we present the results of our bibliometric analysis in order to meet the research objectives presented in section 1.
Figure 1 illustrate annual distribution of scientific production on the data base collected. From 2020 to 2025, we saw a steady increase in the number of articles published each year. The number of publications increased from 216 in 2020 to 833 in 2024, a nearly 4-fold increase in 4 years. Already 236 articles have been published for the first quarter of 2025.
In this figure, India, China, the United States and Saudi Arabia dominating in terms of total number of publications. However, very little contribution comes from countries such as the African countries. Only countries such as Nigeria, Ghana, Senegal, Algeria and Egypt are featured with a small contribution. We can also see that there appears to be fairly dense international collaboration in the field, with several groups of countries working more closely together. India, China and the United States are major centers, indicating their strong involvement in international projects; Europe forms a dense group, showing numerous regional collaborations; Africa tries to situate itself in relation to three countries, India, China and Saudi Arabia, with which collaborations are carried out.
Figure 4 shows the co-occurrence of terms in the field. We can see that the data security aspect has been taken seriously in this period, with an emphasis on blockchain technology, integrating artificial intelligence, machine learning, and deep learning...
clarifying that themes such as "Internet of Things", "network security", "data protection" and "blockchain" appear as moderately central and developed topics. This suggests that they are at the heart of the discussions in the corpus studied. These concepts form a structuring axis of the field but still require eforts to reach full maturity.
Several distinct color clusters can be identified in this figure: • The red cluster centered on the theme "authentication", including terms such as "cryptography", "privacy prevention", "IoT security", "digital signature", highlights the security aspects of the systems. Authentication is a central term in the red security cluster, but it also has links with other clusters, notably healthcare. • A blue cluster associated with "data privacy" and including terms such as "data protection", "privacy preservation", "diferential privacy", "trust", etc., highlighting the challenges of data protection. • A yellow cluster around "blockchain", with terms such as "smart contracts", "distributed ledger", "ethereum" etc, indicating the application of blockchain. Blockchain appears to be a bridge between security and specific applications such as smart contracts and potentially healthcare. • A green cluster linked to "artificial intelligence" and "machine learning", with terms such as "big data" and "predictive analytics", presenting technologies commonly found in the system. • A cluster associated with "data management", "data acquisition", "classification", "clustering", etc., representing aspects of data management and analysis.
list of the 10 best authors.
Most of the most cited works in our database are systematic literature reviews. This shows how important it is for the research community to have an in-depth view of the subject of IoT data security and protection in healthcare. In the healthcare system, medical information is sensitive by nature, and its security is a challenge. Whatever the use of IoT in the healthcare sector, the attack surface and vulnerabilities of the IoT infrastructure the security risks are inherent in any connected device. From these various studies, it is clear that for reliable and sustainable deployment in Africa, certain factors need to be taken into account.
We propose a structured adaptation model based on certain criteria, including: • Appropriate technological choices: low-cost devices, low energy consumption. • Legal aspects: proposal for a minimum medical data protection framework adapted to the African context. • Training and awareness: define training programmes for local users (healthcare professionals, technicians). • Deployment strategy: giving priority to distributed architectures to reduce dependence on unstable networks.
Firstly, the IoT system must be based on reliable infrastructure and quality Internet connectivity (see wireless networks and move towards 5G). Secondly, to ensure data security, algorithms combining machine learning and blockchain must be implemented. In this system, each device must be identiefid by a unique address, using Web transfer protocols such as the Constrained Application Protocol (CoAP). REST APIs must also be used, enabling clients to interact with the blockchain network, which benefits from double security. For implementation in these systems, these complementary technologies need to be considered to accelerate adoption and profitability. Energy optimization must also be a priority. Finally, a blockchain framework for IoT in healthcare needs to be considered. There is a need for explicit IoT security regulations and standards generally. The framework must take into account not only the techno-economic but also the legal realities of African countries.
Therefore, several aspects need to be taken into account to ensure the secure deployment of this technology. For each aspect, we need to:
Equipments : • Biometric sensors adapted to users’ needs. • Wearable devices for remote monitoring of the patient’s condition. • Connected medical devices with simple, ergonomic interfaces. • Secure transmission to the local IoT gateway via (LoRaWAN, NB-IoT, mobile phone networks). • Edge processing: Maximise local data processing (IoT gateway) to reduce dependency on a stable internet connection and limit latency in the event of critical alerts. • Local security: Manage device authentication and data encryption. o Redundancy: Provide mechanisms to keep devices and applications running in the event of temporary loss of connection to the central network.
Data analysis and security • Tools for analyzing the data collected (big data, artificial intelligence), to identify trends, improve clinical decision-making, and support public health policies.
• Develop a blockchain to strengthen system security.
Legal and policy aspects • Draw up clear principles relating to the quality of consent, purpose, minimisation of collection, limitation of storage periods, security and confidentiality. • Establishment of a national authority to monitor compliance with the legal framework for the processing of personal data and penalise breaches.
• Inclusion of data protection modules in the training of health professionals and technicians.
In this article, we conduct a bibliometric study on the secure development of IoT in healthcare. The Internet of connected objects requires particular attention in terms of protecting user data, which is why researchers in recent years have focused on emerging technologies such as Bitcoin, artificial intelligence, machine learning and deep learning to guarantee the integrity of user data, It would be desirable for the major leaders (China, India, USA, Germany), who are at the heart of the collaborative networks, to strengthen their scientific cooperation in order to find solutions to the problems that remain unresolved in this field, and for these nations to try to encourage scientists from other countries who want to tackle this issue. The study revealed that the Internet of Things (IoT), data privacy, and blockchain occupy a central place, indicating that they constitute the pillars of the efild. The study, based on Scopus data, suggests that similar studies in combination with several databases would provide additional in-depth information to understand the subject and compare it to produce more relevant analyzes. Analysis of the most cited articles has enabled us to understand that for secure deployment in Africa, it is necessary to opt for a combination of technologies such as machine learning and blockchain and well-determined protocols. There is an urgent need for explicit IoT security regulations and standards, as well as a comprehensive framework that takes into account all the challenges of IoT deployment in the specific context of Africa. Being a simple review of existing literature, the main limitation of this work is that it does not focus on the application of the proposed technologies, on testing and deployment. But it does provide a starting point for design, implementation and deployment work to establish an appropriate framework for the deployment of IoT systems in the specific case of Africa. Another limitation is that the data used for this study comes solely from Scopus. The inclusion of Web of Science and IEEE Xplore would increase coverage.
During the preparation of this work, the author(s) used X-GPT-4 and Gramby in order to: Grammar and spelling check. Further, the author(s) used X-AI-IMG for figures 3 and 4 in order to: Generate images. After using these tool(s)/service(s), the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content.