Blockchain-enabled Edge Analytics for a Mobile Universal Biotesting Station

Sergii Kushch, Martin Hayes and Eoin O’Connell
September 15, 2020


The impact of COVID-19 on manufacturing, educational, and a varied assortment of locations has been immense. The use case described below proposes a Blockchain-enabled infrastructure for deployment on an edge gateway to secure a universal biotesting station that is capable of processing sensitive personal biodata at scale. The unit can be deployed for access to a health center, manufacturing facility, or a Higher Education laboratory facility.

IoT technologies must be trustworthy so they can enable digital transformation. The goal of a connected system is to provide a better understanding of the physical world, leading to more accurate decision making such as the automation of physical tasks based on historical information and knowledge, or improved outcomes. Distributed ledger technology (DLT) provides a trusted, immutable ledger on which organizations can transmit and store valuable information based on internal operations or interactions with the organization’s environment, including the Industrial Internet of Things (IIoT) devices.

The use case that is presented here is a platform-agnostic approach to the ad-hoc connection of new IoT devices that allows for the secure construction of a holistic, patient-centered digital record. There is a growing requirement that institutions take steps to assure that access is only granted to citizens that pose no threat to the health of others. The Internet of Things (IoT) is a relatively simple concept that seems to be designed for such a public health crisis. IoT uses sensors to collect data in the physical world then transports this data to web-based compute and storage platforms [1]  analyses the biodata using trusted algorithms and is capable of adaptation using machine learning and artificial intelligence techniques [2]. The goal of such a connected access system is to provide a better understanding of the individual who is making the request and requires more accurate decision making that is informed by the use of automation, broad-spectrum testing, synthesis of historical information and knowledge that can yield improved outcomes for all citizens. A requirement is that any proposed IoT solution be secure and accurate. IoT security concerns can be found on all levels of the IoT stack. Trust is always one of the main challenges with any new technology and any change.

At the same time, edge computing is providing the capability to transform the way data is being handled, processed, and delivered from millions of devices around the world – in real-time. The explosive growth of internet-connected devices – the IoT ‘ecosystem’ – along with new applications that require real-time computing power is driving the mass deployment of edge-computing systems. Faster networking technologies, such as 5G wireless, enable edge computing systems that accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, artificial intelligence, and robotics. Edge enabled use cases now exist that are capable of correlating data from multiple input protocols and mediums, undertaking local processing such as data compression, and/or real-time decision making using Artificial Intelligence algorithms [3], providing connectivity to TCP/IP via IP Networks, Wi-Fi or radio access networks (RAN) and interfacing to proprietary networking and industrial connectivity protocols.

The use case here considered is a universal biotesting station that is proposed to enable certifiably safe entry to a secure/important resource. Such a resource can range from a Manufacturing facility where certifiably safe operation is required to a University teaching laboratory where public health guidelines mandate that tracing protocols are in place for safe operation. This use case exhibits the following features: Continuous operation, Minimal Operator/Technician intervention, High testing rates/subject throughput. Implementation of institutional access protocols. Local dynamic determination of rule-based testing procedures depending on real-time diagnostic data, high throughput, minimal outage, latency issues, or WAN connectivity to the supporting Cloud infrastructure. Personal data is stored in a protected, secure fashion where access is strictly controlled using Distributed ledger technology (DLT).

DLT is proposed as a mechanism for the development of a trusted, legally compliant, immutable ledger on wherein organizations can transmit and store valuable information based on internal operations or interactions within an organization’s environment, including the IIoT devices.

Healthcare, Secure Access, Public Health, and DLT

The design of access control mechanisms for healthcare systems is challenging. These mechanisms deal with sensitive data and must guarantee confidentiality within a statutory framework. Specific access control policies must be in place to access a subject’s personal health records (PHRs). Moreover, the PHR’s integrity must be guaranteed and cannot be modified without a clear, immutable ledger entry. evidence. Cohort safety, be that of a patient, student, or classmate must be guaranteed and thus Doctors, Health service professionals, or Higher Education Authorities must be able to access information quickly and without interruptions in case of health care emergency or for contact tracing purposes as part of a public health intervention. Flexible and frictionless access control will always introduce attendant moral hazard problems. Indeed, as reported in [4], 58% of attacks involved insider or ‘Person in the middle’ attacks - insiders are often the biggest threat to an organization. The motives range from simple curiosity about a friend or family member, or students wishing to game the laboratory access system, right through to a malevolent intent to damage a patient by revealing some sensitive data or financial gain (e.g., receiving an insurance payment by using a stolen diagnosis). Thus, access control mechanisms must strike the right balance between throughput, permissions, and restrictions. Given the aforementioned issue with insiders, all the access of a healthcare organization must be tracked through a secure non-repudiation logging system that will identify cases of privilege abuse in such a fashion that it will protect company interests while simultaneously deterring employees from improper behavior.

A biotesting use case requires the ability to dock multiple devices using different communication standards and protocols (i.e., Bluetooth, WiFi, LoRA, and 4G/5G). [5, 6]. However, there are still many open questions that need to be addressed so that any proposed solution is scalable, reliable, and secure for integrated use together (Figure 1).

Figure 1: A distributed Edge deployment.

Figure 1: A distributed Edge deployment.


The design prototype supports multiple physical layer communication protocols received from a variety of medical-grade test-station devices (e.g. basic temperature, heart rate, blood pressure, saliva testing, blood tests, visual cognition, etc.) that are securely labeled for edge-based analysis and network layer training before transmission to a central database. Edge analytics facilitates testing regimes that can evolve dynamically according to clinician requirements but that are also optimized to make the best use of Health Service Resources. The digital record database will be secured using a distributed ledger that is deployed on all testing devices via the edge gateway (Figure 2). Case records will evolve so that deviations can be monitored over time, class lists are checked dynamically so that access protocols are updated and a secure registry is kept in line with the public health tracing regimen.

Figure 2: Prototype Automated Health Testing Station.

Figure 2: Prototype Automated Health Testing Station.


Final Remarks

This project will detail the specifics of system design and implementation, will detail preliminary use case feedback in a busy higher education setting, and will also detail performance and validation information from the new state of the art SFI funded Confirm Smart Manufacturing 5G testbed that is being rolled out at the University of Limerick during Autumn 2020. The authors will provide test results including data throughput to a central cloud-based registry, access decisions based on a significant deviation of subject data from stored biodata, new testing as per clinical need, time-series information being made available to management in realtime with minimal additional impact on lab activity.


  1. J. Chin, V. Callaghan and I. Lam, "Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data," 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, 2017, pp. 2050-2055, doi: 10.1109/ISIE.2017.8001570.
  2. E. O’Connell, D. Moore, and T. Newe, "Challenges Associated with Implementing 5G in Manufacturing," in Telecom, 2020, vol. 1, no. 1, pp. 48-67: Multidisciplinary Digital Publishing Institute.
  3. Vermesan, O. et al., 2017. “Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms.” In: J. B. Ovidiu Vermesan, ed. Cognitive Hyperconnected Digital Transformation : Internet of Things Intelligence Evolution. Gistrup: River Publishers, pp. 97-155
  4. Protected health information data breach report. Verizon (last accessed: September 2020), Available at
  5. S. Kushch and F. Prieto-Castrillo, "Blockchain for Dynamic Nodes in a Smart City," 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 2019, pp. 29-34.
  6. S. Kushch, S. Ranise and G. Sciarretta, "Blockchain Tree for eHealth," 2019 IEEE Global Conference on Internet of Things (GCIoT), Dubai, United Arab Emirates, 2019, pp. 1-5.


Sergii KushchSergii Kushch holds a Ph.D. in Computer Science and a Master degree in Radio Engineering (Hons.) and is a Senior Member of the IEEE. He worked on the development and optimization of microchips structure, human factor assessment in cybersecurity, and on the development of Blockchain technology for use in IoT, WSN, and mobile devices. Currently, Dr. Kushch works as a Senior Research Fellow in the Irish National CONFIRM research centre and the Department of Electronic & Computer Engineering at the University of Limerick, focused on the implementation of new wireless technologies in industrial environments. Additionally, he works with modifications of Blockchain technology for securing and protecting personal data and developing an implementation of Blockchain for WSN and IoT.


Eoin O ConnellEoin O’Connell is a Funded Investigator in the Irish national research centre CONFIRM. He is an academic in the Department of Electronic & Computer Engineering at the University of Limerick. Dr. O’Connell holds a Ph.D. in the wireless integration of fibre optics sensors, he has a Master in Business Administration (MBA) and a Bachelor’s degree in Telecommunications (Hons.) from the University of Limerick. Dr. O’Connell is also a graduate from Limerick Institute of Technology, where he received an NCEA Diploma and an NCEA Certificate in electronic engineering. His research interests are in the areas of Cellular communications, IoT, System Security vulnerabilities, edge processing techniques, Data Management, sensor development, and Wireless Sensor Networks with a particular focus on the corresponding issues of interoperability and scalability.


Martin HayesMartin Hayes is Head of the Electronic & Computer Engineering Department (ECE) at the University of Limerick and is a funded investigator with CONFIRM, the SFI funded institute for Smart Manufacturing Research in Ireland. Previously, Martin acted as a Research Director for the Intelligent Power Management and Control branch of the Circuits and Systems Research Centre within the Department. He is the UL representative on the Irish Signals and Systems subcommittee of the Royal Irish Academy. Martin holds a BE and ME from the University of Limerick and a Ph.D. from Dublin City University. His research interests lie generally in the area of Artificial Intelligence and Systems Theory. Martin’s current funded research work focusses on the intelligent use of system resources within smart manufacturing or educational environments.