1. Project Period
July 2026 – December 2026
2. Key Problems to Be Solved
The key problems addressed in this project relate to the need for a scalable, low-cost, and operationally efficient approach for identifying and monitoring tagged assets and devices. Existing solutions often rely on manual workflows, barcode-based systems, or battery-powered tags, which can be difficult to scale and costly to maintain. In addition, many current platforms provide limited real-time visibility and do not effectively convert collected data into actionable operational insights. Another important challenge is the secure migration of such systems to the cloud, particularly when enabling external API access, which requires strong mechanisms for authentication, authorisation, encryption, access control, and monitoring to ensure secure and reliable real-world deployment.
3. Overall Research Content and Objectives
Ambient Internet of Things (A-IoT) is emerging as an important technology for enabling massive, low-cost, and sustainable connectivity in future wireless networks. Unlike conventional IoT devices that depend on batteries and require periodic maintenance, A-IoT devices can harvest energy from ambient sources and communicate using low-power techniques such as backscatter communication. This makes them suitable for large-scale sensing and tracking applications in areas such as smart cities, logistics, agriculture, and industrial monitoring. The growing interest from 3GPP and recent 6G-oriented studies further highlights the potential of A-IoT to support dense, energy-efficient, and maintenance-free IoT deployments [1-3].
Ambient IoT deployment presents several practical challenges [4,5] because the devices are expected to operate with very low power, limited processing capability, and minimal synchronization support. At the same time, large-scale deployment requires low-cost integration with existing cellular infrastructure. One key challenge is therefore how Ambient IoT services can be supported using current 5G base stations without requiring major hardware changes [4]. This creates an important research problem on how existing 5G physical-layer components can be reused for Ambient IoT while still satisfying the strict power and complexity constraints of A-IoT devices. In particular, integrating A-IoT with 5G infrastructure creates both opportunities and technical challenges. Network operators prefer to reuse existing 5G base stations to reduce deployment cost and enable faster rollout of A-IoT services. Therefore, it is important to study how existing infrastructure can support A-IoT communication. A further problem is that many A-IoT demonstrations are still limited to controlled pilots, short-term trials, or isolated prototypes [6]. Although these studies show the feasibility of A-IoT, they do not fully validate its reliability as a scalable and dependable component of future 6G networks. Research must therefore move toward reproducible, long-duration, and multi-site evaluations that can benchmark technologies such as beamforming, wireless power transfer, ambient backscatter communication, and hybrid energy harvesting under realistic conditions. Existing platforms provide useful foundations, but the lack of unified benchmarking makes it difficult to compare solutions fairly.
This project focuses on the design and development of an Ambient IoT platform to support localization, system demonstration, and proof-of-concept development for industry-relevant use cases. Ambient IoT is an emerging capability in 5G-Advanced and is expected to play an important role in future 6G ecosystems. It offers a scalable and cost-effective approach for identifying, sensing, and monitoring tagged items, assets, and devices across operational environments. The project is motivated by the limitations of current tracking and monitoring approaches, which often depend on manual record keeping, barcode-based workflows, or battery-powered tags. These methods may be labour-intensive, difficult to scale across large deployments, and costly to maintain over time. In contrast, Ambient IoT offers the potential for more seamless, low-power, and operationally efficient monitoring of assets and devices. The research will cover the development of a platform capable of collecting and processing identification data from Ambient IoT devices, supporting localization functions, and enabling real-time visibility of system activity. The platform will also be studied from a deployment perspective, including the secure migration of a locally hosted system to a cloud-based environment. This includes enabling API access for authorised external applications over the internet while ensuring that the platform remains secure, reliable, and suitable for real-world deployment. To support this, the project will investigate cybersecurity mechanisms such as secure API design, authentication, authorisation, encrypted communication, access control, logging, and monitoring.
The research objectives are as follows:
(1)To design and develop an Ambient IoT platform for identification, localization, and monitoring of tagged assets, items, and devices;
(2)To develop a dashboard for real-time visualisation of identification data, device activity, and system status, with support for operational insights;
(3)To evaluate the use of Ambient IoT for scalable and low-cost proof-of-concept solutions aligned with industry-relevant applications;
(4)To design and develop a prototype algorithm and validation report for confidence-aware trajectory tracking in Ambient IoT asset
4. Expected Outcomes and Value
The expected outcomes of this project include the development of a functional Ambient IoT platform capable of identifying, localising, and monitoring tagged assets and devices in real time. The project is also expected to deliver a dashboard that provides clear visualisation of identification data, device activity, system health, and operational insights for end users.
Another key outcome is a prototype algorithm for confidence-aware trajectory tracking in Ambient IoT asset, it can integrate other technical approaches such as vision to achieve precise tracking of target trajectories.
