Research & Development
Real-Time Location Systems
Architecture · Integration · Reliability
Modern tracking solutions — whether deployed in hospitals, logistics hubs, or industrial facilities — rely on a complex interplay of hardware, wireless protocols, edge computing, and cloud-based analytics. While the promise of real-time asset visibility is well established, the engineering reality of delivering consistent, production-grade systems remains a significant challenge. Our R&D programme addresses these challenges methodically, combining empirical field research with software prototyping and cross-vendor hardware evaluation.
Core Research Areas
Efficient system implementation
We investigate deployment architectures that minimise infrastructure cost without sacrificing accuracy — including optimal access point placement, coverage modelling, and edge-side pre-processing to reduce cloud bandwidth and latency.
Cross-vendor hardware interoperability
Most enterprise-grade BLE and RTLS vendors operate within closed ecosystems. We develop abstraction and translation layers that allow devices from multiple manufacturers to coexist within a single unified tracking platform.
Signal reliability in noisy environments
Real-world deployments contend with RF interference, multipath propagation, metallic surfaces, and physical obstacles. We research filtering algorithms, sensor fusion techniques, and adaptive calibration methods to maintain accuracy under these conditions.
Indoor-outdoor transition continuity
Seamless handoff between BLE, Wi‑Fi, GPS, and cellular technologies remains unsolved at scale. Our research focuses on hybrid positioning models that maintain continuity across environment boundaries without data loss or accuracy degradation.
Industry Challenges We Address
The following represent the most significant open problems in the tracking solutions industry today. For each challenge, we outline the nature of the problem and the type of R&D activity we pursue in response.
Challenge 1 — Data overload vs. actionable insight
Raw location streams are inherently noisy: BLE RSSI fluctuates, indoor GPS signals reflect off surfaces, and high-frequency updates generate volumes of data that are difficult to interpret operationally. The gap between knowing where a device is and knowing what to do about it remains wide.
Challenge 2 — Integration with legacy enterprise systems
The majority of hospitals and industrial operators run core business logic on ERP, CMMS, or EHR platforms designed long before real-time IoT data existed. Bridging live, high-frequency location streams with static, batch-oriented legacy databases introduces synchronisation, schema, and latency conflicts that cannot be solved by off-the-shelf middleware alone.
Challenge 3 — Seamless indoor-outdoor tracking transition
When a tagged asset moves from a GPS-covered outdoor area into a BLE-covered indoor environment — or vice versa — the handoff frequently results in a position jump, a gap in the timeline, or outright loss of the asset from the map. This is a particularly acute problem in hospital logistics, where assets regularly cross loading docks, car parks, and building entrances.
Challenge 4 — Scalability and fleet management at enterprise scale
A single hospital may manage tens of thousands of tagged assets concurrently. As fleet size grows, the computational cost of real-time positioning, the network load from tag beaconing, and the complexity of access permission management all scale non-linearly, creating hard engineering limits that few off-the-shelf platforms have adequately addressed.
Challenge 5 — Security, privacy, and regulatory compliance
Tracking systems deployed in healthcare environments must comply with data protection regulations (GDPR, HIPAA) while also securing the communication channel between tags, access points, and cloud backends. Location data, if linked to individuals, becomes personal data with full regulatory implications.
Our Approach
Our R&D work is grounded in real deployments rather than controlled laboratory conditions. We validate our findings against live environments — including active hospital wards — where equipment movement, human presence, and competing RF signals create conditions that synthetic benchmarks cannot replicate. This field-first methodology ensures that the architectures and algorithms we develop translate directly into production systems.
We maintain a vendor-agnostic stance throughout our research, evaluating hardware from multiple BLE and RTLS manufacturers and publishing comparative performance data to inform deployment decisions. Our goal is not to advocate for a single technology stack, but to build the engineering knowledge and software infrastructure that make any compliant stack perform reliably.