SRK 2026

POVZETEK
Določanje natančne lokacije radijskih naprav običajno temelji na oceni razdalje med napravami. Pri cenovno ugodnih napravah, kakršne se uporabljajo v internetu stvari, se razdalja najpogosteje ocenjuje na podlagi nivoja sprejetega radijskega signala (RSSI) in modela izgube na poti. Zaradi nezanesljivosti modelov izgube na poti, prisotnosti šuma in interference ter diskretnega koraka merjenja sprejete moči je natančnost ocenjene razdalje omejena, kar vodi v nizko točnost lokacije. V prispevku predstavljamo integracijo metode merjenja razdalje na osnovi faze sprejetega signala v naprave, zasnovane po standardu IEEE 802.15.4, ter v protokol preskakovanja kanalov s časovnimi režami (TSCH), ki omogoča oceno razdalje ob vsakem poslanem paketu. Z uporabo tehnik stisnjenega vzorčenja smo bistveno zmanjšali število potrebnih meritev, s čimer smo skrajšali čas merjenja ter zmanjšali porabo radijskih virov in energije. Predlagane metode smo implementirali na cenovno dostopnih IoT platformah in njihovo delovanje ovrednotili z obsežnimi meritvami v realnih radijskih okoljih.

ABSTRACT
Accurate location of radio devices typically relies on estimating the distance between nodes. In low-cost devices commonly used in Internet of Things (IoT) applications, distance is usually inferred from the received signal strength indicator (RSSI) and a path-loss model. Due to unreliable path-loss models, noise, interference, and the quantization of received power measurements, the accuracy of distance estimation is limited, which in turn leads to poor localization performance. In this paper, we present the integration of a phase-based ranging method into devices compliant with the IEEE 802.15.4 standard and its Time-Slotted Channel Hopping (TSCH) protocol, enabling distance estimation with every transmitted packet. By employing compressed sensing techniques, we significantly reduced the number of required measurements, thereby shortening the measurement time and reducing both radio resource usage and device energy consumption. The proposed methods were implemented on low-cost IoT platforms and evaluated through extensive measurements in real-world radio environments.