REPOSITORY > RESULTS

Abstract

The evolution of wireless networks toward large-scale, heterogeneous Internet of Things
(IoT) systems has created a need for communication infrastructures that not only support
data exchange but also perceive and interpret the surrounding environment. Integrated
Sensing and Communication (ISAC) has emerged as a key paradigm, aiming to unify wireless
communication and radio sensing within a common framework. While ISAC is gaining
attention in high-throughput and complex telecommunication systems, its application in
low-power, constrained IoT networks remains an open research challenge. Limited resources,
stringent energy constraints, and the use of low-cost hardware make the seamless
integration of sensing functions alongside communication particularly demanding. This
dissertation addresses the challenges of constrained IoT protocols by developing methods
to implement accurate and energy-efficient localization and sensing capabilities into the
IEEE 802.15.4 standard and its Time Slotted Channel Hopping (TSCH) protocol.
The first part of the dissertation demonstrates how the Phase-Based Ranging (PBR)
method can be integrated into TSCH communication, enabling distance estimation with
each packet exchange. The use of compressed sampling technique reduces measurement
overhead while maintaining sub-meter accuracy. The second part extends the integration
paradigm to Angle-of-Arrival (AoA) estimation by embedding a phase measurement
process into the TSCH slot, enabling sequential phase sampling across multiple antennas.
Carrier Frequency Offset (CFO) distortions are mitigated with a two-stage calibration
procedure, enabled by a newly developed CFO estimation algorithm. With the developed
channel sampling methods, a hybrid localization framework that combines distance
and angle estimation is proposed. Leveraging the hierarchical topology of TSCH networks,
the designed system enables simultaneous communication and localization with a
single anchor device, without disrupting network operation. The fourth part addresses a
central ISAC challenge: mutual interference between communication and sensing during
concurrent operation in shared spectrum. The proposed one-way channel reconstruction
algorithm improves distance estimation in multipath environments, outperforming existing
algorithms at low Signal-to-Noise Ratio (SNR) conditions.
The developed methods are implemented on low-cost IoT platforms and validated
through extensive experimental campaigns. The results confirm that sub-meter localization
accuracy can be achieved with minimal modifications to the communication protocol.
Open-source software implementations and publicly available measurement datasets support
reproducibility and promote further development of ISAC systems. The provided
resources establish a benchmark for future studies, enabling consistent comparison and
advancement of localization algorithms under realistic scenarios. The dissertation demonstrates
that accurate localization and sensing can be achieved within existing IoT systems,
laying the foundation for a new generation of energy-efficient, self-aware networks that
combine connectivity and localization within a unified ISAC framework.