Eco-WakeLoc: Energy-Neutral Indoor RTLS

• Eco-WakeLoc is an energy-neutral indoor RTLS leveraging UWB, wake-up radios, and solar harvesting to achieve precise centimeter-level localization.
• It employs on-demand anchor activation, hybrid active-passive localization, and an AIMD-based scheduler to efficiently manage energy and scale deployments.
• Experimental evaluations demonstrate sub-meter accuracy, low latency, and maintenance-free operation in GPS-denied environments using advanced cooperative methods.

Eco-WakeLoc is an energy-neutral, cooperative indoor real-time locating system (RTLS) that leverages ultra-wideband (UWB) radios, ultra-low-power wake-up radios (WuRs), and indoor solar energy harvesting to achieve centimeter-level localization accuracy at scale without continuous infrastructure operation. By combining on-demand anchor activation, hybrid active-passive cooperative localization, and an adaptive additive-increase/multiplicative-decrease (AIMD) scheduler, Eco-WakeLoc addresses the power–responsiveness trade-off inherent in traditional RTLS architectures and enables long-lived, maintainable deployments in GPS-denied environments such as mobile robotics, IoT, and asset tracking contexts (Cortesi et al., 6 Jan 2026).

1. System Architecture and Operational Principles

Eco-WakeLoc integrates three core objectives: (1) achieving centimeter-level localization resolution through UWB, (2) ensuring energy-neutral operation of both tags and anchors via solar harvesting and dynamic activity adaptation, and (3) enabling scalability in anchor density and tag population through cooperative and on-demand protocols.

Each node—either a mobile tag or a static anchor—contains a Qorvo DWM3000 UWB transceiver for high-precision ranging, a Fraunhofer FH101RF-based “WakeMod” WuR for sub-10 μW always-on listening, an Anysolar KXOB201K04TF solar cell connected via an e-peas AEM10900 maximum power point tracker (MPPT) to a 35 mAh LiPo battery, and an STM32U535 MCU for protocol and scheduling control. In deep sleep, each node’s average current consumption is 7.84 μW, supporting multi-year maintenance-free operation under typical indoor illumination (Cortesi et al., 6 Jan 2026).

The system establishes a WuR-based on-demand wake-up protocol: when localization is needed, tags issue wake-up calls (WuCs) over 868 MHz, which anchors detect before activating their higher-power UWB radios. UWB ranging then proceeds using a nested double-sided single-sided two-way ranging (CC-SS-TWR) exchange, where each anchor responds after a deterministic delay:

$$\Delta T_{A_i} = \Delta T_{fix} + \Delta T \cdot [(i-1) \mod \widehat N_A]$$

This interleaving avoids response collisions and restricts active energy consumption to only those anchors required for multilateration (Cortesi et al., 6 Jan 2026).

2. Cooperative Localization Algorithms

Eco-WakeLoc supports both active and passive tag operation modes, enabling energy savings and scalability. Active tags initiate localizations by waking nearby anchors, then perform nested CC-SS-TWR exchanges to derive round-trip times to each anchor, reconstruct distances $d_i$ to anchor positions $(x_i, y_i, z_i)$, and solve for tag coordinates $x$ using non-linear least-squares trilateration (e.g., Levenberg–Marquardt algorithm): $$\min_x \|f(x)\|_2^2; \quad f_i(x) = \|x - x_i\| - d_i$$ Passive tags opportunistically eavesdrop on the UWB exchanges between active tags and anchors, extracting the timestamp differences $\Delta t_{ij}=t_i - t_j$ from anchor messages and using these for hyperbolic TDOA localization: $$\min_x \sum_{i>j} \left(\|x - x_i\| - \|x - x_j\| - c \Delta t_{ij}\right)^2$$ This approach allows passive tags to localize at significantly reduced energy, imposing zero additional anchor transmission overhead (Cortesi et al., 6 Jan 2026).

3. Energy-Aware AIMD Scheduling

The AIMD-based energy scheduler dynamically controls the number of allowed localizations per hour ($r_{n+1}$) to balance harvested and consumed energy. The algorithm uses a battery state metric

$$m = B \cdot (b[t] - b[t-1]) - \left(\frac{1}{b[t]} - 1\right)$$

and two thresholds ($\beta_1, \beta_2$) with a high state-of-charge (SoC) override ($\gamma$) for adaptive rate control:

$$r_{n+1} = \begin{cases} r_n + 1, & m \geq \beta_2 \,\lor\, b[t] \geq \gamma \ \beta\,r_n, & m \leq \beta_1 \ r_n, & \text{otherwise} \end{cases}$$

With typical values $\alpha=1$, $\beta=1/2$, this ensures that over time, average energy expenditure cannot exceed the average harvested power, enabling long-term energy-neutral operation even under variable illumination conditions (Cortesi et al., 6 Jan 2026).

4. Hardware Implementation and Power Profiling

All components are implemented on custom PCBs with STM32U535 MCUs, Qorvo DWM3000 UWB modules, “WakeMod” WuRs (6.9 μW listening), Anysolar solar cells, and AEM10900 MPPT units. Power is distributed via high-efficiency buck/boost converters.

Profiling with high-frequency instrumentation reveals:

• Sleep (WuR-only): 7.84 μW
• Active localization: 3.22 mJ over 84.52 ms for tags (38 mW avg.), 951 μJ over 34.18 ms for passive tags (28 mW avg.)
• Anchor per-localization energy: ~$353\,\mu$J

This sharply contrasts prior always-on RTLS schemes, as WuR-enabled deep sleep and on-demand only UWB operation ensure energy neutrality without loss of accuracy or responsiveness (Cortesi et al., 6 Jan 2026, Cortesi et al., 29 Apr 2025).

5. Experimental Evaluation

Extensive validation includes thousands of UWB measurements in controlled environments and real-world deployments:

• In-room trilateration (five anchors, 8.6 × 7.6 m): Active tag average error 21.89 cm; passive tag TDOA error 25.70 cm.
• On-robot operation (Unitree A1 quadruped, 91 m², nine anchors): Active tag error 0.43 m (median), passive tag median error ≈ 0.45 m; both using LM and Larsson solvers.
• Typical localization latency is ≈ 84 ms (maximum 11.8 Hz update rate).
• Computational cost on STM32U535: LM multilateration at 1.86 ms (267 k cycles), TDOA at 2.17 ms (312 k cycles).
• Year-long building-scale simulations (6,378 m², 89 anchors, up to 100 tags with variable illumination): Tags achieve up to 2,031 localizations/day (active+passive), with > 7% remaining battery SoC, using the AIMD scheduler; a constant-rate scheduler would allow only 180 localizations/day under equivalent energy constraints (Cortesi et al., 6 Jan 2026).

6. Scalability, Energy, and Cooperative Operation

Eco-WakeLoc’s architectural strategy—on-demand anchor activation, opportunistic passive localization, and AIMD rate adaptation—decouples system scalability from continuous energy expenditure:

• Only awakened anchors consume UWB active power, and passive localizations do not require additional anchor responses.
• The cooperative protocol enables dense tag populations to localize concurrently, leveraging message reuse to amortize channel and scheduling overheads.
• Responsiveness is maintained as localization latency is independent of node duty cycles (Cortesi et al., 6 Jan 2026).

A direct design evolution from WakeLoc (Cortesi et al., 29 Apr 2025), Eco-WakeLoc introduces solar harvesting and refined energy-adaptive scheduling, supporting perpetual operation in ambient-lit indoor settings. Key innovations over WakeLoc include a sub-GHz WuR trigger compatible with lower-power WuC transmission, energy-aware anchor selection, and run-time UWB parameter adaptation.

7. Implications and Comparative Perspective

Eco-WakeLoc demonstrates that RTLS deployments can be both high-accuracy and energy-neutral without major sacrifices in responsiveness or scalability. The architecture is particularly suitable for environments where maintenance-free operation, such as battery replacements, is impractical (e.g., industrial IoT, mobile robotics in logistics, or smart asset tracking). By combining aggressive power gating, cooperative reuse of localization exchanges, and dynamic AIMD control, Eco-WakeLoc achieves an energy efficiency, scalability, and maintainability regime not previously attained in RTLS design (Cortesi et al., 6 Jan 2026, Cortesi et al., 29 Apr 2025).

A plausible implication is that the architectural model embraced by Eco-WakeLoc—dynamic on-demand operation, system-level energy harvesting, and cooperative sensing—may inform future infrastructure-less and sustainable RTLS deployments across diverse domains, including extraterrestrial environments, as originally anticipated by the WakeLoc project.