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arxiv: 2605.20379 · v1 · pith:AWCYQ7UAnew · submitted 2026-05-19 · 💻 cs.NI

A Meshtastic-based LoRa Mesh System for Smart Campus Applications: From Solar-Powered Sensing to Containerized Data Management

Rafael Garzon Andosilla , Jos\'e de Jes\'us Rugeles Uribe This is my paper

Pith reviewed 2026-05-21 07:27 UTC · model grok-4.3

classification 💻 cs.NI
keywords LoRa meshMeshtasticsmart campusenvironmental monitoringcontainerized servicessolar-powered sensingasset trackingopen-source infrastructure
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The pith

An open-source LoRa mesh using Meshtastic and containerized services creates an autonomous monitoring system for smart campuses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to establish that a mesh network based on the Meshtastic protocol can handle environmental sensing and asset tracking on a university campus. It describes solar-powered nodes built around a Raspberry Pi Pico and Semtech transceiver, mobile trackers, and a Raspberry Pi 4 gateway that runs a Docker Compose stack with Node-RED for ingestion, InfluxDB for storage, and Grafana for visualization. Real-world tests at the UMNG Cajicá campus measured link quality with RSSI and SNR values and reported stable packet delivery even on a 2.47 km link. A reader would care because the setup shows how freely available tools can replace dependence on commercial LoRaWAN operators for campus-scale data collection.

Core claim

The authors show that their Meshtastic-based LoRa mesh integrated with a Docker Compose microservices stack for data ingestion, time-series storage, and real-time dashboards delivers robust connectivity for smart campus applications, as demonstrated by consistent packet reception across campus sites including an extended link of approximately 2.47 km with mean RSSI of -110 dBm and mean SNR of +2.75 dB.

What carries the argument

The Meshtastic protocol running on LoRa hardware together with a containerized microservices architecture that manages ingestion, storage, and visualization.

If this is right

  • Campus operators gain an independent way to collect environmental data without relying on commercial network providers.
  • Mobile asset trackers integrate into the same data pipeline as fixed sensors.
  • The containerized backend allows straightforward replication and scaling of the monitoring system at other sites.
  • Heterogeneous hardware nodes can feed into one unified visualization layer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mesh-plus-container pattern could support monitoring tasks beyond campuses, such as distributed environmental sensing in rural areas.
  • Long-term archives in the time-series database open the possibility of analyzing trends in campus conditions over months or years.
  • Adding more nodes would require checking whether mesh routing still holds under higher traffic loads.

Load-bearing premise

The assumption that signal conditions and obstacles at this one campus layout are representative enough for the results to apply to other smart campus environments.

What would settle it

A repeat deployment of the same nodes and software stack on a different campus that records frequent packet loss or broken mesh links over comparable distances would falsify the claim of reliable performance.

Figures

Figures reproduced from arXiv: 2605.20379 by Jos\'e de Jes\'us Rugeles Uribe, Rafael Garzon Andosilla.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System block diagram of the low-power Meshtastic node (Node 1) based on the RP2040 microcontroller and SX1262 transceiver. Its low-power design, based on photovoltaic energy harvesting, removes de￾pendence on grid power and enables permanent outdoor deployment.As shown in the block diagram, the RP2040 interfaces with the SX1262 via a four-wire SPI bus (GP3 for CS, GP10 for SCK, GP11 for MOSI, and GP12 for … view at source ↗
Figure 3
Figure 3. Figure 3: Physical deployment of the Meshtastic mesh on the UMNG Cajicá campus. Orange circles indicate node positions (1–4); the dashed orange path shows the tracker test route; blue dashed lines indicate LoRa mesh links at 915 MHz. The inset detail shows the tracker path within the Node 4 laboratory complex [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Georeferenced packet reception map for the extended range test (Scenario B). Axes show displacement in meters from Node 4 (gateway). Blue circles indicate POSI￾TION_APP frames from the SenseCAP T1000-E tracker. Colored markers indicate RSSI-measured packets from Meshtastic gaho: green (>−90 dBm), orange (−90 to −110 dBm), and red (<−110 dBm). The dashed purple line shows the 2.47 km extended link to Mirado… view at source ↗
read the original abstract

This work presents the design, implementation, and evaluation of a LoRa-based mesh network using the Meshtastic protocol for Smart Campus applications at Universidad Militar Nueva Granada (UMNG). The system integrates heterogeneous hardware nodes including a solar-powered ecological sensing node built around a Raspberry Pi Pico and a Semtech SX1262 transceiver, and mobile trackers based on the Seeed SenseCAP T1000-E managed through a containerized edge gateway running on a Raspberry Pi 4. A Docker Compose microservices stack handles data ingestion via Node-RED, time-series storage in InfluxDB, and real-time visualization through Grafana dashboards. The architecture's performance was evaluated under realistic propagation scenarios at the UMNG Cajic\'a campus, characterizing link quality using Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) metrics. Experimental results demonstrate robust mesh connectivity across key university facilities, including an extended-range link of approximately 2.47 km linking the campus gateway to a remote station at Mirador La Cumbre (n = 62 packets received, mean RSSI = -110 dBm, mean SNR = +2.75 dB). This architecture demonstrates that open-source mesh protocols combined with containerized microservices offer an autonomous, highly reproducible infrastructure for environmental monitoring and asset tracking, supporting the transition toward data-driven "Smart Campus" ecosystems without reliance on centralized commercial LoRaWAN operators.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents the design, implementation, and evaluation of a Meshtastic-based LoRa mesh network for smart campus applications at UMNG Cajicá. It integrates a solar-powered Raspberry Pi Pico sensing node with SX1262 transceiver, Seeed SenseCAP T1000-E mobile trackers, and a containerized Raspberry Pi 4 edge gateway running Docker Compose with Node-RED for data ingestion, InfluxDB for time-series storage, and Grafana for visualization. Field tests report link quality via RSSI and SNR, including a 2.47 km link with n=62 packets, mean RSSI=-110 dBm, and mean SNR=+2.75 dB, claiming this architecture provides an autonomous, reproducible open-source infrastructure for environmental monitoring and asset tracking without commercial LoRaWAN.

Significance. If the results hold, the work supplies a practical, fully open-source and containerized alternative to proprietary LoRaWAN for campus-scale IoT deployments. The direct empirical measurements (specific packet counts and signal metrics from real propagation scenarios) and use of standard tools like Docker and Grafana support reproducibility and accessibility for educational settings.

major comments (2)
  1. [Evaluation / Results] The central claim that the mesh+containerized system delivers reliable infrastructure for environmental monitoring and asset tracking rests on RSSI/SNR metrics alone (e.g., the 2.47 km Mirador link with n=62 packets, mean RSSI=-110 dBm, mean SNR=+2.75 dB). No end-to-end metrics such as packet delivery ratio, latency for mobile nodes, data completeness over time, or solar-node uptime are reported, so observed link quality does not directly confirm functional reliability for the stated use cases.
  2. [Evaluation / Results] The evaluation assumes the specific propagation conditions and obstacles at the UMNG Cajicá campus (including the extended 2.47 km link) are representative of typical smart campus environments, yet provides no statistical analysis, interference handling, or comparison to other sites to support generalizability.
minor comments (2)
  1. [Abstract] The abstract contains the LaTeX escape 'Cajicá' which should be rendered cleanly as 'Cajicá'.
  2. [Figures] System architecture figures would benefit from explicit data-flow arrows between mesh nodes, the containerized gateway, and the visualization layer.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's detailed review and constructive feedback on our manuscript. We address the major comments point by point below, providing clarifications and indicating where revisions will be made to strengthen the paper.

read point-by-point responses

  1. Referee: [Evaluation / Results] The central claim that the mesh+containerized system delivers reliable infrastructure for environmental monitoring and asset tracking rests on RSSI/SNR metrics alone (e.g., the 2.47 km Mirador link with n=62 packets, mean RSSI=-110 dBm, mean SNR=+2.75 dB). No end-to-end metrics such as packet delivery ratio, latency for mobile nodes, data completeness over time, or solar-node uptime are reported, so observed link quality does not directly confirm functional reliability for the stated use cases.

    Authors: We thank the referee for highlighting this aspect. The manuscript's evaluation section focuses on characterizing the physical layer link quality through RSSI and SNR measurements in realistic campus environments, which are standard metrics for assessing LoRa mesh performance and propagation. The reported successful packet receptions, including 62 packets over the extended 2.47 km link, provide evidence of functional connectivity. However, we agree that explicit end-to-end metrics such as packet delivery ratio would provide a more complete picture of reliability. Since these were not collected in the current deployment (which prioritized long-term autonomous sensing), we will revise the manuscript to explicitly discuss the scope of the evaluation, acknowledge this limitation, and suggest directions for future work on comprehensive performance metrics. revision: partial

  2. Referee: [Evaluation / Results] The evaluation assumes the specific propagation conditions and obstacles at the UMNG Cajicá campus (including the extended 2.47 km link) are representative of typical smart campus environments, yet provides no statistical analysis, interference handling, or comparison to other sites to support generalizability.

    Authors: The study is presented as a case study of a smart campus deployment at UMNG Cajicá, which features a mix of open areas, buildings, and varying distances that are common in many university campuses. The open-source nature of the system facilitates replication and adaptation to other sites. We acknowledge the absence of multi-site comparisons or detailed statistical analysis of interference in the current work. We will update the discussion section to better contextualize the generalizability based on the observed scenarios and emphasize the reproducibility aspects. revision: partial

Circularity Check

0 steps flagged

No circularity: results are direct empirical field measurements

full rationale

The paper presents a system design and reports direct RSSI/SNR measurements from campus field tests (e.g., 2.47 km link with n=62 packets). No equations, parameter fitting, predictions, or derivations appear in the provided text. The central claims rest on observed link quality under specific propagation conditions rather than any reduction to prior definitions, self-citations, or fitted inputs. This is a standard empirical engineering report with no load-bearing derivation chain to inspect for circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on off-the-shelf hardware and open-source software without introducing new mathematical models, fitted parameters, or postulated entities; it uses standard engineering assumptions about radio metrics.

axioms (1)
  • domain assumption Radio signal propagation in the campus environment follows patterns measurable by RSSI and SNR under the tested conditions.
    The performance claims rest on these standard metrics being sufficient indicators of link quality for the applications.

pith-pipeline@v0.9.0 · 5802 in / 1597 out tokens · 58330 ms · 2026-05-21T07:27:34.449449+00:00 · methodology

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Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages

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