Ambulance Telemetry System

Bulawayo, Zimbabwe

Abstract—System-level telemetry is fundamental to modern remote monitoring, predictive maintenance, and AI-driven in- frastructure optimisation. Existing telemetry encodings such as JSON, JSON Lines, CBOR, and Protocol Buffers were designed for high-bandwidth, always-online environments. They impose significant overhead when deployed in bandwidth-constrained networks common across Sub-Saharan Africa, rural enter- prise deployments, and unstable LAN environments.

This paper introduces MTS-1 (Magenta Telemetry Standard v1), a novel delta-encoded binary telemetry format designed for offline-first system monitoring, LAN-assisted proxy delivery, and energy-efficient IoT-to-server transmission. We compare MTS-1 against JSON, JSON Lines, CBOR, MessagePack, and Protocol Buffers across payload size, encoding cost, network efficiency, and cost–latency performance. Synthetic benchmarking demon- strates preliminary compression improvements of up to 74.7% versus JSON and 5.4% versus MessagePack, with linear scaling characteristics across dataset sizes.

Index Terms—Telemetry encoding, delta compression, offline- first systems, IoT monitoring, bandwidth optimisation, predictive

I. Introduction

Remote device monitoring systems depend on continuous transmission of CPU load, temperature, memory, disk status, and network throughput. However, prevailing telemetry formats assume high availability, high throughput networking . This assumption breaks down in real-world African SME

Deployments Where:

• Internet connectivity is intermittent and unreliable • Cost per megabyte of cellular data is economically sig-

Nificant

• Sites operate fully offline within isolated local area net-

Works

• Energy budgets are limited during frequent power inter-

Ruptions

Magenta is a monitoring platform designed specifically for such resource-constrained realities. Its core software compo- nent, the Magenta Agent, collects and transmits system metrics under these challenging conditions. To support large-scale deployment across low-resource contexts, a new telemetry encoding format is required that explicitly addresses band- width constraints, intermittent connectivity, and computational efficiency.

This Work Makes The Following Contributions:

1) Design and implementation of MTS-1, a delta-encoded

Constrained Environments

2) Comprehensive empirical evaluation across three dataset

Against Five Existing Serialization Formats

3) Mathematical cost modeling demonstrating bandwidth savings of up to $1,056 annually per 1,000 devices 4) Analysis of AI-readiness for predictive maintenance

A. Existing Serialization Formats

Existing serialization formats include JSON, JSON Lines

(Ndjson), Cbor , Messagepack, Apache Avro, Cap’N

Proto, and Protocol Buffers. While these formats address efficiency concerns in various contexts, none explicitly tar- get the combination of intermittent connectivity, bandwidth constraints, and LAN-mediated proxy delivery common in resource-limited deployments .

JSON and JSON Lines provide human readability at the cost of significant size overhead due to verbose field names and lack of binary encoding . CBOR (Concise Binary Object Representation) offers binary efficiency but lacks delta encod- ing . MessagePack achieves strong compression but does not optimise for time-series telemetry patterns . Protocol Buffers require schema definition and compilation, adding deployment complexity unsuitable for dynamic monitoring scenarios .

B. Proprietary Telemetry Systems

Systems such as Apple Diagnostics and Google Pixel De- vice Health employ proprietary telemetry encoding optimised

Arxiv:2601.01602V1 [Cs.Se] 4 Jan 2026

for mobile device health monitoring; however, no open re- search documentation describing these formats exists publicly from the vendors themselves . This lack of transparency limits reproducibility and comparative analysis.

C. Predictive Maintenance Research

Predictive hardware failure research largely focuses on SMART disk metrics and CPU thermal decay patterns , yet no literature unifies encoding efficiency with hardware- level AI prediction capabilities specifically designed for low- resource environments. This gap motivates the design of MTS-1 as both an efficient encoding and an AI-ready data representation.

Iii. Problem Definition

Given a telemetry sequence of system health observations S = {x1, x2, . . . , xn} where xi is a structured record of CPU, thermal, memory, disk, and network parameters collected at regular intervals, the transmission cost is defined as:

• B = Bytes Per Payload

• F = transmission frequency (transmissions per time unit)

• P = Network Cost Per Megabyte

The objective of MTS-1 is to minimize this cost while

Where X′

i is the reconstructed telemetry at the backend and θ is the minimum acceptable accuracy threshold.

A. Core Design Principles

MTS-1 is engineered around four fundamental constraints that directly address the challenges of low-resource deploy-

Ment Environments:

1) Minimal Payload Weight: Binary float packing with

Delta Encoding

2) Delta-Based Transmission: Only changed values ex-

Ceeding Threshold Ε Are Transmitted

3) Offline Queue Support: Local buffering capability for

Disconnected Operation

4) AI-Ready Representation: Native delta format elimi-

B. Format Structure

Each MTS-1 record is represented as a delta vector:

(3)

where components represent changes in CPU load, temper- ature, memory pressure, disk occupation, network sent, and network received respectively.

Values transmit only when changes exceed a configurable

(4)

This threshold mechanism dramatically reduces transmis- sion volume for stable systems while preserving all significant state changes.

C. Network Topology Support

MTS-1 supports distributed transmission through a forward- ing graph G = (V, E) where (vi, vj) ∈E implies node vj forwards telemetry for node vi. This enables LAN-based proxy architectures where edge devices with intermittent internet connectivity can relay through local gateways.

A. Dataset Generation

Three synthetic telemetry corpora were generated using real Magenta system-level metric samples, scaled to represent realistic fleet deployment scenarios. The datasets are defined

• S3 (Tele50K): 50,000 Telemetry Payloads

Each dataset contains comprehensive system observations including CPU load percentage, CPU frequency, core tempera- ture, memory pressure percentage, disk occupation percentage, network transmission state, and system uptime. Metrics were sampled at approximately 30-second intervals to simulate continuous monitoring conditions.

B. Encoding Process

For each dataset, encodings were produced in six formats: JSON, JSON Lines (NDJSON), CBOR, MessagePack, MTS-1 (binary), and MTS-1+LZ4 (with compression). Encoding size was measured on-disk after serialization without additional compression (except for the explicitly compressed variant). All encodings preserved identical semantic information to ensure fair comparison.

C. Evaluation Metrics

Performance was evaluated across multiple dimensions:

• Absolute Size: Raw Byte Count After Encoding

• Relative Reduction: Percentage decrease compared to

Json Baseline

• Scaling Behaviour: Growth rate across dataset sizes • Cost Modeling: Real-world bandwidth cost implications

A. Raw Storage Cost Analysis

Table I presents the absolute byte sizes across all tested formats and datasets. MTS-1 consistently achieves the smallest payload size across all three dataset scales, with MTS-1+LZ4 showing minimal additional benefit due to the already-compact binary representation.

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