How much does it cost to develop a Battery-Powered LTE Temperature & Humidity Sensor?
Project Context & System Requirements
The client’s device is an LTE Temperature & Humidity Sensor designed to be battery-powered, typically built around ultra-low-power MCUs such as STM32, nRF, or ESP32, with a target lifespan of up to one year without recharging. It will periodically (approx. once per hour) transmit temperature and humidity data and operate autonomously in indoor environments such as educational or public sector facilities. The broader aim is to transition toward a scalable, low-maintenance IoT sensing solution.
Current Key Pain Points Identified
These challenges underscored the need for a fully integrated solution – one that not only addresses hardware and firmware limitations but also empowers the client with greater design flexibility, documentation clarity, and long-term scalability.
To meet these needs, WizzDev delivered a custom-designed LTE Temperature Humidity & Sensor, featuring a tailored hardware and firmware stack optimized for low power consumption, robust LTE connectivity, and seamless integration with cloud infrastructure.
Hardware Stack
Firmware Stack
Solution Overview
Additionally, WizzDev will support cloud-side integration with either existing infrastructure or a new custom solution. This will enable seamless provisioning, real-time data visualization, remote device management, and long-term scalability. The solution will also address technical and regulatory needs such as antenna tuning, FCC/CE certification support, and BOM optimization to reduce unit cost and improve supply chain resilience. The solution is designed to integrate with leading cloud platforms such as AWS IoT Cloud, enabling robust and scalable data pipelines.
Architecture Overview
Sensors – Accurate temperature and humidity sensors integrated via UART, I2C, or RS485. Sensor selection is optimized for reliability, cost, and low power consumption.
Control – Handles sensor polling, basic signal filtering, and time-based triggers. Designed with predictable power profiles and a deterministic event model.
MCU + LTE Modem – Ultra-low-power MCU with LTE modem (e.g., nRF, ESP32, or STM32) selected based on regional network compatibility and certification readiness. Runs WizzDev’s modular firmware stack with MQTT communication, OTA update support, and robust error recovery mechanisms.
Custom Logic Module (e.g., Occupancy Engine) – Represents domain-specific logic (e.g., rule evaluation, ML model inference, event handling). Built to be modular and replaceable depending on use case. Compatible with popular embedded frameworks and MCU families (e.g., nRF, ESP32, STM32), ensuring efficient implementation of logic engines.
Device Provisioning – Secure and scalable provisioning pipeline. Supports credential injection, device grouping, and seamless onboarding — integrated with Cloud IoT Core or client-specified platform.
Web API – Exposes REST endpoints for frontend apps, third-party services, or automation scripts. Designed for long-term maintainability with clear API versioning and documentation.
Web Services – Application logic layer for data processing, command handling, and OTA coordination. Deployed using containerized infrastructure or serverless (e.g., AWS Lambda) depending on project size and SLA.
Data Storage – Time-series and metadata storage optimized for retrieval, aggregation, and dashboard rendering. Architecture is cloud-agnostic but cloud-native by default.
Database – Stores device registry, configuration data, user accounts, and permissions. Supports integrations with analytics or alerting tools.
Admin Web App – Internal dashboard for managing devices, monitoring sensor status, and running diagnostics. Secure access with role-based permissions.
WebApp – Client-facing interface for data visualization. Built for responsiveness and extensibility, with support for real-time updates and historical views.
User – Interacts with the system via browser-based apps over REST API.
Project Duration Estimate
| Stage | Description | Min h | Max h |
|---|---|---|---|
|
Feasibility & Architecture |
Choose LTE-capable MCU and modems, finalize system specs;
Calculation of battery life vs sampling/transmission periods and battery size. Emphasis on the reliability (QoS). BOM estimates. |
30 |
30 |
|
Proof of Concept |
Basic firmware for sensor + LTE, devboard setup, test MQTT connection. |
60 |
90 |
|
MVP – Hardware |
Custom PCB with MCU, LTE modem, sensors. |
80 |
150 |
|
MVP – Firmware |
Full sensor logic, LTE communication with cloud, OTA-ready codebase, power management features. |
140 |
190 |
|
Cloud Integration MVP |
Cloud IoT Core platform (e.g., AWS IoT Cloud), device provisioning, data dashboard, OTA mechanism setup. |
80 |
120 |
|
Firmware Optimisation |
Battery life measurements and optimizations, especially for LTE communication and sensors. |
30 |
50 |
|
Alpha Device |
First prototype, testing, design improvements based on clients feedback. |
80 |
120 |
|
Beta & Pilot |
Real-world testing, bugfixes, signal tuning. |
60 |
90 |
|
Final Production |
Manufacturing-ready PCB, certification preparation (FCC/CE), further bugfixes and improvements based on clients feedback. |
80 |
120 |
|
Total time |
|
640 |
980 |
|
QA |
Quality Assurance (optional, recommended). |
56 |
96 |
|
PM |
Project Management. |
56 |
96 |
|
Total time with QA&PM |
|
752 |
1170 |
Risk Assessment
| Risk | Description | Min h | Max h |
|---|---|---|---|
|
LTE Certification/Modem Incompatibility
[Medium risk] |
Using uncertified LTE modems may require additional certification or rework. Also there may be unexpected issues with modem compatibility with specific network. |
0 |
80 |
|
Antenna/SNR Performance Issues [Low risk] |
Field signal issues may require redesign or antenna tuning. |
0 |
60 |
|
Battery Life Underperformance
[Low risk] |
If battery performance is insufficient, firmware and hardware tuning may be needed to meet power goals. |
0 |
80 |
|
OTA Update Strategy & Cost Optimization
[Low risk] |
OTA update infrastructure may involve additional costs, especially when relying on large-scale cloud platforms. Optimizing update frequency, signing, and delivery may add DevOps time. |
0 |
60 |
|
Component Shortage or Redesign [Low risk] |
Unavailable or EOL components can require redesign and retesting of hardware and firmware. |
0 |
80 |
|
MQTT Broker & Cloud Integration Gaps [Low risk with custom cloud, Medium risk otherwise] |
Existing cloud backend may need adjustments especially when integrating with platforms like AWS IoT Cloud, Azure IoT Hub, or custom MQTT brokers. |
0 |
60 |
|
Ambiguity in Bill of Materials (BoM) [Low risk] |
If no clear unit cost target or preferred components are given, multiple iterations may be needed to hit market expectations. |
0 |
90 |
