GPS Tracking Network Technologies are essential for efficient real-time tracking. But what often gets overlooked is the communication network that makes real-time location tracking, alerts, and data transmission possible. Technologies such as 2G, 3G, 4G, 5G, and LPWAN are critical in extending the functionality of GPS/GNSS devices beyond simple location data.

This article explores the role of each network generation and low-power alternatives in the world of GPS tracking, giving you a better understanding of which one is right for your application.

1. Understanding the Differences Between Mobile Networks (2G/3G/4G/5G/LPWAN)

Each mobile communication generation brings unique capabilities that directly influence the performance and cost of GPS tracking solutions.

2G: The Digital Pioneer

• Speed: 50 Kbps – 1 Mbps
• Technology: GSM, CDMA
• Use Case: Basic tracking and SMS-based alerts
• Drawback: Limited data speed, gradually being phased out

As the first digital mobile communication standard, 2G marked a big leap from analog (1G) by enabling SMS and low-data applications. Many early GPS trackers leveraged 2G for basic location updates and text-based commands. Though still used in some regions, its phase-out is accelerating globally.

3G: A Step Toward Broadband Mobility

• Speed: Up to 7 Mbps (typical usage)
• Technology: UMTS, HSPA
• Use Case: Real-time tracking, map-based visualization
• Drawback: High infrastructure cost and declining global support

3G networks introduced high-speed mobile internet and allowed GPS trackers to transmit more data more frequently. However, their high operating cost and infrastructure demands led many providers to begin phasing them out in favor of more efficient networks.

4G LTE: The Game Changer for Telematics

• Speed: 100 Mbps – 1 Gbps
• Technology: LTE (Long Term Evolution)
• Use Case: Video telematics, driver behavior monitoring, high-volume data transmission
• Drawback: Higher battery and data consumption

4G revolutionized GPS tracking. Its high speed and low latency enabled applications like real-time video streaming, driver analytics, and cloud-based dashboards. Devices like dual-channel dash cams and advanced fleet trackers depend on 4G to function effectively.

LPWAN: The Backbone of IoT Tracking

Low-Power Wide-Area Networks (LPWANs) are tailored for low-data, long-range applications with excellent energy efficiency.

Licensed LPWAN (Cellular-based like LTE-M and NB-IoT)

• Pros: Secure, reliable, supports mobility
• Cons: Slightly higher power consumption due to communication protocol overhead

Unlicensed LPWAN (e.g., Sigfox, LoRa)

• Pros: Long range, low power
• Cons: Low data rate, prone to interference, best for static or low-frequency updates

Spread Spectrum and Telegram Splitting

These advanced signal processing techniques enhance range, reduce interference, and improve scalability. They’re often used in industrial LPWAN deployments for asset tracking in remote areas.

LTE-M (CAT-M1): Optimized for Mobile IoT

• Bandwidth: 1.4 MHz
• Speed: 100–300 Kbps (peak ~1 Mbps)
• Latency: 10–15 ms
• Best Use: Mobile asset tracking, remote diagnostics
• Bonus: Supports voice (VoLTE) and mobility

LTE-M balances power consumption, mobility, and data speed, making it ideal for applications like fleet tracking, rental car monitoring, and logistics management.

NB-IoT: Power-Efficient Static Monitoring

• Bandwidth: 200 KHz
• Speed: Up to 250 Kbps
• Latency: 1.6–10 seconds
• Best Use: Stationary assets, environmental monitoring
• Drawback: No roaming support, not suited for fast-moving vehicles

NB-IoT excels in use cases requiring minimal power and sporadic updates—such as pipeline monitoring, smart meters, and cold chain sensors.

5G: The Future of Connected Mobility

• Speed: Up to 35.46 Gbps (theoretical)
• Latency: As low as 1 ms
• Use Case: Smart cities, autonomous driving, real-time video tracking
• Limitation: Limited coverage, poor penetration, high infrastructure needs

5G promises unmatched speed and ultra-low latency, ideal for next-gen telematics, autonomous vehicles, and AI-enabled video analytics. However, due to limited coverage and high costs, it’s not yet viable for most tracking use cases outside urban centers.