Sub-GHz vs 2.4 GHz ISM Band: 5 Implications for Your IoT Deployment

sub-GHz vs. 2.4 GHz

BehrTech Blog

Sub-GHz vs 2.4 GHz ISM Band: 5 Implications for Your IoT Deployment

When assessing different wireless solutions for your IoT deployment, you may be surprised to discover that most technologies are adopting license-free Industrial, Scientific and Medical (ISM) frequency bands for their operations. Without the complex licensing processes and hefty fees of the licensed bands, the license-free spectrum has been a preferred choice for many developers. While there are various ISM bands available today, the decision often boils down to sub-GHz vs 2.4 GHz frequencies.

If you think the difference between these two groups of ISM frequency bands only matters to wireless system developers, think again. For all IoT adopters, being able to distinguish between sub-GHz and 2.4 GHz ISM bands is a major help in your wireless technology decision. This is because the operating frequency impacts the ways in which a radio system performs in key network criteria.

In this blog, we outline 5 major distinctions between sub-GHz and 2.4 GHz to be measured against your network priorities.

1. Sub-GHz ISM Bands = Much Longer Range

As the wavelength is inversely proportional to frequency, sub-GHz waves are much longer than the 2.4 GHz ones. A major advantage of the longer wavelength is that signals can better penetrate through walls, trees, buildings and other structures along the propagation path. On top of that, longer waves are less susceptible to reflection and can bend farther around solid obstacles (i.e. diffraction). Wavelength is also inversely proportional to free space path loss, meaning longer radio waves can travel farther in open areas. Simply put, radio signals in sub-GHz ISM bands offer a better range and can operate more reliably in structurally dense environments.

2. Sub-GHz Signals Experience Less External Radio Interference

Due to its free access, the unlicensed spectrum is filled with multiple co-existing radio technologies. In comparison, the 2.4 GHz ISM band is much more crowded because of legacy use. Wi-Fi hubs, Bluetooth-enabled devices, cordless phones, welding equipment, RF lighting and microwave ovens are just a few examples. The high radio traffic and electromagnetic noise in the 2.4 GHz airways can greatly dampen the reliability and scalability of the IoT network. Saturated 2.4 GHz channels in your facility are not recommended for your IoT deployment.

3. Systems Using Sub-GHz ISM Bands are Often Designed for Lower-Bandwidth Transmissions

Bandwidth represents the range between the upper and lower frequency limit used for transmitting a signal. The more information a signal carries, the more bandwidth it requires. In radio communications, high bandwidth is associated with a high data rate, as you can send more data per time unit. Generally, higher carrier frequencies allow for more bandwidth usage due to larger available spectrum resources, and vice versa. As a result, radio systems in the 2.4 GHz ISM band are often designed for higher-throughput data communication compared to sub-GHz systems.

4. Technologies Using Sub-GHz ISM Bands Are More Power Efficient

Because they use less bandwidth and lower data rates, systems operating in sub-GHz ISM bands offer the benefit of lower power consumption. Signals requiring less bandwidth result in lower thermal noise which in turn, provides better receiver sensitivity (the minimum power level at which a receiver can detect a signal). All things being equal, systems with higher receiver sensitivity require less transmission output, resulting in less power consumption.

Another aspect that favors the power efficiency in sub-GHz solutions is network topology. Thanks to the better range performance (i.e. many kilometers), a sub-GHz transmitter node can send a message directly to a remote receiver using the one-hop star topology. On the other hand, systems in the 2.4 GHz ISM band often resort to a relaying mesh topology to compensate for their short physical range (i.e. few to hundred meters) and extend the data communication distance. The fact that a node must actively stay awake to relay messages through them greatly increases power consumption and thus reduces battery life in 2.4 GHz mesh networks.

5. Systems Using Sub-GHz ISM Bands Are Likely to Entail Less Infrastructure Cost

In terms of infrastructure cost, both options have their benefits, but sub-GHz networks might be the winner here. As antenna length is a function of wavelength, 2.4 GHz solutions require smaller and less expensive antennas for their operations. However, in long-range applications, 2.4 GHz mesh systems often demand extra repeaters to ensure sufficient coverage. A scalable sub-GHz network, on the contrary, requires as few as one base station to cover the same area and number of sensing devices.

Key Takeaways

To wrap it up, both 2.4 GHz and sub-GHz ISM frequency bands have their own pros and cons and are geared for different applications. 2.4 GHz RF is generally a better fit for use cases requiring high data rates within a smaller network environment, such as camera surveillance, personal health and fitness, and some consumer applications. On the other hand, in low-throughput, latency-tolerant scenarios where priorities are placed on range, power efficiency and scalability, sub-GHz RF would be more feasible. This refers to the majority of batter-powered, remote monitoring use cases in industrial and commercial marketplaces.

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What Is MQTT And Why You Need It In Your IoT Architecture

MQTT in the IoT architecture

BehrTech Blog

What is MQTT and Why You Need It in Your IoT Architecture


If you play in the IoT space, you may have heard of the MQ Telemetry Transport (MQTT) protocol. In addition to being used as an underlying communications protocol for IoT and Industrial IoT architectures, MQTT is used in smart home automation systems alongside cloud platforms such as Microsoft Azure, AWS and IBM Watson. Facebook also uses MQTT as a communication protocol for its Messenger and Instagram platforms.

The Industrial Internet of Things (IIoT) can be loosely defined as a system of sensors and other devices interacting with industrial and manufacturing systems all in an effort to enhance business operations. Industries like manufacturing, mining, oil & gas and agribusiness, to name just a few, deploy massive numbers of sensors. These sensors in turn send critical telemetry data to analytics engines, where the data is analyzed for trends and/or anomalies, enabling organizations to better understand and improve their operations.

In environments using Low Power Wide Area Network (LPWAN) solutions, sensor data is sent over wireless radio transmissions where it is received by one or more central base stations. This data, small as individual packets but massive when aggregated together, is then sent to analytics and visualization tools whether in the cloud or on-premises. That’s where MQTT comes in. Residing on top of the TCP/IP network stack, MQTT is a lightweight publish/subscribe messaging protocol designed for low-bandwidth, high latency, unreliable networks. MQTT’s features make it an excellent option for sending high volumes of sensor messages to analytics platforms and cloud solutions.

[bctt tweet=”As organizations migrate to LPWAN, MQTT will prove to be an excellent option for sending high volumes of sensor messages to analytics platforms and cloud solutions.”]

History of MQTT

MQTT was invented in 1999 by engineers Andy Stanford-Clark and Arlen Nipper, as a method of allowing pipelines in the oil and gas industry to communicate with Supervisory Control and Data Acquisition (SCADA) systems. At the time, these systems used disparate, proprietary protocols, and, as such, were not able to communicate with each other. Adding MQTT capabilities helped overcome inter-communication problems. In addition to interoperability, the original goals for the protocol were that it should be lightweight, bandwidth efficient, data agnostic and simple to implement while offering quality-of-service data delivery.

Publish/Subscribe Model

Unlike the traditional client-server model, in which a client communicates directly with an endpoint, MQTT clients are split into two groups: A sender (referred to as a publisher in MQTT) and a consumer that receives the data (an MQTT subscriber). The publisher and the subscriber do not know anything about each other, and, in fact, are never in direct contact with each other. A third component (an MQTT broker), acts like a ‘traffic cop’, directing messages from the publisher to any end points acting as subscribers.

MQTT in the IoT architecture

MQTT Topics

Messages make their way from a publisher, through a broker, to one or more subscribers using topics. Topics are hierarchical UTF-8 strings. Each level in a topic is delimited by a forward slash. Every message from a publisher must include a topic. To receive the published message, the entity that consumes the message must subscribe to the same topic. A broker sends the message it receives only to those clients that have subscribed to the same topic. Examples of topics are:



In environments using LPWAN solutions such as MIOTY by BehrTech, base stations perform the role of MQTT publisher. When a base station receives a message from a sensor, the base station publishes the message to the MQTT broker on behalf of the sensor. The broker then sends the message over TCP/IP to whatever device or devices that have subscribed to the topic.

MQTT in the IoT Architecture

MQTT is natively supported on BehrTech base stations. MQTT brokers and mappings for publishing data are configured with ease. Our out-of-the-box Azure integration also uses MQTT in its underpinnings. Another way in which we use MQTT is through the Node-RED programming tool. Performing the role of the subscriber, Node-RED takes incoming sensor data, processes it and sends it to visualization dashboards. As the published messages from the base station continue, Node-RED dashboards are updated on the fly.

Going forward, as organizations continue to move to LPWAN solutions in order to take advantage of its many benefits, the popularity of MQTT in the IoT architecture will grow as well.


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A New Approach to Indoor Localization in Large-Scale Environments

Indoor Localization

BehrTech Blog

A New Approach to Indoor Localization in Large-Scale Environments


Data on the location of critical equipment and workers, provides powerful insights to optimize asset management and worker safety. Nevertheless, effective indoor localization, especially in large-scale environments, has been a bottleneck in available tracking solutions.

The Global Positioning System (GPS) and many other terms like Navstar, Glonass, Satellite Location and Position Tracking are buzzwords we hear every day.

GPS provides services like asset tracking, fleet tracking, family and even pet tracking that have become essential to our daily life. These services are constantly evolving. UBER is a great example using GPS as a key enabling technology for its innovative service offerings in over 500 cities now globally.

Unfortunately, despite all of its unique power and global availability, GPS is not feasible for indoor location tracking. This is because it doesn’t have the capability to receive satellite signals in indoor environments or provide location calculations independently when no signal is available.

So, what if you’re looking for a commercial-grade indoor asset tracking solution? What if you need to track medical equipment in a large hospital, locate a tool in a large warehouse, or keep an eye on mining workers when moving in underground mines where GPS is out of reach?

Several technologies exist for asset tracking and positioning in indoor locations. However, these solutions come with multiple challenges, especially when implemented in a large-scale environment.

[bctt tweet=”Providing unprecedented range, LPWAN has the potential to replace Wi-Fi and BLE for indoor positioning in geographically dispersed, challenging environments.”]

Challenges of Existing Indoor Localization Solutions

1. Proximity-Based Indoor Asset Tracking

Asset tracking solutions that rely on RFID and BLE Low Energy (BLE) have been around for some time. These solutions incorporate RFID tags or BLE beacons attached to the devices and communicate their ID data to a nearby hard-wired receiver. As RFID and BLE are very limited in coverage, these systems require a high density of receivers/sensing equipment. This quickly inflates infrastructure costs, not to mention the significant hassle of wiring a receiver every few meters in large-scale facilities.

Wi-Fi beacons/tags are another option, but the Wi-Fi range is also very constrained. A single Wi-Fi Access Point or Router usually covers a small area of up to 400 sq ft only, depending on the precision level needed. Also, Wi-Fi-enabled tags are very power-hungry compared to BLE, which makes it impractical for tracking of small devices.

Indoor Localization
Proximity-based Indoor Tracking

2. Trilateration-based Indoor Positioning Systems

Indoor Positioning Systems (IPS) using Wi-Fi or BLE and trilateration technologies are an alternative for indoor localization. These solutions analyze the Received Signal Strength Indicator (RSSI) of each signal to estimate the distance between a device and a Wi-Fi/BLE sensing equipment (RSSI is inversely proportional to distance). Trilateration algorithms are then applied to locate the device by coordinating its distance data from three or more sensing points.

There are generally two scenarios for IPS solutions using Wi-Fi or BLE. In the first scenario, a user device, often a mobile phone, picks up signals from multiple access points or beacons and transfer these signals to the trilateration server. This approach is often applied with people tracking and navigation where typically every user has a smartphone.

Indoor Localization
Trilateration-based IPS Scenario 1 (BLE example)

In the second scenario, a Wi-Fi tag or BLE beacon sends signals to multiple hard-wired receivers, which then relay received signal strength values to the trilateration server for location calculation. This scenario is more relevant for asset tracking applications where you can’t equip every asset with a smartphone. However, similar to proximity-based tracking, the major challenge is the high concentration of hard-wired receivers is required, especially in large indoor facilities.

Indoor Localization
Trilateration-based IPS Scenario 2 (BLE example)

Another major pitfall of all Wi-Fi/BLE-based IPS solutions is the labor-intensive and costly calibration process. Due to the random characteristics of indoor signal propagation, calibration is required in the initial installation to collect RSSI samples and build a radio map (i.e. fingerprint) of a given area. Given the short range of Wi-Fi and BLE, RSSI samples have to be gathered every few meters, which is a major undertaking in vast areas. Also, for worker safety applications in challenging environments like underground mines, Wi-Fi and BLE signals are very unreliable.

Could LPWAN Be the New Answer to Large-Scale Indoor Localization?

So, what type of solution offers a cost-effective, practical and easy to install indoor localization solution in large-scale, long-distance applications? The answer could be Low-Power Wide Area Networks (LPWAN).

Today, LPWAN technology is regarded as a key enabler of the Internet of Things (IoT). Whether tracking parts, people, or equipment, LPWAN is driving IoT advancements with no trade-offs in battery life or costs for improved range and much greater reliability.

Providing unprecedented, multi-kilometer range, LPWAN is potentially the only solution able to cover an entire factory, warehouse, hospital or a mine shaft. As such, LPWAN can potentially replace Wi-Fi and BLE for indoor positioning in geographically dispersed, challenging environments.

How can you build a robust, real-time Indoor Positioning System with a cutting-edge LPWAN technology? At its core, the solution architecture resembles the second scenario of WiFi/BLE-based IPS. There are two major components involved: LPWAN radio equipment delivering robust, wireless indoor connectivity and coverage, and a positioning server running trilateration algorithms.

LPWAN gateways pick up signals from a low-cost LPWAN transmitter attached to an asset or carried by a worker, and then relays the RSSI data to the positioning server. Thanks to much better range and penetration capability, an LPWAN-based IPS requires only a few gateways to cover an entire industrial plant or commercial building.

Indoor Localization
LPWAN-based Indoor Localization Architecture

Indoor tracking solutions with LPWAN do not require every asset and worker to have an expensive, battery-hungry Internet-connected device like a smartphone or tablet. What’s more, since only a few gateways are needed, the expensive and complex wiring process can be minimized.

Compelling LPWAN-based IPS use cases include worker safety and theft prevention of critical assets in vast industrial and commercial facilities, where sub-meter precision is not required.

Wrapping Up

There are many different tracking solutions and technologies in the market today. However, when factoring in cost as well as ease of implementation and management, LPWAN could be the most viable option. Furthermore, some LPWAN solutions, including ours, work on the principle that the data is the sole property of the customer and should not leave the premises. This makes LPWAN even more attractive for industrial and commercial applications where security and data privacy are a top priority.



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