Graphic representing the difference between LoRa and LoRaWAN

The Difference Between LoRa and LoRaWAN and Their Application in Agriculture

In the realm of Internet of Things (IoT) technologies, LoRa and LoRaWAN are often mentioned together, but they serve distinct roles in communication. This article provides a brief overview to clarify the differences between the two terms and their roles in IoT applications, particularly in agriculture.

LoRa, short for Long Range, is a proprietary wireless modulation technique developed by Semtech for long-range, low-power communication. It uses a modulation method called chirp spread spectrum (CSS) to achieve long-distance transmission with very low energy consumption. Operating on various unlicensed frequency bands, LoRa enables devices to communicate over several miles, even in rural or challenging environments.

While LoRa refers to the physical layer technology, it is also used by third parties to create proprietary solutions based on LoRa radio technology. These solutions may offer functionalities similar to LoRaWAN but remain proprietary and are not based on open standards. This means they might not be interoperable with devices and networks from other vendors, potentially leading to vendor lock-in.

LoRaWAN, standing for Long Range Wide Area Network, is an open standard network protocol built on top of LoRa. It defines the communication protocol and system architecture for managing network communication between LoRa devices and gateways. LoRaWAN adds an additional layer of functionality by specifying how devices communicate with network gateways and how data is handled in the network to ensure secure and reliable communication. This includes features like end-to-end encryption, device authentication, and network management.

One of the key distinctions is that LoRaWAN is an open standard protocol, governed by the LoRa Alliance, which promotes interoperability and widespread adoption across various industries. In contrast, while LoRa technology itself is proprietary to Semtech, third-party proprietary solutions based on LoRa can further limit interoperability. The use of open standards like LoRaWAN allows for greater collaboration, flexibility, and scalability, as manufacturers and developers can build compatible devices and applications without being tied to a single vendor or facing licensing restrictions. This openness fosters innovation and drives down costs, benefiting users and the industry as a whole.

Advantages of Open Standards for Farmers

For farmers and growers, the adoption of open standards like LoRaWAN offers significant advantages. A primary benefit is the avoidance of vendor lock-in. Since LoRaWAN is an open protocol, farmers are not dependent on a single vendor for their IoT solutions. If a vendor goes bankrupt, raises subscription prices, or shifts market focus, farmers can switch to other vendors without losing their existing investments in devices and infrastructure. The ability to mix and match devices and services from different providers ensures continuity and protects against unforeseen changes in the vendor landscape. This flexibility ultimately leads to more sustainable and cost-effective agricultural operations.

In contrast, proprietary solutions based on LoRa technology may lock farmers into a single vendor’s ecosystem. Should issues arise with that vendor, farmers might face significant costs and disruptions in replacing hardware and reconfiguring systems. Open standards mitigate this risk by ensuring that equipment and software from different sources work seamlessly together.

Application in Agriculture

LoRa’s long-range capabilities enable sensors and devices to monitor soil moisture, weather conditions, and crop health from remote locations without frequent maintenance or battery changes. LoRaWAN builds upon this by providing a scalable and secure open-standard network for these devices. By leveraging an open standard like LoRaWAN, agricultural IoT solutions can benefit from a broad ecosystem of compatible devices and services. This allows farmers to collect data from numerous sensors spread across large fields and transmit it to centralized systems for analysis. The result is enhanced efficiency and productivity in farming operations, with the added assurance that their technology investments are protected against vendor-related risks.

To learn more about LoRaWAN, and how it allows Zenseio devices to help optimize crop yields and more, visit: LSMP | TeleFarm.

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Solutions in Precision Agriculture

As climate conditions continue to shift while the global population increases, farmers and food producers everywhere need to continually adapt their methods like never before. Managing and allocating resources to maximize efficiency is crucial, and precise data-driven monitoring is key to accomplishing this. Understanding and overcoming the challenges of farming in the 21st century introduces us to the world of precision agriculture.
What is precision agriculture?
Though definitions can vary, the International Society of Precision Agriculture defines it as “a management strategy that takes account of temporal and spatial variability to improve the sustainability of agricultural production.”
In practice, this generally boils down to a system of agriculture that aims to maximize efficiency in food production while minimizing waste and maintaining a healthy (and ideally self-sustaining) ecosystem. Some experts suggest that precision agriculture draws its roots from the adoption of mechanized processes of food cultivation in the early 20th century, continuing through the Green Revolutions of the 1960’s, when new techniques of controlled irrigation became standard and newly developed high-yield strains of crucial grains were introduced in the form of dwarf wheat and hybrid rice. Today, precision agriculture is driven by technological innovation and careful analysis of continuously changing data in real time.
The use of advanced metrics for managing agricultural production is not just a cost-saving measure used by food producers; it is instead vital to keep up with the ever-increasing demand for nutritious food globally. One frequently cited statistic estimates that “world food production needs to increase by 70% to feed the world population in 2050”, and to produce this food, “it is estimated that 52.8 million gallons of water per second are required”.
Feeding the World’s Present and Future Generations
Raising the world’s net food production is no easy feat, especially when considering delicate environmental considerations. It is now abundantly clear that previous techniques used to maximize food production, like monocropping and concerted animal feeding operations (or CAFOS), are not sustainable in the long term.
This is where the role sensors play in precision agriculture systems come into play. One of the most important and commonly used sensors in agriculture is the soil moisture probe. Soil moisture is a critical factor influencing plant growth, nutrient uptake, and overall crop health, and maintaining the right soil moisture levels is essential for ensuring optimal conditions for plant development. Soil moisture probes can enable farmers to tailor irrigation schedules to the specific needs of each crop and can adjust accordingly. These probes use capacitance or impedance to provide accurate and real-time data about soil moisture levels. To further monitor and manage irrigation systems , pressure sensors are strategically placed within irrigation systems to measure water pressure. This data is then analyzed to determine if adjustments are needed to maintain an optimal water flow rate for different crops.
Of course, natural irrigation must be tracked and accounted for as well. Understanding local precipitation patterns empowers farmers to adapt and optimize their farming practices by gaining insights into rainfall patterns. Rain buckets, also known as rain gauges, operate on a simple yet effective mechanism, consisting of a funnel that directs rainwater to a calibrated container, which is then measured to determine the rainfall intensity. The next level of hydration tracking lies directly at the plant level.
Monitoring leaf wetness is crucial for preventing diseases, as excessive moisture on plant surfaces creates favorable conditions for pathogens to thrive. Early detection and management of these conditions are essential for crop protection. Leaf wetness sensors use conductive or capacitive methods to detect moisture on plant surfaces. These sensors provide real-time information about the duration and intensity of leaf wetness, helping farmers make informed decisions on irrigation and disease control.
All the sensors listed above are just pieces of the overall puzzle of developing and maintaining a precision agriculture operation. Every crop and plot of land may require or benefit from their own specialized monitoring systems, but each of these sensors and the resulting adaptations from the data make up the present and future of precision agriculture, with only new innovations and techniques to come.

Zenseio provides easy to use, long-range telemetry solutions for commercial farms, including remotely monitoring soil moisture, irrigation systems, and weather conditions. Zenseio solutions work with many of the most commonly used agricultural and industrial sensors, with more being made compatabile. To learn more about Precision Ag and how Zenseio makes it a reality, visit: https://zenseio.com/

A padlock and key connected to a chain

Key considerations for IoT sensor devices: 4 – Security

Executive Summary:

  • Security is critical for IoT
  • End application defines security policy requirements
  • Security is weak if device crypto key are not well protected in IoT devices
  • Best-in-class security policies for IoT sensors are achieved with tamper-proof hardware crypto key management

Details:

These days, there is hardly any mention in the media about IoT without security concerns being mentioned in the same sentence. The harsh critique of the status quo is well justified. Security is a must in great majority of IoT applications. However, security is a very loaded word, so let’s plainly clarify how it applies to industrial IoT sensor devices, without getting into the complexities of this involved topic.

What we mean here by security is the practice of defending information from unauthorized access, use, disclosure, disruption, modification, perusal, inspection, recording or destruction.

Security objectives and practices are defined by an end application. Security is a whole system concern, and, thus, it is only as good as its weakest link. Since sensor devices are part of a much larger IoT system, there is no meaning in saying whether the devices themselves are secure or not. For instance, if a device can securely handle its sensor data without being compromised, but a cloud service receiving this data has security holes, the overall IoT application is still insecure and easy to exploit.

On the other hand, what is important is to require that the devices can support security policies as defined for the system. Depending on application requirements, the security policies can be of various levels from lax to very stringent. For example, security policies for a radiation monitoring system around a nuclear reactor are very different than security policies for a public service weather station. The level of stringent-ness is always driven by an application’s criticality due to the trade off of exponential increase in cost and system complexity for better protection.

Security policies for IoT sensor devices typically deal with the requirements of authenticity, authorization, integrity, confidentiality, and adaptability/resistance of the underlying hardware system. In plain language, devices may be required to uniquely and securely identify themselves to the connecting cloud service (and vice versa), to provide restricted data access only to the users/services that have explicit authorization to do so, to protect the restricted data from being eavesdropped or hacked, and to enable on-going security bug fixes as they are discovered.

For IoT sensor devices to support such security requirements, the relevant security firmware has to be implemented in the device and the embedded hardware has to be able to run complex cryptographic algorithms in real time. This, in itself, has been a technological challenge as the embedded hardware has been often too resource constrained to run anything by the simplest crypto algorithms, if any at all.

Additionally, the security firmware is typically based on well-tested, open source cryptographic protocols. These protocols follow the best practice of separating the cryptographic operations, which are well publicized, from randomized secret keys which are the basis for all these cryptographic operations. As long as a hacker has no physical access to a sensor device, the system provides excellent security for communication link even if these crypto keys are not fully protected in hardware. However, once a hacker can gain physical access to the device (which should be assumed true for unsecured perimeter deployments) and can manage to extract the secret keys (for example via a debug access port or by monitoring electrical current signature on power supply), all the security goes out through the window and the system is compromised. What makes the matter worse is that majority of IoT communication radio standards use Pre-Shared Key cryptography (PSK). PSK combined with lax security implementation of using the same secret key for many devices (perhaps due to ignorance or laziness of personnel) results in large scale security breach. Once one device is compromised, all other devices with the same pre-shared secret key are compromised too. As the result, a deceptively secure system could be used to steal or manipulate restricted data or to stage further attacks on other parts of the system – a disaster scenario known too well from recent media headlines.

To help resist such security breaches, an effective practice is to protect secret keys and unique device identifiers within specialized hardware crypto coprocessors. They are designed to resist even sophisticated physical attacks such as reading electromagnetic signatures radiated by device or visually inspecting non-volatile memory state of the decapped IC chips. Once programmed during the sensor device commissioning, the secret keys are never again revealed externally, instead they are used by cryptographic algorithms internally within hardware, revealing only a securely encrypted output.

The concept is analogous to a hardware-based Trusted Platform Module (TPM) in many enterprise-grade PC’s which creates a “root of trust” for the security software to rely on to implement stringent security policies. Similar crypto technology that is used in multi-thousand dollar equipment is now feasible to be used in ultra low cost IoT sensor devices. However, few sensor devices implement any hardware-based protection for the secret crypto keys today.