Overview
In this guide, we examine how the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is unlocking new opportunities for industries, especially those operating in remote and hard-to-reach areas. We explore the technologies enabling AIoT, including satellite connectivity, LoRa® networks, and hybrid models, and address the key challenges of scalability, security, and regulation.
By providing insights into how AIoT is enhancing operations in sectors such as agriculture, logistics, energy, and maritime, this guide aims to help business leaders, IoT developers, and policymakers understand the critical role of ubiquitous, reliable connectivity in delivering real-time intelligence where it’s needed most.
The guide draws on the expertise of EchoStar Mobile, a leader in satellite IoT and the founder of the first LoRa®-enabled satellite IoT network. Aiming to provide an essential resource for those looking to adopt or expand their AIoT strategy, our guide underscores how satellite technology is driving the future of intelligent, autonomous systems.
Section 1
Background: The Convergence of AI and IoT
The Internet of Things (IoT) is rapidly altering how industries operate, enabling devices to collect and exchange data at an unprecedented scale. At the end of 2024, there were an estimated 18.8 billion connected IoT devices, an increase of 13% in just 12 months. By 2030, it is predicted that this number will rise to 40 billion. These devices are being deployed across a wide variety of industries, from smart agriculture sensors monitoring crop conditions to industrial machinery predicting maintenance needs.
You can read more about IoT and its evolution in our Complete Guide to Satellite IoT.
As IoT networks generate and deliver massive volumes of data, analysis is becoming increasingly challenging. IoT on its own cannot provide real-time intelligence. Therefore, this is where Artificial Intelligence (AI) steps in, converging with IoT to offer AIoT.
What is AIoT?
AIoT (Artificial Intelligence of Things) combines AI and IoT. It’s an emerging technology that integrates AI-driven analytics with IoT-generated data, enabling devices and systems to process information in real time, recognise patterns, and make intelligent, automated decisions without human intervention!
With IoT offering large-scale data collection, and AI providing the means to analyse it, we now have the technology to turn connected devices into proactive, self-optimising assets capable of enhancing everything from efficiency and performance to safety and security. AIoT offers immense opportunities across an extensive range of industries. For example, it can detect when equipment malfunctions in manufacturing, enhance security around critical infrastructure and send alerts about unsafe water levels in flood-prone regions. We’ll take a closer look at the applications of AIoT in section 4 of this guide.
The Connectivity Challenge
AIoT offers massive potential, but just like standard IoT applications that came before it, this technology relies on connectivity to deliver real-time data exchange. Terrestrial networks are an excellent option, but their coverage has significant gaps. Estimates suggest that only 15% of the world’s surface is covered by cellular networks, leaving operations in industries like maritime shipping, oil and gas, agriculture and environmental monitoring often out of reach.
Satellite services bridge this gap, providing continuous, always-on coverage that can enable AIoT applications to function reliably in any location, no matter how remote. Therefore, in these extensive, underserved regions, satellites play a vital role in unlocking the potential of AIoT.
Section 2
The AIoT Landscape: Where Intelligence Resides
As AI and IoT combine, the ability to collect, analyse and respond to data autonomously from remote assets is realised. This autonomous intelligence can reside in two primary places: the centralised cloud or on the device itself (at the edge).
Each model has advantages and disadvantages, creating a growing interest in developing a third hybrid approach that blends the best of both.
Cloud AIoT
In the cloud AIoT model, IoT devices collect raw data and send it to a central cloud computing platform. The AI in the cloud can then analyse the data according to specific algorithms, identifying patterns, generating insights and providing autonomous responses. The power of the cloud AIoT is its significant storage and immense computing power.
Cloud AIoT is best suited for making complex decisions across multiple systems. However, latency and dependence on connectivity mean that it is not typically suitable for real-time responses.
Key benefits of cloud AIoT:
- AI can leverage massive datasets for sophisticated, deeper analysis.
- Supports long-term forecasting and machine learning model training.
- Enables centralised management of IoT networks across multiple locations.
Edge AIoT
With Edge AIoT, analysis occurs directly on the device or at the edge of the network, i.e. local gateways or edge servers. This model enables real-time data processing, reducing latency and bandwidth usage and removing the reliance on continuous connectivity. With this ability for instant response, edge AIoT is ideal for remote or time-sensitive applications. However, the disadvantage of edge AIoT is its limited processing power, memory and storage, meaning that (currently) it’s not suitable for highly complex tasks.
Key benefits of edge AIoT:
- Faster decision-making without waiting for cloud processing.
- Lower bandwidth consumption, reducing data transmission costs.
- Greater reliability in areas with limited or intermittent connectivity.
The Hybrid Approach
With the two primary models having certain disadvantages, an emerging technology seeks to combine them both. This approach would deliver the advantages of both cloud and edge AIoT: handling real-time decision-making and processing larger datasets and insights. Hybrid AIoT will allow devices to make immediate decisions as required while transferring data to the cloud for enriched analytics and long-term optimisation.
It’s a technology that is still evolving. However, we have seen it leveraged in the IBEX infrastructure of our partner, MountAIn. Its pilot case study monitors early warning signs for wildfire. Data is analysed on-device, and alerts are triggered and sent to a central platform when specific parameters are met and action is required. While this is a pilot, the technology can be applied to a wide range of use cases. Full details of this use case can be found here .
As AIoT adoption grows, the choice between edge, cloud, and hybrid models will depend on latency needs, connectivity availability, and the scale of AI processing required.
Section 3
The Role of Satellite in AIoT
Connectivity plays a vital role in the success of AIoT, enabling data transfer between IoT sensors and cloud gateways or servers. Without it, even the most advanced AI-powered IoT devices cannot operate. In urban areas, traditional terrestrial networks, like cellular and fibre, make connectivity for AIoT applications widely available. However, there are vast regions that are not covered by this level of service.
In remote rural areas and offshore environments, coverage can be patchy at best or entirely non-existent. Expanding infrastructure to these regions is often too costly or impractical. For industries operating in these areas, this lack of reliable terrestrial coverage creates a significant barrier to accessing emerging AIoT technology.
For AIoT to deliver all its promised benefits to every industry, regardless of location, ubiquitous connectivity is essential. This connectivity must be resilient and secure to enable AIoT applications to collect, transmit and respond to data in real time.
Satellite services offer a solution for overcoming this coverage gap, providing the reach and reliability needed for IoT devices to stay connected in remote regions. Satellite IoT can also complement terrestrial networks to create a hybrid connectivity model that leverages the optimal coverage option depending on the environment and supports true ubiquitous connectivity.
The addition of low-power IoT technologies like LoRa® enhances this model further by enabling long-range, energy-efficient data transmission, working in conjunction with network infrastructures, such as terrestrial gateways or satellite links, to relay data. In remote areas where terrestrial networks are unavailable, LoRa® devices transmit small packets of critical data, using direct-to-satellite technology to the cloud platform. The use of LoRa® technology reduces bandwidth costs, extends battery life, and ensures reliable coverage even in off-grid environments, making LoRa® ideal for applications like smart agriculture, asset tracking, and environmental monitoring.
Other low-power IoT technologies, such as NB-IoT (Narrowband IoT), also support long-range communication. They currently rely on licensed cellular networks, but there are emerging approaches for NB-IoT over NTN (Non-Terrestrial Networks).
You can read more about LoRa® and NB-IoT technologies in our Complete Guide to Satellite IoT.
Section 4
How AIoT is Transforming Industries
With the ability to collate massive amounts of data across extensive areas and carry out real-time analysis and autonomous decision-making, AIoT has the potential to drive meaningful change across a wide range of industries, especially those that rely on remote operations and complex asset management. Integrate satellite connectivity to extend reach beyond the boundaries of terrestrial networks, and we see the potential of AIoT become even more powerful.
Here are a few examples of the advantages satellite-enabled AIoT can bring to these industries:
Smart Agriculture
Agriculture is becoming increasingly data-driven, meaning that AIoT can be pivotal in improving efficiencies, quality, and safety. The use of IoT sensors to collect information on properties such as soil moisture, temperature, and nutrient levels is growing in the agriculture market.
The next step is using these satellite-connected IoT devices to feed data into AI models, allowing for optimisation of irrigation schedules, fertiliser use, and crop health. Livestock tracking solutions follow a similar approach. IoT collars can be combined with AI to monitor grazing patterns and detect early signs of illness. Additionally, AIoT can analyse historical data and live satellite feeds to identify weather patterns and climate trends, helping farmers to make better-informed decisions about planting, harvesting, and resource management, even in remote locations with no cellular coverage.
Supply Chain & Logistics
AIoT offers a compelling way to optimise operations for industries moving goods across expansive distances. Asset tracking via satellite IoT solutions, such as Linxens Satellite Sticker, ensures that cargo, vehicles, and containers can be monitored wherever they are.
Integrate AI into these solutions to offer route analysis and delay predictions. In addition, by collecting and analysing weather data and traffic patterns, AIoT can recommend alternative routes and help prevent disruptions to enhance efficiency. Satellite-enabled AIoT has the capability to provide visibility across an entire supply chain that is not only transparent but also responsive in real time, even when shipments cross through areas with limited terrestrial connectivity.
Energy & Utilities
In the energy sector, infrastructure is often spread across remote and hazardous environments, providing the opportunity for AIoT to improve both safety and efficiency. Many pipelines, wind farms, and power stations rely on satellite-connected IoT sensors to monitor equipment performance, environmental conditions, and structural integrity and deliver data to a central hub.
With the addition of AI, this data can be autonomously analysed at the edge or in the cloud to identify potential faults before they escalate into failures, enabling predictive maintenance and reducing downtime. In utilities, AIoT can help to balance supply and demand across smart grids, making energy delivery more resilient and efficient, even as consumption patterns fluctuate. In a sector where reliable data transfer is essential for service continuity, satellite connectivity ensures that these critical systems remain online, regardless of location.
Environmental Monitoring
With the elevated importance of climate change and sustainability, gathering and acting upon environmental data has never been more crucial. Environmental IoT sensors are currently being used to track a variety of environmental conditions, such as temperature, humidity and air quality.
AIoT can enhance this data collection process by identifying unusual patterns that could signal an impending event. For example, in wildfire-prone areas, AI-powered early warning systems can analyse heat levels and wind speeds, issuing alerts for proactive action to prevent the fires from spreading. Flood and landslide AIoT systems could operate similarly, allowing for timely evacuations and mitigation efforts. With satellite connectivity, these solutions remain reliable even in remote and underserved regions, providing real-time insights that protect lives and property.
Maritime
When vessels are operating at sea, they are far from terrestrial networks, making satellite connectivity critical. Furthermore, as ships generate large amounts of operational data, including engine performance, fuel consumption and navigation status, having an AI application that can automate data analysis would provide considerable benefits.
AIoT could be used to optimise routes, manage fuel efficiency, and predict maintenance needs, ensuring smoother voyages and reducing operational costs. With the resilience of satellite connectivity, these insights can be reliably delivered in real time, helping fleets operate more safely and sustainably across global waters.
Across these industries and beyond, AIoT has the scope to create smarter systems that can predict, respond and adapt, and with the support of satellite networks, these innovations are no longer limited by geography.
Section 5
Addressing Challenges: Scalability, Security & Regulatory Considerations
As AIoT adoption accelerates, so do the challenges of managing scalable, secure and compliant systems, particularly when deploying numerous devices in remote locations. For AIoT to offer reliability and efficiency across our operations, successfully overcoming these barriers is essential.
Here are some of the key considerations that need to be addressed as the adoption of AIoT grows:
Scalability
Successfully scaling AIoT depends on having the right connectivity foundation to support extensive networks of devices that stretch from urban to remote and hard-to-reach areas. As discussed earlier, satellite connectivity provides the continuous coverage needed to keep AIoT systems online wherever they operate. LoRa® technology is also valuable, offering low-power, long-range communication and helping reduce energy use and transmission costs. Meanwhile, hybrid networks, which combine terrestrial, satellite, and relay systems (such as EM-SR), add flexibility, optimising coverage while reducing costs and supporting the efficient scaling of AIoT deployments across geographies.
Once this connectivity foundation is established, there are additional strategies that support scalability while managing bandwidth, power consumption, and operational efficiency:
- Edge AI reduces bandwidth demands by processing data locally and sending only essential insights.
- Adaptive data compression leverages context-aware algorithms to dynamically reduce the size of routine or low-importance data while preserving the accuracy of critical insights, thus optimising bandwidth usage without losing essential information.
- AI-optimised transmission scheduling helps avoid network congestion by sending data during off-peak windows, lowering costs and improving network efficiency.
- Firmware Updates Over The Air (FUOTA) enables remote software updates, eliminating the need for on-site maintenance.
- Compact, plug-and-play device designs simplify large-scale deployments by making installation faster, easier and more cost-effective.
By combining resilient, ubiquitous connectivity with these intelligent data handling and streamlined device management approaches, AIoT systems can scale effectively and deliver reliable, real-time insights at any operational size.
Cybersecurity in AIoT Ecosystems
With IoT ecosystems comprising massive networks of connected devices that handle sensitive data, cybersecurity risks become elevated. As AIoT is rolled out on a wider scale, protection against these threats must be a top priority.
Options include using AI-driven threat detection with machine learning (ML) to monitor networks for unusual activity and block potential cyberattacks before they can cause harm. For satellite-enabled AIoT, industry-recognised data encryption, such as the Advanced Encryption Standard (AES), can be leveraged to safeguard data as it travels from device to cloud, ensuring the integrity and privacy of critical information, even over long distances and across multiple jurisdictions.
Policy and Regulation
Operating across borders means navigating a complex and evolving regulatory environment. While no specific data sovereignty laws currently exist exclusively for AIoT, these systems inherit the same legal considerations that apply to IoT data. This includes adhering to national rules that control where data can be stored, processed, and transmitted. These regulations are particularly relevant for satellite-connected devices that move data across multiple jurisdictions.
At the same time, emerging AI regulations such as the EU AI Act and established frameworks like GDPR and the NIS2 Directive place additional responsibilities on organisations to ensure ethical, secure, and transparent use of AI technologies. For AIoT systems, this means not only safeguarding the flow of IoT data but also ensuring that AI-driven decisions made on that data meet regulatory standards for accountability, fairness, and privacy.
By addressing these challenges and being proactive in deploying appropriate strategies to overcome them, businesses can benefit from the full potential of AIoT to deliver secure, scalable, and compliant solutions that work wherever they’re needed.
Section 6
The Future: Where is AIoT Headed?
With significant interest in the potential of AIoT, the technology is evolving rapidly. Edge AI, hybrid networks and the opportunities around autonomous systems are all driving its progress. Moreover, as we get closer to the reality of seamless global connectivity, AIoT will make a greater impact on industries that rely on real-time intelligence in even the most remote environments.
Here are a few emerging trends that will likely form the future of AIoT:
Increased Adoption of AI-at-the-Edge
The ability of edge AI to reduce bandwidth dependence by enabling on-device decision-making is likely to lead to substantial growth in this area. With AI models running directly on IoT devices, analysis and decision-making can happen in real time, minimising the need for constant data transmission. This capability is particularly valuable in applications such as autonomous vehicles, drones, wearable monitoring devices, and security cameras.
Advances in Hybrid Networks
As discussed, the full potential of AIoT can only be realised with a foundation of ubiquitous, reliable connectivity. Hybrid models, where technology such as satellite and terrestrial networks and LoRa® connectivity are interoperable, will support this level of seamless coverage and enable more cost-effective AIoT deployment at scale.
AI-Driven Autonomous IoT Systems
We’re already seeing the rapid adoption of autonomous IoT networks, as they provide critical data and decision-making in traffic management, environmental monitoring and predictive maintenance. As AI becomes more sophisticated and satellite IoT offers wider coverage, this uptake will only accelerate, enhancing situational awareness, operational efficiency, asset protection and safety across more expansive regions.
Driving Innovation and Industry Standards
Leading industry players are coming together to drive progress and regulation in global connectivity and data sharing. For example, EchoStar Mobile founded the LoRaWAN® Over Satellites Task Force in partnership with the LoRa Alliance®. This technical committee is focused on the seamless convergence of satellite and terrestrial networks while addressing essential regulatory requirements.
Efforts like these will set the foundation for a more connected, intelligent, and scalable AIoT future.
Conclusion
As AIoT continues to advance, it enables industries to benefit from smarter, faster, and more autonomous operations, even in the most remote environments. However, accessing the opportunities AIoT offers requires overcoming challenges around connectivity, scalability, security, and regulation. Satellite services, low-power connectivity technologies and innovative application design are helping to overcome these barriers and open up the benefits of AIoT to a broader range of industries.
Now is the time for businesses to prepare. Those who embrace AIoT today will gain a competitive edge through greater operational efficiency, real-time insights, and future-proofed infrastructure.
To learn more about the role our satellite solutions can play in your AIoT use case, please get in touch with our team.