By Javier Surasky
Este post está disponible en español en https://globalradar-analysis.blogspot.com/2025/12/el-poder-en-el-borde-iot-edge-computing.html
Introduction
The future of AI is coming and becoming part of our everyday surroundings. From your computer to your neighborhood, and even your laundry machine, AI is making itself at home. We now find ourselves operating at the edge, enveloped in the "fog," with the Internet of Things (IoT) woven into daily life. Let’s explore what this means together.
IoT: The Internet landed in the Physical World
Let’s begin with a familiar entry point into these technological transformations: the Internet of Things. IoT is already part of our daily routines, often operating quietly in the background. Industrial sensors, medical devices, transport systems, and power grids now form an infrastructure where the internet and the physical world come together. In simple terms, IoT integrates physical objects into a digital information ecosystem through devices that continuously generate data—rooted in local contexts, sensitive to time, and often connected to important questions of personal security and privacy (Atzori et al., 2010; Gubbi et al., 2013).
Imagine smart heating that senses temperature and occupancy and adjusts itself automatically; wearable heart monitors that keep track of your heart rate, connect to a medical platform, and send alerts if something unusual happens; or traffic lights that adjust their timing based on real-time congestion. These examples help make IoT feel more real—and they also highlight its three basic building blocks: (1) sensors that capture what’s happening in the physical world, (2) connections that let data flow back and forth, and (3) the ability to make decisions and turn them into action.
The cloudy ecosystem
IoT can bring convenience and safety, but it also generates a growing flood of data that must circulate to and from data centers—most often through cloud infrastructures. That makes the circulation speed (latency) central to whether these systems work properly (Atzori et al., 2010). As the number of connected devices rises, latency is put under pressure: bandwidth bottlenecks multiply, and security risks deepen because many devices handle highly sensitive information (including whether someone is home or what a heart is doing in real time).
This matters geopolitically because IoT does not simply add a “virtual layer” to objects; it materializes digital infrastructure through them. Data is produced in concrete places, but the management and exploitation that generate its value tend to happen across transnationally distributed servers. That creates a structural friction between the national and the international. In response, computing architectures have been moving away from strict centralization toward distributed arrangements that operate closer to where data originates, reducing latency (Shi et al., 2016; Shi & Dustdar, 2016).
Edge Computing
This takes us to edge computing, commonly described as “any computing and network resources along the path between data sources and cloud data centers. The logic of edge computing is that computation should occur at the proximity of data sources (Shi et al., 2016:638)
Gateways are crucial in this story. What do they do? Sensors often rely on short-range protocols (for instance, Bluetooth). Their signals are picked up by intermediary devices that extend reach—and, in the process, translate data into formats compatible with the internet’s IP “language.” Sometimes they also filter out noise and apply basic rules (e.g., if X is detected, start process Y). When a gateway performs these functions, it effectively becomes an edge node. The term fits literally: it is the “gate” connecting locally generated data to the broader internet.
Fog and Edge: same idea, different directions
Edge computing does not handle locally produced data on its own. It works alongside fog computing. They overlap—and are frequently mixed up—yet an important distinction is often highlighted: “and they are regularly confused with one another, there is a slight distinction between them. In fog computing, there is a single centralized processing device that handles information from various endpoints in the system. In edge computing, each system participates in processing information” (Singla et al., 2021:44)
What used to look like a direct line (“device → cloud processing”) is now mediated by additional layers—edge and fog—that coordinate services, allocate resources, and strengthen security for especially sensitive data (Shi & Dustdar, 2016). Rather than rivals, edge and fog are complementary: they jointly support the functional decentralization that IoT increasingly demands.
The political significance follows from the technical shift: distributing processing capacity across multiple layers alters where and how automated decisions are produced. Edge- and fog-level devices stop being passive data feeders and become active nodes that filter inputs, execute logic, and shape the behavior of the whole system (Shi & Dustdar, 2016). Seen this way, edge computing becomes a critical pillar of digital infrastructure. It supports resilience in advanced networks—particularly where dependence is high, tolerance for failure is low, and continuity is essential. Because edge nodes sit at the first point of contact with emitting devices, they can isolate faults, reduce systemic choke points, and keep vital services running when connections to large data centers are disrupted—directly affecting domains like security, health, and crisis response.
Who's in charge of the Edge?
Because edge infrastructures perform these strategic functions, standard-setting becomes especially consequential. This includes, for instance, ITU-T Recommendation X.1648 on edge computing data security (ITU, 2025). Progress exists, but standards are never only technical—or, if you prefer, never politically neutral. Setting standards determines interoperability patterns, which in turn can lock in dependencies, privilege certain systems over others, and shape who can steer data flows over time. Whoever leads the race to define global edge standards may end up shaping how future critical infrastructures operate.
These struggles involve states, but they also revolve around major technology firms and infrastructure providers that design and operate the equipment enabling edge and fog systems.
At the international level, this often translates into state–corporate alignments to advance particular approaches to edge governance—yet always under tension: states generally do not control the technologies or data-flow architectures that firms control at the edge. As a result, corporate and public priorities will not always match, because they follow different logics.
States respond by trying to compensate for this “capacity deficit” through regulation: defining which data must be processed and stored locally, what counts as sensitive, and what may or may not be transferred to centralized infrastructures located beyond national territory.
The outcome is a partnership where both sides rely on each other more than either would prefer.
Edge AI and Development
The picture becomes even more complex with the fusion of edge computing and AI: “the convergence of edge computing and artificial intelligence has given birth to a new research area, namely ‘edge intelligence’ or ‘edge AI.’ Instead of relying entirely on the cloud, edge intelligence makes full use of pervasive edge resources to gain AI insights.” (Zhou et al., 2019:1). Autonomous vehicles, surveillance systems, and digital health tools often require real-time responses using sensitive data. Edge intelligence aims to filter and decide locally, sending only processed outputs to the cloud.
This trajectory aligns with “Industry 4.0” (or “smart industry”), which is built “on the use of emerging technologies to improve manufacturing processes, machine maintenance, optimize production costs, enhance employee training and conditions, boost customer relationships, or create new high-quality services and products” (Rodal Montero, 2020:6). Achieving this requires advanced energy systems and robust edge infrastructures—an overall complexity recognized in policy and strategy documents from both the European Union (European Commission, 2020 and 2025) and UNESCO (2021). Having these capabilities—or lacking them—becomes a strategic asset in the international system (OECD, 2025).
And none of this unfolds evenly. UNDP’s Digital Development Compass shows how technology management and human capabilities combine to enable (or constrain) the deployment of IoT and edge computing, exposing sharp inequalities across regions and countries (UNDP, 2023).
In the United States, strong digital capabilities and deep STEM-based human capital have turned edge computing into an extension of its broader digital power—replicating territorial models and exporting de facto architectures and standards that shape the choices of receiving countries.
China, by contrast, uses its capacity to build edge-processing networks that absorb vast volumes of domestically generated data without depending on external infrastructures. At the same time, these systems strengthen government tools for population control and provide practical experience that supports China’s push to export IoT and edge solutions (UNDP, 2023).
Brazil illustrates a frequent pattern among emerging economies: meaningful digital infrastructure and digital-economy capacity, but unevenly distributed and concentrated in a few zones. This enables sector-specific IoT/edge strategies, yet complicates the creation of a coherent national approach that would allow sustained power accumulation—so Brazil’s dependent position in the global digital value chain remains largely intact (UNDP, 2023), even if it gains functional autonomy in specific domains.
Kenya—often highlighted as an African case of digitalization—shows edge computing as a still-conditional possibility. Deployment remains tied to the consolidation of nascent national capacities, and in the meantime, depends heavily on external actors. The result can be a fragile position for the state: local processing without meaningful control over infrastructure, and without equitable capture of the value created from local data (UNDP, 2023).
A New Geopolitical Battleground
Taken together, these dynamics help explain why IoT and edge computing are increasingly central to digital policy, international cooperation, and development debates. Edge is not only about efficiency in handling data flows. It also drives questions about how countries plug into global technological chains, how digital infrastructures are built and governed, how human capacities are strengthened to manage frontier technologies, and what real margins of maneuver exist.
Edge computing does not automatically reduce digital inequality. Depending on politics and power relations, it may intensify existing asymmetries. It depends on sovereign decisions about data, yet it also reveals unequal capacities to manage and extract value from that data. The earlier paradox returns with new actors: leading countries need data from lagging countries; lagging countries can try to retain that data through regulation, but cannot fully capitalize on it unless systems are dominated by leaders. Cooperation is framed as “win-win,” but the question is whether that can hold when unequal power sets the tempo.
There is no single answer. But there is a basic reality that any geopolitical analysis of digital technology—especially AI—has to absorb: IoT and edge computing are not a passing phase. They are becoming structural, and their scale and sophistication will continue to expand.
References
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Deng, S.; Zhao, H.; Fang, W.; Yin, J.; Dustdar, S. y Zomaya, A. Y. (2020). Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8), 7457-7469.
European Commission (2020). Shaping Europe’s digital future. European Union. https://eufordigital.eu/wp-content/uploads/2020/04/communication-shaping-europes-digital-future-feb2020_en_4.pdf
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