Decentralization and De-anonymization in Internet of Things Architectures: An Analytical Review

Abstract
This paper examines the paradoxical coexistence of decentralization rhetoric and practices of systemic de-anonymization in contemporary digital infrastructures, with a particular focus on the Internet of Things (IoT). It argues that decentralization in such systems most often denotes a redistribution of functions and responsibilities rather than a redistribution of power and control. The analysis identifies architectural, economic, and regulatory mechanisms through which IoT infrastructures intensify centralized data correlation and governance over subjects, while simultaneously preserving the appearance of autonomy and networked organization.

1. Introduction: Decentralization as a Political–Technical Narrative In public discourse, decentralization is commonly associated with democratization, resilience, and a reduction in the concentration of power. Within digital infrastructures, however, the term increasingly functions as a normative and legitimizing frame that does not correspond to the actual distribution of control. This discrepancy becomes particularly salient in systems that combine formally decentralized architectures with policies of de-anonymization.

The aim of this paper is to analyze how decentralization operates as both an architectural and discursive mechanism for legitimizing centralized control, and to explain why the expansion of IoT infrastructures renders such configurations especially stable and economically advantageous.

1. Architectural Distinction: Distribution of Functions vs. Distribution of Power A central methodological distinction must be drawn between:

the distribution of computation, sensors, and actuators, and
the distribution of authority over identity, rules, and permissible actions.
In IoT architectures, a persistent pattern can be observed in which the network periphery (the edge) is responsible for data collection and localized decision-making, while the following elements remain centralized or subordinated to a unified root of trust:

the definition and management of subject and device identity,
policy formulation and enforcement,
data correlation and long-term model training.
As a result, decentralization functions not as an alternative to centralization, but as its operational extension.

1. The Political Economy of Decentralized Surveillance The growth of IoT infrastructures makes distributed architectures particularly advantageous for centralized control for several interrelated reasons.

3.1 Density of Sensor Coverage
The large-scale deployment of peripheral devices transforms physical and social space into a near-continuous sensor array. This significantly reduces blind spots and increases the stability and granularity of behavioral profiling.

3.2 Externalization of Costs
Capital and operational expenditures for sensors and their maintenance are shifted to households, private enterprises, and municipalities, while centralized actors concentrate resources on cloud-based analytics, correlation, and decision-making systems.

3.3 Acceleration of Governance Feedback Loops
Edge computing enables real-time responses at the local level, while aggregated metadata is transmitted to centralized systems for model training, policy refinement, and long-term oversight.

1. Mechanisms of De-anonymization in IoT Systems Anonymity in IoT environments is not undermined by a single technical decision, but by a constellation of mutually reinforcing mechanisms:

Persistent identifiers: hardware-backed keys, certificates, eSIMs, serial numbers, and firmware attestation mechanisms establish stable and legally actionable bindings between devices and subjects.
Cross-modal correlation: the fusion of video, audio, radiometric, geospatial, and energy-consumption data erodes local pseudonymity.
Transport-layer metadata: even where local processing is emphasized, network protocols (e.g., MQTT, CoAP, HTTP) generate correlatable metadata streams.
Service and payment coupling: subscriptions, insurance products, warranties, and incentives depend on continuous identity attestation.
Behavioral uniqueness: spatio-temporal routines generate distinctive behavioral signatures that are difficult to obfuscate without degrading system utility.

1. Why This Configuration Is Still Labeled “Decentralization” The persistence of the term “decentralization” can be explained by several factors:

functions are distributed, while decision-making sovereignty is not;
federated learning and edge analytics operate as interfaces of autonomy under a unified root of trust;
control becomes multi-layered and networked in form, yet standardized and centralized in its governing rules.
In this sense, decentralization serves as both an architectural strategy and a discursive device that mitigates social resistance and enhances the legitimacy of surveillance infrastructures.

1. Representative Use Cases Smart metering: local measurement and automated disconnection coexist with centralized billing, load profiling, tariff optimization, and sanctioning mechanisms. Vehicle telematics: edge-based risk assessment operates in real time, while centralized systems determine insurance pricing and behavioral penalties. Urban sensing infrastructures: distributed cameras, sensors, and actuators regulate flows and access, while centralized correlation systems construct mobility models, social graphs, and service prioritization schemes.
2. Discussion and Conclusions Decentralization in IoT systems should not be understood as the antithesis of centralized power, but as its structural continuation. It enables the scaling of control without monolithic institutions, reduces visibility and cost, and translates policy into background conditions of the environment itself.

At the same time, alternative architectural trajectories remain possible. Data minimization by default, unlinkable credentials, separation of trust jurisdictions, and the auditing of policies rather than individuals represent potential levers for restoring meaningful autonomy. Their implementation, however, requires not only technical innovation but also institutional and regulatory transformation.

From this perspective, decentralization in IoT is less an engineering problem than a political-economic and normative one, implicating the very structure of digital sovereignty and anonymity in networked societies.