Competition Law and Intelligent Sensing Platforms and Market Power

1. Introduction

Intelligent sensing platforms are digital systems that collect, process, combine and commercially exploit data generated by sensors and connected devices. Examples include:

• IoT sensor networks;
• smart-home devices;
• wearable health and fitness devices;
• connected vehicles;
• industrial sensors and predictive-maintenance systems;
• smart meters and energy-management systems;
• location and proximity sensors;
• environmental and agricultural sensors;
• cameras, LiDAR and other machine-vision systems; and
• platforms using AI to convert sensor data into predictions, rankings or automated decisions.

Competition-law concerns arise when a platform controls not merely the physical sensors but also the data, analytics, operating system, APIs, cloud infrastructure, standards, algorithms and distribution channels surrounding them.

The central competition question is therefore:

Can control over sensor-generated data and the intelligent infrastructure that processes it create, strengthen or protect market power?

The answer depends on market definition, data substitutability, network effects, interoperability, switching costs, exclusivity, vertical integration and the conduct of the platform.

2. Meaning of an Intelligent Sensing Platform

An intelligent sensing platform generally operates through five layers:

Layer 1 — Physical sensing

Sensors collect information concerning:

• location;
• movement;
• temperature;
• heart rate;
• sleep;
• vehicle performance;
• energy consumption;
• industrial equipment;
• environmental conditions; and
• consumer behaviour.

Layer 2 — Connectivity

The sensor communicates through:

• Bluetooth;
• Wi-Fi;
• 5G;
• IoT networks;
• vehicle networks;
• cloud connections; or
• proprietary communication protocols.

Layer 3 — Data aggregation

The platform combines sensor information with:

• user profiles;
• historical information;
• location data;
• purchasing information;
• search behaviour;
• advertising data;
• machine-learning datasets; and
• third-party information.

Layer 4 — Intelligence

AI and analytics convert raw information into:

• predictions;
• recommendations;
• risk scores;
• targeted advertising;
• predictive maintenance;
• health insights;
• automated pricing;
• routing; or
• resource allocation.

Layer 5 — Commercial ecosystem

The resulting intelligence may be supplied through:

• apps;
• cloud services;
• advertising;
• marketplaces;
• enterprise software;
• APIs;
• subscription services; or
• connected-device ecosystems.

Market power can therefore exist at one or several layers simultaneously.

3. Relevant Competition-Law Framework

The principal legal questions are normally:

1. What is the relevant product market?
2. What is the relevant geographic market?
3. Does the sensing platform possess substantial market power or dominance?
4. Is sensor data an important competitive input?
5. Can competitors obtain comparable data elsewhere?
6. Are switching costs substantial?
7. Does the platform restrict interoperability?
8. Does it favour its own sensing products?
9. Does it prevent competitors from accessing APIs or data?
10. Does it combine sensor data with another dominant ecosystem?
11. Does it impose exclusivity?
12. Does it engage in tying or bundling?
13. Does it use data generated by business users to compete against them?
14. Could a merger eliminate an important source of sensor data?
15. Does the conduct reduce innovation rather than merely price competition?

Under traditional competition law, possession of valuable data is not automatically unlawful. The issue becomes substantially more serious where control over data or infrastructure is used to exclude rivals or reinforce an existing dominant position.

4. Market Definition in Intelligent Sensing Markets

Several markets may need to be distinguished.

A. Sensor hardware market

For example:

• wearable sensors;
• automotive sensors;
• industrial sensors;
• smart meters.

B. Sensor operating-system/platform market

A company may control the software through which sensors communicate.

C. Sensor-data market

The relevant competitive asset may be the resulting dataset rather than the physical device.

D. Analytics market

Platforms may compete to transform sensor data into intelligence.

E. Cloud/IoT infrastructure market

Sensor information may depend on a particular cloud or computing infrastructure.

F. Downstream markets

Sensor data may strengthen:

• advertising;
• healthcare;
• insurance;
• mobility;
• energy;
• manufacturing; or
• e-commerce.

Thus, a company can possess modest power in the sensor-hardware market while exercising significant power in a data or downstream intelligence market.

5. Why Sensor Data Can Create Market Power

5.1 Data scale

A large sensing platform may collect millions or billions of observations.

More data can improve:

• prediction accuracy;
• machine-learning models;
• anomaly detection;
• personalisation; and
• product development.

This can create a data feedback loop:

More users → more sensors → more data → better algorithms → better service → more users → more data.

5.2 Data variety

A platform possessing several categories of information may have an advantage over a rival possessing only one.

For example:

Wearable data + location data + search data + purchasing data + health information

may generate intelligence that cannot easily be reproduced by a new entrant.

5.3 Network effects

The value of a sensing ecosystem can increase as:

• more users join;
• more sensors connect;
• more developers build applications; and
• more businesses depend upon the platform.

5.4 Switching costs

Consumers may hesitate to leave an ecosystem because doing so could mean losing:

• historical sensor records;
• health histories;
• device configurations;
• personalised recommendations;
• subscriptions;
• applications;
• connected-home settings; or
• compatibility with other devices.

6. Important Competition-Law Theories

A. Refusal of access

A dominant sensing platform may refuse competitors access to:

• APIs;
• sensor interfaces;
• technical specifications;
• essential datasets;
• interoperability functions; or
• cloud interfaces.

This can raise essential-facility or refusal-to-deal concerns, although the legal requirements for intervention are demanding.

B. Data foreclosure

A dominant platform might prevent rivals from obtaining sensor-generated information.

For example:

A dominant wearable operating system could prevent competing analytics companies from accessing relevant sensor data while using that same information for its own analytics service.

The competition issue is not simply data ownership; it is whether restricting access forecloses effective competition.

C. Self-preferencing

A platform may use sensor-generated information to favour its own products.

Example:

An IoT platform operates an application marketplace and gives its own smart-home products preferential access to sensor information while denying equivalent functionality to competing products.

D. Tying

A platform may require customers to use:

Sensor hardware + proprietary software + proprietary cloud

as a single package.

Competition concerns become stronger if the dominant component is used to exclude competitors in an adjacent market.

E. Exclusive dealing

A platform might require manufacturers to:

• use only its sensors;
• upload data exclusively to its cloud;
• avoid rival analytics providers; or
• refrain from interoperability with competing ecosystems.

F. Leveraging

A company dominant in one market may use sensing data or infrastructure to strengthen its position elsewhere.

For example:

Smartphone ecosystem → wearable data → advertising market

or:

Vehicle operating system → vehicle sensor data → mobility-services market.

7. Case Laws and Enforcement Decisions

The following cases are particularly useful for analysing intelligent sensing platforms, although some arise from broader digital-platform, data or ecosystem markets rather than sensor platforms specifically.

1. Google/Fitbit — European Commission

The proposed acquisition of Fitbit by Google became an important competition-law example concerning wearable devices, health data and digital ecosystems.

The European Commission examined whether Google's acquisition of Fitbit could strengthen Google's position through access to Fitbit's health data and its combination with Google's existing data ecosystem.

The Commission ultimately cleared the transaction subject to commitments concerning the use of health data and interoperability.

Competition principle

The case demonstrates that:

A merger involving a relatively small hardware market may nevertheless raise competition concerns because the acquired company's data can strengthen market power in adjacent digital markets.

Relevance to intelligent sensing

This is perhaps the most directly relevant precedent for:

• wearable sensors;
• health monitoring;
• sensor-generated data;
• data aggregation;
• digital advertising;
• ecosystem effects; and
• interoperability.

The ACCC similarly identified concerns that Google's acquisition of Fitbit could increase barriers to entry by consolidating Google's access to consumer health data.

2. Google Android — Google LLC and Alphabet Inc. v European Commission

Case T-604/18

The General Court considered Google's conduct concerning Android, including:

• app-store arrangements;
• search applications;
• browser applications;
• device manufacturers;
• exclusivity payments; and
• anti-fragmentation requirements.

The case is important because competition can be affected by control over an ecosystem rather than a single product.

Relevance to sensing platforms

An intelligent sensing ecosystem may similarly involve:

device → operating system → application store → cloud → data → analytics.

A dominant platform could potentially use control at one layer to reinforce power at another.

3. Google Shopping

The Google Shopping litigation concerned Google's treatment of competing comparison-shopping services and the preferential placement of its own service.

Competition principle

A dominant platform may face Article 102-type concerns when it uses its position in an upstream platform or infrastructure to favour its own downstream service.

Application to intelligent sensing

Suppose a dominant IoT platform operates:

• the sensing infrastructure;
• the data marketplace; and
• its own analytics service.

If the platform systematically gives its own analytics product superior access to sensor data or visibility, the reasoning surrounding self-preferencing and leveraging becomes relevant.

The European Commission has continued to examine similar ecosystem issues under both competition law and the Digital Markets Act.

4. Facebook/Meta — German Facebook Data Case

The German competition authority's Facebook proceedings are important because they demonstrated how data collection and combination across services can intersect with competition law.

The underlying concern was that a powerful platform could combine information from multiple sources in ways that reinforce its position.

Relevance to sensing platforms

The same analytical issue arises where a sensing platform combines:

• device data;
• location information;
• behavioural data;
• health information;
• purchasing information; and
• third-party data.

The competition concern is particularly significant where rivals cannot obtain a comparable combination of datasets.

Principle

Data practices can become relevant to competition analysis where they contribute to market power, entry barriers or exclusionary effects.

5. Qualcomm — European Commission / General Court

Qualcomm provides an important precedent concerning dominant firms, exclusionary conduct and technological markets.

The General Court examined the Commission's treatment of Qualcomm's conduct and the requirements for establishing exclusionary effects.

Relevance to sensing platforms

Intelligent sensing markets frequently involve technologically sophisticated components where:

• interoperability is important;
• manufacturers depend on particular suppliers;
• switching suppliers can be expensive;
• technical standards matter; and
• access to technological inputs can influence downstream competition.

The case therefore helps demonstrate that technological complexity does not place conduct outside conventional dominance analysis.

6. SAMR v. Alibaba — China

China's State Administration for Market Regulation imposed a major penalty on Alibaba in 2021 concerning its "choose one from two" exclusivity practices.

The case concerned Alibaba's use of its platform position to restrict merchants from operating simultaneously on competing platforms. The penalty was RMB 18.228 billion.

Relevance to sensing platforms

Consider a dominant IoT platform that tells manufacturers:

"If you use our sensing ecosystem, you cannot simultaneously connect your products to a competing sensing platform."

This could create a similar platform foreclosure/exclusivity problem, depending upon market power and effects.

Principle

A sensing platform cannot necessarily use its ecosystem position to prevent suppliers or users from participating in competing ecosystems.

7. Nomi Technologies — FTC

Nomi Technologies is not a conventional dominance case, but it is highly relevant to the economic and regulatory characteristics of sensing systems.

Nomi deployed sensors that collected information concerning mobile devices and provided retailers with aggregated information concerning customer movements and behaviour. The FTC challenged representations concerning consumer choice and tracking.

Competition relevance

The case demonstrates the economic value of sensor-generated behavioural information.

Such information can potentially reveal:

• customer movements;
• store visits;
• dwell time;
• repeat visits;
• device characteristics; and
• consumer behaviour.

Where a dominant platform controls comparable information, the dataset can become a commercially significant competitive input.

Thus, Nomi is useful for understanding what sensing data actually represents economically, even though the proceeding itself was primarily consumer-protection oriented.

8. Google/Fitbit — Australian Competition and Consumer Commission

The Australian proceedings concerning Google's proposed Fitbit acquisition provide another important precedent.

The ACCC stated that access to Fitbit's health information could:

• increase Google's data advantage;
• strengthen barriers to entry;
• reinforce Google's position in digital advertising; and
• affect competition in health-related markets. 

The ACCC also expressed concerns regarding competitors' dependence on Google's Android ecosystem for effective operation of wearable devices.

Importance

This demonstrates a particularly important principle:

A sensor-data acquisition can create competitive concerns even when the acquired company's primary product market is not itself highly concentrated.

8. Synthesis of the Case Law

9. Essential-Facility Issues

The most difficult question is whether sensor data can constitute an essential facility.

A claimant would generally need to demonstrate more than:

"The data would be useful to my business."

The analysis may require consideration of:

1. whether the data is genuinely indispensable;
2. whether an alternative source exists;
3. whether duplication is technically or economically feasible;
4. whether access can be provided without undermining legitimate business interests;
5. whether denial has exclusionary effects; and
6. whether the platform has sufficient market power.

For example, a dominant autonomous-driving platform possessing an enormous proprietary dataset might become difficult for competitors to replicate.

But rarity alone does not automatically transform data into an essential facility.

10. Interoperability

Interoperability is particularly important in sensing ecosystems.

A dominant platform may control:

• APIs;
• device permissions;
• operating-system interfaces;
• cloud protocols;
• data formats;
• authentication systems; and
• communication standards.

If rivals cannot communicate effectively with the dominant platform, users may become locked into that ecosystem.

Current EU digital-market enforcement illustrates the increasing importance of interoperability for connected physical devices. The Commission has specifically addressed interoperability between smartphones and connected devices such as smartwatches and smart glasses.

11. Data Portability and Switching Costs

Data portability can reduce market power by allowing consumers to move their accumulated sensor histories.

For example, a consumer changing wearable providers may wish to transfer:

• heart-rate history;
• sleep history;
• activity records;
• device settings;
• exercise records; and
• personalised information.

If portability is technically restricted, historical data can become a lock-in mechanism.

The EU's current digital-market work includes measures facilitating transfer of device data between ecosystems, demonstrating the competition importance of portability in connected-device markets.

12. Algorithmic Discrimination

Intelligent sensing platforms increasingly use algorithms to determine:

• pricing;
• insurance risk;
• energy consumption;
• transportation routes;
• maintenance schedules;
• advertising;
• product rankings; and
• access to services.

A dominant platform could potentially use proprietary sensor data to create an algorithmic advantage unavailable to rivals.

For example:

Platform A operates 70% of connected vehicles and receives continuous vehicle-performance data. It uses that information to develop predictive-maintenance software and then denies comparable access to independent repair and analytics providers.

Potential competition theories include:

• refusal to supply;
• leveraging;
• discrimination;
• foreclosure;
• tying;
• self-preferencing; and
• abuse of dominance.

13. Vertical Integration

Vertical integration is especially important.

Consider:

Sensor manufacturer → IoT platform → cloud → analytics → marketplace

A vertically integrated company could potentially disadvantage independent firms at several levels.

Examples include:

• charging higher API-access fees to rivals;
• delaying interoperability;
• withholding technical information;
• giving proprietary devices superior access;
• using downstream customer data to compete against customers; or
• tying hardware to cloud services.

Vertical integration itself is not unlawful. The question is whether it produces anticompetitive foreclosure or other legally cognisable harm.

14. Merger Control

Mergers involving sensing platforms require special attention because conventional turnover or market-share analysis may underestimate the strategic importance of data.

Important questions include:

Horizontal effects

Will the merger eliminate an important sensor competitor?

Vertical effects

Will the merged firm control an important sensor-data input?

Conglomerate effects

Can the merged firm combine sensor data with another dominant digital service?

Innovation effects

Will the transaction eliminate a potential future competitor?

Data effects

Will the transaction combine datasets that rivals cannot reproduce?

The Google/Fitbit proceedings are particularly important because regulators examined precisely these ecosystem and data-related theories.

15. Chinese Competition-Law Perspective

Under China's Anti-Monopoly Law and platform-economy enforcement framework, intelligent sensing platforms can raise concerns involving:

• abuse of dominance;
• exclusive dealing;
• discriminatory treatment;
• unreasonable conditions;
• refusal to transact;
• tying;
• data-related exclusion;
• algorithmic conduct;
• platform rules; and
• concentration of economic power.

China's platform-economy enforcement has increasingly focused on exclusionary practices and the competitive consequences of platform ecosystems. The Alibaba case remains a major reference point.

The current regulatory direction also emphasises stronger antitrust enforcement and data-driven regulatory capabilities.

16. Competition Concerns by Conduct

17. Consumer Welfare and Innovation

The competition assessment should not assume that every large sensing ecosystem is harmful.

Intelligent sensing platforms can produce significant efficiencies:

• improved safety;
• predictive maintenance;
• lower energy consumption;
• better healthcare monitoring;
• reduced transport congestion;
• more accurate forecasting;
• improved product quality; and
• new technological innovation.

Therefore, competition authorities must distinguish between:

Legitimate integration

Integration that improves interoperability, efficiency and product quality.

and

Anticompetitive integration

Integration that uses market power to prevent rivals from competing effectively.

The central issue is competitive process, not simply technological size.

18. Remedies

Possible remedies include:

Structural remedies

• divestiture;
• separation of business units;
• prohibition of certain acquisitions.

Behavioural remedies

• non-discriminatory API access;
• interoperability obligations;
• data-portability requirements;
• restrictions on exclusive dealing;
• non-discrimination requirements;
• transparent ranking rules.

Data-related remedies

• controlled data sharing;
• data portability;
• limits on combining datasets;
• access to particular technical interfaces.

Monitoring remedies

Authorities may require:

• compliance officers;
• periodic reporting;
• independent monitoring;
• algorithmic audits; and
• technical interoperability testing.

19. Hypothetical Example

Assume IntelliSense Ltd. operates the largest smart-building sensor platform.

It controls:

• 65% of connected-building sensors;
• the dominant cloud platform;
• the largest building-temperature dataset;
• the principal analytics marketplace; and
• the API through which third-party applications access sensor information.

It then launches its own energy-optimisation software.

At the same time, IntelliSense:

1. gives its own software real-time sensor access;
2. delays rival API requests;
3. charges rivals substantially more;
4. prevents customers from exporting historical data; and
5. requires sensor manufacturers to use its cloud.

The competition analysis could involve:

Market power → data advantage → API control → foreclosure → downstream analytics advantage → increased entry barriers.

Possible legal theories include:

• refusal to deal;
• discriminatory access;
• tying;
• exclusive dealing;
• self-preferencing;
• leveraging; and
• restriction of interoperability.

The actual legal outcome would depend upon the relevant market, dominance, evidence of exclusionary effects and applicable jurisdiction.

20. Key Legal Principles

The principal lessons are:

1. Sensor data can be a competitive asset.
2. Data possession alone does not establish unlawful dominance.
3. Data becomes more significant where it cannot easily be replicated.
4. Interoperability can be central to competition in sensor ecosystems.
5. APIs may constitute strategically important competitive infrastructure.
6. Control over sensor data can reinforce downstream market power.
7. Self-preferencing can become problematic where a dominant platform favours its own downstream products.
8. Exclusive dealing can foreclose competing sensing ecosystems.
9. Mergers involving sensor-data businesses require analysis beyond traditional hardware market shares.
10. Network effects and switching costs can make market power persistent.
11. Data portability can reduce ecosystem lock-in.
12. Competition law should distinguish legitimate technological integration from exclusionary leveraging.

21. Conclusion

Intelligent sensing platforms represent a convergence of hardware, data, artificial intelligence, cloud computing and digital ecosystems. Their competition significance therefore extends beyond the traditional market for sensors.

The most important competition-law issue is increasingly control over the entire information ecosystem:

Sensors → connectivity → data → analytics → AI → applications → consumers/business users.

The Google/Fitbit proceedings demonstrate why sensor-generated health information can matter to merger analysis; Google Android and Google Shopping illustrate ecosystem and self-preferencing theories; the Facebook data proceedings demonstrate the competitive significance of data aggregation; Qualcomm illustrates competition concerns in technologically sophisticated markets; Alibaba demonstrates platform foreclosure through exclusivity; and Nomi illustrates the commercial value of sensor-generated behavioural information.

Accordingly, future competition analysis of intelligent sensing platforms is likely to focus not merely on who sells the most sensors, but on who controls the data, interoperability, interfaces, algorithms and ecosystem through which sensor information becomes economically valuable.