Natural Disaster Detection IoT Market by Component (Hardware, Services, Software), Technology (Advanced Computing & Big Data Analytics, Artificial Intelligence & Machine Learning, Mobile & Communication Technologies), Application, End-User - Global Foreca
Description
The Natural Disaster Detection IoT Market was valued at USD 519.48 million in 2024 and is projected to grow to USD 693.28 million in 2025, with a CAGR of 34.36%, reaching USD 5,519.78 million by 2032.
A concise yet comprehensive introduction to natural disaster detection IoT that frames urgency, technological convergence, and strategic priorities for stakeholders
Natural disaster detection solutions increasingly sit at the intersection of sensing hardware, resilient connectivity, and advanced analytics that together transform how governments, responders, and private operators anticipate and react to extreme events. The proliferation of low-power sensors, edge compute modules, and scalable communication stacks has enabled denser observation networks than previously feasible. Consequently, organizations are shifting from episodic monitoring to continuous situational awareness that supports faster decision cycles and preemptive mitigation measures.
At the same time, the operational environment has grown more complex. Data interoperability, ethical use of predictive models, and cross-jurisdictional coordination now shape procurement and deployment decisions as much as raw technical capability. As a result, leaders must weigh near-term readiness against long-term resilience, ensuring that investments in detection infrastructure are accompanied by governance frameworks, workforce training, and sustainable maintenance practices. This introduction frames the subsequent analysis by highlighting the interplay among technology maturity, operational constraints, and policy dynamics that will determine success in disaster detection initiatives.
How transformative shifts in sensing, connectivity, analytics, and policy are redefining disaster response and resilience across public and private sectors
The landscape of disaster detection is undergoing transformative shifts driven by maturation of sensing platforms, the rise of edge AI, and the integration of heterogeneous data sources including satellite, aerial, and in-situ instruments. These technical advances have reduced latency from detection to actionable insight while improving the fidelity of event characterization. In parallel, communications technologies such as narrowband IoT variants, enhanced cellular standards, and resilient mesh protocols are enabling connectivity in austere environments where wired infrastructure is absent or compromised.
Policy and procurement practices are also evolving; stakeholders increasingly prioritize interoperability and open data standards to enable cross-agency collaboration and third-party innovation. Private-sector entrants are bringing agile development cycles and service-oriented business models, which in turn pressure incumbents to accelerate upgrades and provide modular solutions. Finally, societal expectations for rapid notification and transparent decision-making are pushing designers to embed explainability and data provenance into analytic pipelines. Taken together, these shifts are reshaping how solutions are designed, deployed, and sustained, with implications for procurement, operations, and strategic partnerships.
Assessment of the cumulative effects of United States tariffs enacted through 2025 on supply chains, sourcing strategies, and procurement of disaster detection IoT components
Tariff measures enacted through 2025 have become an important factor influencing procurement strategies, supplier selection, and inventory planning across disaster detection programs. Components such as specialized sensors, high-performance compute modules, and telemetry devices are often sourced through international supply networks that span tariff jurisdictions. As a result, procurement teams have responded by diversifying supplier portfolios, increasing local sourcing where feasible, and re-evaluating bill-of-materials configurations to balance performance with total acquisition cost implications.
Operationally, the cumulative tariff environment has accelerated adoption of design choices that reduce exposure to tariff volatility. These include modular architectures that allow substitution of subcomponents without redesigning entire systems, a stronger emphasis on standard interfaces to support multi-vendor compatibility, and longer-term supplier agreements that lock in pricing or allocate tariff risk. Additionally, organizations are investing in in-country assembly and value-add activities to mitigate duties while complying with regulatory requirements. While tariffs add complexity, they have also stimulated regional supply-chain resilience initiatives and closer collaboration between procurement, engineering, and legal teams to ensure continuity of deployment schedules for mission-critical detection infrastructure.
Key segmentation insights synthesized across components, technologies, applications, and end users to reveal adoption patterns, integration challenges, and value drivers
A granular view across components, technologies, applications, and end users highlights differentiated adoption drivers and integration challenges that require tailored strategies. When evaluating component-level dynamics, hardware spans computational and storage devices, data transmission devices, power supply and energy storage solutions, sensors and detectors, and user interface and notification systems; software comprises communication and networking platforms, data analysis and management tools, and geographic information system capabilities; services encompass systems integration, managed operations, and consultancy. These component distinctions matter because hardware choices determine deployment footprint and maintenance cadence, software shapes interoperability and analytics capability, and services influence mission readiness and ongoing operational effectiveness.
From a technology perspective, advanced computing and big data analytics enable richer situational models and retrospective analysis, artificial intelligence and machine learning add predictive capacity and anomaly detection, and mobile and communication technologies ensure resilient data flows under stress. Application domains vary in latency tolerance and sensor needs: drought detection and management emphasize long-term hydrological datasets, earthquake management requires ultra-low-latency seismic networks, flood monitoring relies on rapid telemetry and hydraulic modeling, forest fire management prioritizes rapid thermal and smoke detection fused with meteorological inputs, landslide detection combines soil moisture and geotechnical monitoring, and weather monitoring integrates broad atmospheric sensing. End users span government organizations, law enforcement agencies, private companies, and rescue personnel, each bringing distinct operational priorities, procurement constraints, and integration requirements. Understanding these layered segmentations in tandem enables more effective solution design, procurement alignment, and deployment sequencing that respects both technical constraints and user needs.
Regional dynamics and capability differentials across the Americas, Europe Middle East and Africa, and Asia-Pacific shaping deployment, partnerships, and operational readiness
Regional dynamics significantly influence how detection capabilities are prioritized, procured, and integrated into operational frameworks. In the Americas, investments often emphasize multi-hazard interoperability and scalability to cover extensive geographic diversity, with strong demand for solutions that can operate across urban and remote environments. North and South American stakeholders tend to pursue public–private collaborations and scalable cloud-enabled services that complement dense ground networks and satellite data streams.
Across Europe, the Middle East & Africa, there is notable heterogeneity: European nations frequently prioritize regulatory compliance, cross-border interoperability, and integration with established civil protection frameworks, while parts of the Middle East focus on rapid deployment in desert and coastal zones and Africa emphasizes low-power, ruggedized sensing and local capacity building. In the Asia-Pacific, high hazard exposure combined with dense populations drives an emphasis on early-warning systems, rapid communications, and resilient network topologies; regional supply-chain ecosystems and strong public investment in national detection systems further accelerate deployment. These regional nuances shape partnership models, technology choices, and operational priorities, and they underscore the need for flexible architectures that can be localized to meet regulatory, environmental, and logistical constraints.
Competitive and collaborative company insights highlighting strategic positioning, technology portfolios, partnership approaches, and areas of competitive differentiation in IoT disaster detection
Company strategies within the disaster detection IoT space are becoming defined by a combination of core technology strengths, partner ecosystems, and service delivery models. Organizations investing in sensor R&D and ruggedized hardware differentiate on reliability, lifetime maintenance costs, and environmental tolerance. Firms focusing on analytics and software distinguish themselves through the depth of domain models, ability to fuse heterogeneous data sources, and support for actionable workflows for decision-makers. Service-oriented players that blend managed operations, system integration, and training can accelerate client adoption by reducing the internal burden of capability development.
Strategic partnerships are central to competitive positioning. Technology alliances that link satellite data providers, communications carriers, and edge-compute vendors yield vertically integrated solutions with faster time-to-value. At the same time, open-interface approaches invite an ecosystem of third-party developers and niche sensor manufacturers, expanding solution breadth. Companies pursuing sustainability commitments and transparent data governance practices are increasingly attractive to public-sector customers and international funders. Overall, competitive differentiation arises from the ability to combine domain expertise with modular architectures and scalable service models that support phased adoption and long-term resilience.
Actionable, prioritized recommendations for industry leaders to accelerate resilient deployments, optimize supply chains, and build interoperable, ethical, and sustainable IoT ecosystems
Industry leaders should adopt a set of prioritized actions that accelerate resilient deployments and ensure long-term operability. First, design modular systems that allow component substitution and phased upgrades to reduce supply-chain exposure and support rapid field repairs. Second, build hybrid analytics architectures that balance edge inference for low-latency decisioning with centralized analysis for model refinement and historical trend assessment. Third, formalize interoperability through adherence to open standards and API-driven integration to enable multi-vendor ecosystems and data exchange across jurisdictions.
In parallel, invest in workforce capabilities and operational procedures by creating training pathways for field technicians and incident managers, and by embedding data-governance frameworks that address provenance, privacy, and model explainability. Strengthen supplier relationships through multi-sourcing strategies and regional assembly to mitigate tariff and logistical risks. Finally, prioritize pilot programs that demonstrate cross-functional value in operational contexts, then institutionalize lessons through documented playbooks and maintenance contracts. These steps together create a resilient pathway from pilot to scale while protecting operational continuity and safeguarding ethical deployment principles.
Transparent research methodology describing data sources, analytical frameworks, validation processes, and triangulation techniques used to produce robust, objective findings
The research underpinning this analysis used a multi-method approach to ensure rigor, transparency, and traceability of findings. Primary data collection included structured interviews with practitioners across public safety, emergency management, and private-sector operators, supplemented by technical briefings with product and solution engineers. Secondary sources comprised peer-reviewed literature, standards documents, policy papers, and publicly available technical specifications; these were synthesized to validate technical capability claims and to map interoperability constraints.
Analytic techniques included qualitative coding to identify recurring operational requirements, technology readiness assessments to calibrate deployment feasibility, and scenario analysis to explore supply-chain and tariff contingencies. Findings were triangulated through cross-validation among interview insights, technical documentation, and observed deployment case studies. Quality controls included peer review by domain specialists and iterative revision cycles to refine assumptions and language. The resulting methodology emphasizes evidence-based interpretation and practical relevance for decision-makers seeking to translate analysis into prioritized actions.
Conclusion synthesizing strategic implications, operational priorities, and the imperative for coordinated investment in detection technologies, training, and governance
The cumulative analysis points toward a clear imperative: investments in detection technologies must be matched by commitments to interoperability, operationalization, and governance to realize their full societal value. Technological maturity in sensors, communications, and analytics now enables systems that can detect a broad range of hazards with higher fidelity and lower latency than before. Yet capability alone does not guarantee impact; success depends on integration into decision-making workflows, sustained maintenance, and institutional preparedness to act on early warnings.
Looking ahead, organizations that prioritize modular architectures, diverse supply chains, and human-centered operational design will be best positioned to improve resilience and reduce response times. Moreover, cross-sector collaboration-spanning public agencies, private operators, and community responders-remains essential to scale effective solutions while maintaining trust and accountability. The conclusion consolidates the strategic direction: align technological choices with operational realities, invest in people and processes, and pursue partnerships that embed detection capabilities into actionable resilience pathways.
Note: PDF & Excel + Online Access - 1 Year
A concise yet comprehensive introduction to natural disaster detection IoT that frames urgency, technological convergence, and strategic priorities for stakeholders
Natural disaster detection solutions increasingly sit at the intersection of sensing hardware, resilient connectivity, and advanced analytics that together transform how governments, responders, and private operators anticipate and react to extreme events. The proliferation of low-power sensors, edge compute modules, and scalable communication stacks has enabled denser observation networks than previously feasible. Consequently, organizations are shifting from episodic monitoring to continuous situational awareness that supports faster decision cycles and preemptive mitigation measures.
At the same time, the operational environment has grown more complex. Data interoperability, ethical use of predictive models, and cross-jurisdictional coordination now shape procurement and deployment decisions as much as raw technical capability. As a result, leaders must weigh near-term readiness against long-term resilience, ensuring that investments in detection infrastructure are accompanied by governance frameworks, workforce training, and sustainable maintenance practices. This introduction frames the subsequent analysis by highlighting the interplay among technology maturity, operational constraints, and policy dynamics that will determine success in disaster detection initiatives.
How transformative shifts in sensing, connectivity, analytics, and policy are redefining disaster response and resilience across public and private sectors
The landscape of disaster detection is undergoing transformative shifts driven by maturation of sensing platforms, the rise of edge AI, and the integration of heterogeneous data sources including satellite, aerial, and in-situ instruments. These technical advances have reduced latency from detection to actionable insight while improving the fidelity of event characterization. In parallel, communications technologies such as narrowband IoT variants, enhanced cellular standards, and resilient mesh protocols are enabling connectivity in austere environments where wired infrastructure is absent or compromised.
Policy and procurement practices are also evolving; stakeholders increasingly prioritize interoperability and open data standards to enable cross-agency collaboration and third-party innovation. Private-sector entrants are bringing agile development cycles and service-oriented business models, which in turn pressure incumbents to accelerate upgrades and provide modular solutions. Finally, societal expectations for rapid notification and transparent decision-making are pushing designers to embed explainability and data provenance into analytic pipelines. Taken together, these shifts are reshaping how solutions are designed, deployed, and sustained, with implications for procurement, operations, and strategic partnerships.
Assessment of the cumulative effects of United States tariffs enacted through 2025 on supply chains, sourcing strategies, and procurement of disaster detection IoT components
Tariff measures enacted through 2025 have become an important factor influencing procurement strategies, supplier selection, and inventory planning across disaster detection programs. Components such as specialized sensors, high-performance compute modules, and telemetry devices are often sourced through international supply networks that span tariff jurisdictions. As a result, procurement teams have responded by diversifying supplier portfolios, increasing local sourcing where feasible, and re-evaluating bill-of-materials configurations to balance performance with total acquisition cost implications.
Operationally, the cumulative tariff environment has accelerated adoption of design choices that reduce exposure to tariff volatility. These include modular architectures that allow substitution of subcomponents without redesigning entire systems, a stronger emphasis on standard interfaces to support multi-vendor compatibility, and longer-term supplier agreements that lock in pricing or allocate tariff risk. Additionally, organizations are investing in in-country assembly and value-add activities to mitigate duties while complying with regulatory requirements. While tariffs add complexity, they have also stimulated regional supply-chain resilience initiatives and closer collaboration between procurement, engineering, and legal teams to ensure continuity of deployment schedules for mission-critical detection infrastructure.
Key segmentation insights synthesized across components, technologies, applications, and end users to reveal adoption patterns, integration challenges, and value drivers
A granular view across components, technologies, applications, and end users highlights differentiated adoption drivers and integration challenges that require tailored strategies. When evaluating component-level dynamics, hardware spans computational and storage devices, data transmission devices, power supply and energy storage solutions, sensors and detectors, and user interface and notification systems; software comprises communication and networking platforms, data analysis and management tools, and geographic information system capabilities; services encompass systems integration, managed operations, and consultancy. These component distinctions matter because hardware choices determine deployment footprint and maintenance cadence, software shapes interoperability and analytics capability, and services influence mission readiness and ongoing operational effectiveness.
From a technology perspective, advanced computing and big data analytics enable richer situational models and retrospective analysis, artificial intelligence and machine learning add predictive capacity and anomaly detection, and mobile and communication technologies ensure resilient data flows under stress. Application domains vary in latency tolerance and sensor needs: drought detection and management emphasize long-term hydrological datasets, earthquake management requires ultra-low-latency seismic networks, flood monitoring relies on rapid telemetry and hydraulic modeling, forest fire management prioritizes rapid thermal and smoke detection fused with meteorological inputs, landslide detection combines soil moisture and geotechnical monitoring, and weather monitoring integrates broad atmospheric sensing. End users span government organizations, law enforcement agencies, private companies, and rescue personnel, each bringing distinct operational priorities, procurement constraints, and integration requirements. Understanding these layered segmentations in tandem enables more effective solution design, procurement alignment, and deployment sequencing that respects both technical constraints and user needs.
Regional dynamics and capability differentials across the Americas, Europe Middle East and Africa, and Asia-Pacific shaping deployment, partnerships, and operational readiness
Regional dynamics significantly influence how detection capabilities are prioritized, procured, and integrated into operational frameworks. In the Americas, investments often emphasize multi-hazard interoperability and scalability to cover extensive geographic diversity, with strong demand for solutions that can operate across urban and remote environments. North and South American stakeholders tend to pursue public–private collaborations and scalable cloud-enabled services that complement dense ground networks and satellite data streams.
Across Europe, the Middle East & Africa, there is notable heterogeneity: European nations frequently prioritize regulatory compliance, cross-border interoperability, and integration with established civil protection frameworks, while parts of the Middle East focus on rapid deployment in desert and coastal zones and Africa emphasizes low-power, ruggedized sensing and local capacity building. In the Asia-Pacific, high hazard exposure combined with dense populations drives an emphasis on early-warning systems, rapid communications, and resilient network topologies; regional supply-chain ecosystems and strong public investment in national detection systems further accelerate deployment. These regional nuances shape partnership models, technology choices, and operational priorities, and they underscore the need for flexible architectures that can be localized to meet regulatory, environmental, and logistical constraints.
Competitive and collaborative company insights highlighting strategic positioning, technology portfolios, partnership approaches, and areas of competitive differentiation in IoT disaster detection
Company strategies within the disaster detection IoT space are becoming defined by a combination of core technology strengths, partner ecosystems, and service delivery models. Organizations investing in sensor R&D and ruggedized hardware differentiate on reliability, lifetime maintenance costs, and environmental tolerance. Firms focusing on analytics and software distinguish themselves through the depth of domain models, ability to fuse heterogeneous data sources, and support for actionable workflows for decision-makers. Service-oriented players that blend managed operations, system integration, and training can accelerate client adoption by reducing the internal burden of capability development.
Strategic partnerships are central to competitive positioning. Technology alliances that link satellite data providers, communications carriers, and edge-compute vendors yield vertically integrated solutions with faster time-to-value. At the same time, open-interface approaches invite an ecosystem of third-party developers and niche sensor manufacturers, expanding solution breadth. Companies pursuing sustainability commitments and transparent data governance practices are increasingly attractive to public-sector customers and international funders. Overall, competitive differentiation arises from the ability to combine domain expertise with modular architectures and scalable service models that support phased adoption and long-term resilience.
Actionable, prioritized recommendations for industry leaders to accelerate resilient deployments, optimize supply chains, and build interoperable, ethical, and sustainable IoT ecosystems
Industry leaders should adopt a set of prioritized actions that accelerate resilient deployments and ensure long-term operability. First, design modular systems that allow component substitution and phased upgrades to reduce supply-chain exposure and support rapid field repairs. Second, build hybrid analytics architectures that balance edge inference for low-latency decisioning with centralized analysis for model refinement and historical trend assessment. Third, formalize interoperability through adherence to open standards and API-driven integration to enable multi-vendor ecosystems and data exchange across jurisdictions.
In parallel, invest in workforce capabilities and operational procedures by creating training pathways for field technicians and incident managers, and by embedding data-governance frameworks that address provenance, privacy, and model explainability. Strengthen supplier relationships through multi-sourcing strategies and regional assembly to mitigate tariff and logistical risks. Finally, prioritize pilot programs that demonstrate cross-functional value in operational contexts, then institutionalize lessons through documented playbooks and maintenance contracts. These steps together create a resilient pathway from pilot to scale while protecting operational continuity and safeguarding ethical deployment principles.
Transparent research methodology describing data sources, analytical frameworks, validation processes, and triangulation techniques used to produce robust, objective findings
The research underpinning this analysis used a multi-method approach to ensure rigor, transparency, and traceability of findings. Primary data collection included structured interviews with practitioners across public safety, emergency management, and private-sector operators, supplemented by technical briefings with product and solution engineers. Secondary sources comprised peer-reviewed literature, standards documents, policy papers, and publicly available technical specifications; these were synthesized to validate technical capability claims and to map interoperability constraints.
Analytic techniques included qualitative coding to identify recurring operational requirements, technology readiness assessments to calibrate deployment feasibility, and scenario analysis to explore supply-chain and tariff contingencies. Findings were triangulated through cross-validation among interview insights, technical documentation, and observed deployment case studies. Quality controls included peer review by domain specialists and iterative revision cycles to refine assumptions and language. The resulting methodology emphasizes evidence-based interpretation and practical relevance for decision-makers seeking to translate analysis into prioritized actions.
Conclusion synthesizing strategic implications, operational priorities, and the imperative for coordinated investment in detection technologies, training, and governance
The cumulative analysis points toward a clear imperative: investments in detection technologies must be matched by commitments to interoperability, operationalization, and governance to realize their full societal value. Technological maturity in sensors, communications, and analytics now enables systems that can detect a broad range of hazards with higher fidelity and lower latency than before. Yet capability alone does not guarantee impact; success depends on integration into decision-making workflows, sustained maintenance, and institutional preparedness to act on early warnings.
Looking ahead, organizations that prioritize modular architectures, diverse supply chains, and human-centered operational design will be best positioned to improve resilience and reduce response times. Moreover, cross-sector collaboration-spanning public agencies, private operators, and community responders-remains essential to scale effective solutions while maintaining trust and accountability. The conclusion consolidates the strategic direction: align technological choices with operational realities, invest in people and processes, and pursue partnerships that embed detection capabilities into actionable resilience pathways.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
182 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of edge computing and AI driven analytics for real time seismic and flood detection systems
- 5.2. Deployment of low power wide area network sensors for continuous wildlife and ecosystem stress monitoring
- 5.3. Adoption of satellite based IoT connectivity for remote area earthquake and landslide early warning
- 5.4. Use of drone mounted multispectral sensors for rapid post disaster damage assessment and recovery planning
- 5.5. Implementation of blockchain enabled data integrity frameworks for secure disaster event reporting and coordination
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Natural Disaster Detection IoT Market, by Component
- 8.1. Hardware
- 8.1.1. Computational & Storage Devices
- 8.1.2. Data Transmission Devices
- 8.1.3. Power Supply & Energy Storage
- 8.1.4. Sensors & Detectors
- 8.1.5. User Interface & Notification Systems
- 8.2. Services
- 8.3. Software
- 8.3.1. Communication & Networking Software
- 8.3.2. Data Analysis & Management Software
- 8.3.3. Geographic Information System (GIS) Software
- 9. Natural Disaster Detection IoT Market, by Technology
- 9.1. Advanced Computing & Big Data Analytics
- 9.2. Artificial Intelligence & Machine Learning
- 9.3. Mobile & Communication Technologies
- 10. Natural Disaster Detection IoT Market, by Application
- 10.1. Drought Detection & Management
- 10.2. Earthquake Management
- 10.3. Flood Monitoring & Management
- 10.4. Forest Fire Management
- 10.5. Landslide Detection & Management
- 10.6. Weather Monitoring
- 11. Natural Disaster Detection IoT Market, by End-User
- 11.1. Government Organizations
- 11.2. Law Enforcement Agencies
- 11.3. Private Companies
- 11.4. Rescue Personnel
- 12. Natural Disaster Detection IoT Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Natural Disaster Detection IoT Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Natural Disaster Detection IoT Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. ABB Ltd.
- 15.3.2. Accenture PLC
- 15.3.3. ALE International SAS
- 15.3.4. Aplicaciones Tecnológicas S.A.
- 15.3.5. AT&T Inc.
- 15.3.6. Atos SE
- 15.3.7. BlackBerry Limited
- 15.3.8. Cisco Systems Inc.
- 15.3.9. Eaton Corporation PLC
- 15.3.10. Environmental Systems Research Institute, Inc
- 15.3.11. Google LLC by Alphabet Inc.
- 15.3.12. Green Stream Technologies, Inc.
- 15.3.13. Grillo Holdings Inc.
- 15.3.14. Hala Systems, Inc.
- 15.3.15. Hitachi Ltd.
- 15.3.16. InfiSIM Ltd.
- 15.3.17. Infosys Limited
- 15.3.18. Intel Corporation
- 15.3.19. International Business Machines Corporation
- 15.3.20. Knowx Innovations Pvt. Ltd.
- 15.3.21. Mitsubishi Electric Corporation
- 15.3.22. NEC Corporation
- 15.3.23. Nokia Corporation
- 15.3.24. One Concern, Inc.
- 15.3.25. Optex Co., Ltd.
- 15.3.26. OroraTech GmbH
- 15.3.27. Responscity Systems Private Limited
- 15.3.28. Sadeem International Company
- 15.3.29. SAP SE
- 15.3.30. Scanpoint Geomatics Ltd.
- 15.3.31. Semtech Corporation
- 15.3.32. Sony Group Corporation
- 15.3.33. Telefonaktiebolaget LM Ericsson
- 15.3.34. Tractable Ltd.
- 15.3.35. Trinity Mobility Private Limited
- 15.3.36. Venti LLC
- 15.3.37. Zebra Technologies Corporation
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