+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)

PRINTER FRIENDLY

Internet of Things Development Survey 2020, Volume 1

  • ID: 5157024
  • Report
  • September 2020
  • Region: Global
  • 208 Pages
  • Evans Data Corp

The Internet of Things Development Survey is conducted twice a year. This comprehensive report, based on primary research with developers actively developing for connected devices and the Internet of Things, gives a comprehensive view of the attitudes, adoption patterns and intentions of these developers.

Topics include: Demographics and Firmographics, Developing and Deploying IoT Projects, IoT Technology Landscape, Business of IoT Development, IoT APIs and Services, Language Use, Platform Targets, Automotive and Connected Car Development, Wearables Development, Real-time Operating Systems, Connected Hardware Development, AI and Advanced Analytics, Cloud and Container Use, Edge Computing and IoT, Performance and Optimization, Security and Technology Adoption.

This survey gives a comprehensive view of the attitudes, adoption patterns and intentions of developers in relation to the Internet of Things. The analyst wishes to make this survey series as valuable as possible to our clients; thus, we solicit input from subscribers prior to the publication of each volume. This subscriber input is incorporated into the content of the survey, providing answers and insight into issues of interest to our clients. Publication rights to any of the results are not granted to any subscribers outside of their own companies without written permission from the analyst.

Survey Methodology

This survey series is completed entirely online. Respondents from the panel were sent invitations to participate and complete the survey online. Incentives for completing the survey are the ability to influence tool makers and receive points that build up and can be used to redeem cash cards.

Research Design

The survey research method is the basic research design. The questionnaire for this survey is constructed for developers actively involved with developing a variety of applications using a wide variety of technologies. An e-mail invitation was sent out to software developers inviting them to come to the survey site, fill out the survey online, and register for the drawing. Verbatim of any appreciable length was not used in this volume. The answers were compiled in SPSS.

Relative Rankings

In order to facilitate better at-a-glance comprehension of complex data sets,  the analyst provides relative ranking tables next to summary sheets. These rankings have a numerical weighting for the various categories. For example, in a question where the possible answers are w, x, y, and z, w is multiplied by 3, x by 2, y by -2, and z by -3. The sum of these comprises the relative ranking for the category in question. The results are then sorted, from highest-ranking to lowest, to give a closer comparison. The Sample - IoT Developers  This survey consists of 519 in-depth interviews conducted with English-speaking developers worldwide who work on apps that run on connected devices or the Internet of Things. This provides a margin of error of 4.2%.

Note: Product cover images may vary from those shown

Overview

  • Objectives of the Survey
  • Survey Methodology
  • Research Design
  • Relative Rankings
  • The Sample - IoT Developers  

What’s New

  • The EDC Panel
  • Custom Surveys
  • Targeted Analytics

Executive Summary

1. Profiling the IoT Developer

  • Involvement in Software Development
  • Primary Focus of IoT Development
  • IoT’s Organizational Fit  
  • Distribution of IoT Product Team
  • IoT in Moonlighting Efforts  
  • Company Size
  • Team Size
  • Company Age
  • Industry
  • Target Verticals for IoT Deployments

2. Developing and Deploying IoT Projects

  • Primary Host Operating Systems
  • Other Host Operating Systems Used
  • Length of Typical Development Lifecycle
  • Most Time Consuming Development Phases
  • Top Reasons for Delays in Early Development
  • Current Challenges in IoT Development  
  • Anticipated Challenges in IoT  
  • Device Cloud Use in Testing
  • Use of Emulators and Simulators in Testing
  • Use of Sandboxes for Testing  
  • Frequency of Updating IoT Apps
  • Likely Method of Delivering Updates to Devices

3. The IoT Technology Landscape

  • Anticipated Adoption of Emerging Technologies
  • Reasons for Targeting Emerging Technologies
  • Initial
  • Most-used Resources for Learning about IoT Tools and Platforms
  • MainIoT Tool or Platform
  • Most Critical Platform Support

4. Business of IoT Development

  • Involvement in Tool Purchasing
  • Top Stakeholders in IoT Tool Purchasing
  • Obstacles to IoT Innovation
  • Focus on Enterprise versus Consumer Development
  • Outside Sales of Applications  
  • Monetization of IoT Development
  • Greatest

5. IoT APIs and Services

  • Delivery of Services via Apps
  • Integration of APIs from Vendors  
  • API Access Features
  • Symmetry in APIs
  • Use
  • Importance of Features in Mobile LBS Platform Selection  
  • Access Methods for LBS Data Types  
  • Most Important Use Cases for Location-based Services

6. Language Use

  • C/C++ Use
  • C# Use
  • Objective C Use
  • Swift Use
  • Java Use
  • Scala Use
  • Go (golang) Use
  • Kotlin Use
  • Scripting Language Use
  • Other Language Use

7. Platform Targets

  • Symmetry in Operating Systems and Runtimes
  • Plans for Targeting Mobile Devices
  • Mobile Device Targets
  • Plans for Targeting Specific Mobile Platforms
  • TargetDesign  
  • Versions of Windows Targeted
  • Versions of Windows Server Targeted  
  • Linux Distros Targeted
  • Versions of Android Targeted
  • Versions of Green Hills Targeted
  • Versions of VxWorks Targeted
  • Versions of QNX Targeted

8. Automotive and Connected Car Development

  • Plans for Developing
  • Likely In-vehicle Platform Targets  
  • Preferred Delivery of
  • Interest in Smart Assistants for Auto Deployments  
  • Nature of Automotive Development
  • Anticipated
  • Greatest Challenge
  • Barriers to Targeting Automotive Platforms
  • Barriers to Targeting Automotive Platforms by Plans for Automotive Deployment

9. Wearables Development

  • Plans for Types of Wearables Targeted  
  • Initial Wearable Platforms Targeted  
  • Monitoring of Functions in Wearables Projects
  • Top Concerns for Wearable Device Deployments

10. Real-time Operating Systems

  • Development for Real-time Operating Systems
  • Optimization of Real-time Projects for Hardware Architectures
  • Expected Development of Real-time Control Projects
  • Processor Architectures Used with Embedded Systems Designs  
  • Absolute Target Performance
  • Approaches to Measuring Absolute Target Performance
  • Absolute Target Performance in Terms of Edge Acceleration
  • Worst Acceptable Case for Real-time in Terms of Determinism

11. Connected Hardware Development

  • Connected Device Hardware Development
  • Languages Used for Embedded Designs  
  • Flexibility Requirements of Multi-CPU Cluster Architectures
  • Top Requirements for Infrastructure and Backbone Support
  • CPU/Vendor Infrastructure for Device- and Application-level Security  
  • Requirements for Functional Safety Support  
  • Importance of Requirements
  • Hardware, Architecture and IP Considerations for IoT Development  
  • Dependence
  • Location of C or C++ Code in Hardware Development
  • CPU Vendor Lock-in in the Ecosystem  
  • Biggest Barrier of Porting Application to a Different Architectures
  • Most Difficult
  • Tools for Addressing Processors and Accelerators
  • Future-Proofing Single Purpose Devices

12. AI and Advanced Analytics

  • Use of Big Data
  • Reasons for Leveraging Big Data in IoT Projects
  • Timeline IoT Projects  
  • Preferred IoT Projects
  • Best AI  
  • Methods Used for Optimizing Inference  
  • Machine Learning Libraries Used with IoT Projects
  • Use Cases Supported for AI Projects
  • Typical Location for Inference
  • Distinguishing jobs
  • Machine Learning Use: Day Jobs versus Outside Development
  • Types of Frameworks Used in Deploying ML Workloads to the Cloud
  • Familiarity with AI Frameworks
  • Why Developers Frameworks
  • AI Tools Use
  • Approaches for Targeting Hardware with AI Applications

13. Cloud and Container Use

  • Plans for Platform Use in IoT Implementation
  • IoT Middleware Offerings Used  
  • Use of Containers for Deployment
  • Deploying Independent VM Workloads
  • Use of Containers in Specific Environments
  • Benefits of Using Containers for IoT  
  • Anticipated Use of Serverless Computing

14. Edge Computing and IoT

  • Plans Computing
  • Top Technical Barriers to Developing Edge Infrastructure Solutions  
  • Top Technical Barriers to Edge Infrastructure by Plans for Developing for Edge Computing
  • Percentage
  • Workloads Used in Edge Computing
  • Top
  • Verticals Targeted by Percentage
  • Levels of Virtualization Targeted at the Edge
  • Types of Artifacts Deployed to the Edge  
  • Deploying to Edge Computing Solutions

15. Performance and Optimization

  • Optimization
  • Optimization for Specific Processors
  • Use
  • Graphics Performance Optimization in IoT Projects
  • Use
  • Performance and Power Tuning Methods
  • Most Difficult Performance Problem to Diagnose
  • Processor Optimizations in IoT Projects
  • Performance Profiler Use in IoT Projects  
  • Tools for IoT Performance Optimization  
  • Use of Math Libraries in IoT Projects
  • Math Libraries Used in IoT Projects
  • CUDA Libraries Used in IoT Projects  
  • Functions Addressed by Math Libraries

16. Security

  • Optimization for Security
  • IoT-related Security Breaches  
  • Most for IoT Apps
  • Government Mandated Protocols for Authentication
  • Trust in Data Security and Privacy  

17. Technology Adoption

  • Sensor Use
  • Communication Protocol Use
  • Connectivity Protocol Use
  • Technology Adoption
  • IDEs Used for Embedded/IoT/Edge Development
Note: Product cover images may vary from those shown

Loading
LOADING...

Adroll
adroll