Internet of Things: Trends and challenges for future
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This document discusses trends and challenges in the Internet of Things (IoT). It covers several topics: briefly defining IoT; challenges and opportunities for startups in areas like security, privacy, integration; key research directions including massive scaling, knowledge and big data, openness, and humans in the loop; and final thoughts on the potential of IoT and IoT startups to develop whole solutions and services.
![Index
• Motivation
• Briefly about IoT [source]
• The IoT: Challenges and Opportunities [source]
• Research Directions [source]
• Final thoughts [source]
• Discussion](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Internet of Things: Trends and challenges for future
- 1. Internet of Things: Trends and Challenges for Future Kick-off Workshop Madrid, 21 October 2015
- 2. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 3. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 5. A brilliant future for IoT startups • The future of IoT is more open than ever for entrepreneurs or startups. • General Electric, Google, Apple, Microsoft, IBM, Oracle. SAP, Cisco are announcing their IoT strategies for consumer and enterprises: good news for IoT startups.
- 7. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 13. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 14. Drivers of Change as IoT evolves Smaller, lower power, less expensive devices allows for more distributed networks. This enables us to gather more granular data much faster. Big Data will accelerate the need for better analytics. Decision making. IoT devices (consumer & business sectors) spawn new uses cases, applicatioms, architectures, protocoles and standards. The new use cases will spur new business models, opening new markets and opportunities. Many companies will morph from pure Hw/Sw into service companies that provile whole solutions.
- 16. Key Challenge Areas Startup perspective Challenges will provide new business oportunities for technologies companies, middleware and tools developers, system integrators, device builders and cross-platform integration platform,
- 17. 5 Key Challenge Areas Startup perspective • Security: The more devices the more entry points for malware. More layers of Sw, APIs, new security risks. • Trust and Privacy: With remote sensors and monitoring a core use case, there will be heightened sensitivity to controlling access and ownership of data. • Unproven consumer needs: “While there is interest and curiosity on what a super smart refrigerator can do, the fact is that most consumers do not have a real need for such device”.
- 18. 5 Key Challenge Areas Startup perspective • Complexity & integration issues: IoT systems integration and testing will be a challenge. • Evolving architectures, protocol wars and competing standards: Too many players involved with the IoT => protection of proprietary systems & new standards from open systems proponents. • Concrete use cases & compelling value propositions: IoT providers will have to explain the key benefits of their services.
- 19. Some recommendations 1. Avoid the “technology driven trap”. Robust use cases to solicit early customer feedback. 2. Don’t wait for standards to gel. Provide new MVP using existing tools and protocols to gain immediate feedback. 3. Be nimble. Flexible approaches for new products, protocols & architectures changes that will come. 4. Track and engage with standards groups (AllJoyn, Open Internet Consortium, Thread,...). 5. Secure adequate funding (50% longer that expected). 6. Don’t sucumb to the “partial solution” trap. Customers want a whole solution.
- 20. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 21. Research Directions • Research in IoT relies on underlying technologies (real-time computing, machine learning, security, signal processing, big data, etc.). • Nowadays we have many examples of IoT deployments (buildings, vehicles, wearables, industry, healthcare,...). • But a qualitative change is needed.
- 22. 8 main topics areas (from the article “Research Directions for the IoT”, by J.A. Stankovic, Univ. of Virginia) 1. Massive Scaling. How to name, authenticate access, maintain, protect, use and support a large scale of things? Will IP6 suffice? Will protocols such as 6LowPAN play a role? Will entirely new standards and protocols emerge? Will energy scavenging and low power circuits eliminate the need for batteries? How will the massive amounts of data be collected, used, and stored? How real-time will be supported? What will be the architectural model that can support the heterogeneity of devices and applications? 1. Architecture and Dependencies. Trillions of things connected to the Internet need an adequate architecture that permits easy connectivity, control, communications, and useful applications. Research is needed to develop a comprehensive approach to specifying, detecting, and resolving dependencies across applications. Integrating multiple systems is very challenging as each individual system has its own assumptions and strategy to control the physical world variables without much knowledge of the other systems.
- 23. 8 main topics areas (cont.) 3. Knowledge and Big Data. New techniques to convert raw data in usable knowledge. Data interpretation (noise), Data Mining techniques, further inference techniques with confidence values. Make good decissions with the created knowledge (minimize false negatives and positives and guarantee safety). Data association ensuring that collected data and subsequent inferences are associated with the correct individual(s). 4. Robustness. Conditions can deteriorate over time, e.g. clock synchronization. Nodes put out of place (re-localization is needed). System tends towards disorder. Re-running protocols and self-healing mechanisms are needed. Control of actuators can also deteriorate due to physical conditions and controlling Sw/protocols. Services to support run time certification of safety.
- 24. 8 main topics areas (cont.) 5. Openness. Most sensor based systems have been closed system but these systems’ capabilities are expanding rapidly. Systems require openness to achieve benefits. New unified communication interfaces are required to enable efficient information exchange. Security and privacy => have a balance between access to functionality and security/privacy. Some work has been done (stochastic control, robust control, adaptative control,...) but not enough to support the degree of openness and dynamics of IoT systems. 6. Security. Security attacks are an issue because of openness, physical accesibility to sensors/actuators, wireless, etc. Updating the firmware also is a risk. Systems must adapt to attacks unanticipated when the system was designed. Detection, countermeasures and repairs must run in real-time as part of a runtime self-healing architecture. Significant Hw support will be needed for encyption, authentication, attestation, and tamper proof keys. Dealing with legacy devices will prove difficult.
- 25. 8 main topics areas (cont.) 7. Privacy. IoT will provide useful services for individuals but also create many opportunities to violate privacy. Privacy policies for each system/domain must be specified to grant or deny data access to users. New languages to express privacy policies are needed: different types of context, users, petitions of aggregated data through inference mechanisms, request to a set of system’s parameters, dynamic changes to policies, etc. 8. Humans in the Loop. Humans and things will operate synergistically in many domains (healthcare, energy management, automovile systems, etc.). New understanding of the spectrum of types of human-in-the-loop controls (4 categories: humans control directly the system, the system passively monitors humans, physiological parameters are modeled, hybrids of them). Extensions to system identification to derive models of human behaviors. Incorporate the human behavior as part of the system itself.
- 26. Index • Motivation • Briefly about IoT [source] • The IoT: Challenges and Opportunities [source] • Research Directions [source] • Final thoughts [source] • Discussion
- 27. Final thoughts The potential of IoT • IoT is a new wave of technology advancement in the early stages of market development. • Like many preceding waves it is characterized by innovation, fragmentation, confusion, competitive jostling and emerging standards. • The IoT will leverage, embrace and extend cloud, big data, mobiles and social networks to provide more granular sensors and devices. • New applications and use cases that will drive new business models and revenue opportunities. • It will also threaten many existing industries, markets and products. • Value will migrate from devices/components into “whole solutions” and services.
- 28. Final thoughts (cont.) The potential of IoT startups • Most IoT hardware entrepreneurs come from universities and laboratories. • They have been developing Arduino/Raspberry Pi Hw and embedded software using investment coming mainly from public sector. • Private investors are eager to enter these startups where the knowledge is difficult to find. • But...most of them may disappear due to absence of experience in making large-scale and robust designs. • Design hardware for prototype is fine but design for production consumed by thousands or millions is more difficult.
- 29. Final thoughts (cont.) The potential of IoT startups • For horizontal software-oriented startups (IoT platforms) the challenge is to attract a great number of industry application developers to use their tools and partner with M2M service providers (mainly network operators) that provide secure and robust connectivity services and bundle IoT services with others. • For industry solutions-oriented startups the challenge is to focus on developing a vertically integrated product. • For IoT service provider-oriented startups the challenge is partner with ISV and sensor/device/hw vendor to offer useful services and make sure the customer experience is excelent.
- 30. Final thoughts (really final!) • Technical background is important but will not be enough to convince investors. • Money will flow onto those companies that demonstrate a realistic strategy, reliable allicances in the ecosystem and a viable plan to execute the business model at big scale. • When it comes to putting money behind IoT, business need proof of viability and profitability more than buzz and excitement.
Editor's Notes
- According with Peter Sondergaard, senior vice president at Gartner and global head of research, “The incremental revenue generated by the Internet of Things’ suppliers is estimated to reach $309 billion per year by 2020.This growth opens up new business opportunities, as half will be attributed to new startups and 80 percent will be in services, not products”. According with Mr. Sondergaard, industries leading the digitalization of everything are manufacturing (15 percent), health care (15 percent) and insurance (11 percent).
- The future of the “Internet of Things” (IoT) is more open than ever for entrepreneurs or startups thinking about getting into the market. The fact that companies like General Electric, Cisco, IBM, Oracle, SAP, Apple, Google, Microsoft, Samsung and many others are announcing their IoT/M2M strategies for consumer and enterprises and the need they have to add their portfolios IoT products and services are good news for IoT startups.
- One of the most ambitious attempts to pull together the key players in the IoT space can be found in TechCrunch, courtesy of Matt Turck of FirstMark Capital. Originally compiled in 2013, their ecosystem is divided into verticals, enablers and building blocks. Like any attempt to make sense of such a complex web of players, it is imperfect – see ARM, Amazon and Sony grouped rather randomly into a ‘Corporates’ bucket. What is startling is that this version has increased the number of companies from 199 to 612, largely driven by start-up activity. For entrepreneurs, the opportunity is massive. Where Web 1.0-connected computers and their data and Web 2.0-connected people and their data, Web 3.0 is shaping up to be connecting just about everything else — things, plants, livestock, babies. Each new wave has spun out giant companies (Google and Amazon for Web 1.0, Facebook and Twitter for Web 2.0). Will Web 3.0 create a comparable pair of behemoths? The space has been evolving so rapidly over the last year and a half that the IoT landscape became quickly outdated.
- We are giving our world a digital nervous system. Location data using GPS sensors. Eyes and ears using cameras and microphones, along with sensory organs that can measure everything from temperature to pressure changes. These inputs are digitized and placed onto networks.
- Connected Devices § In 2014 nearly 2 billion connected devices will be shipped This number will grow to nearly 8 billion devices for the year 2020 Home (Consumer) Transport (Mobility) 392.72 Body (Health) 360.03 Buildings (Infrastructure) 1,726.59 Cities (Industry) 1,524.70
- These networked inputs can then be combined into bi-directional systems that integrate data, people, processes and systems for better decision making.
- The interactions between these SENSORS + CONNECTIVITY + PEOPLE + PROCESSES entities are creating new types of smart applications and services. Starting with popular connected devices already on the market . SMART THERMOSTATS CONNECTED CARS ACTIVITY TRACKERS SMART OUTLETS PARKING SENSORS nest Save resources and money on your heating bills by adapting to your usage patterns and turning the temperature down when you're away from home. CFIFI EGO Tracked and rented using a smartphone. Car2Go also handles billing, parking and insurance automatically. ”/ /% BASIS Continuously capture heart rate patterns, activity levels, calorie expenditure and skin temperature on your wrist 24/7. belkin Remotely turn any device or appliance on or off. Track a device's energy usage and receive personalized notifications from your smartphone. . ', STREETLINE CONNECTING THE REAL WORLD Using embedded street sensors, users can identify real-time availability of parking spaces on their phone. City officials can manage and price their resources based on actual use.
- There are many good examples of commertial solutions using part of the IoT potential.
- Smaller, lower power, less expensive devices allow for more distributed networks. We can now embrace not just computational devices but all types of devices and sensors, closer to “the edge” where we choose to deploy them. This enables us to gather more granular data, much faster. Machine sensors that years ago gathered data and stored it in a database for daily or weekly review can now report on conditions or even take immediate action in near real time. All this granular data is like Big Data on steroids. It will further accelerate the need for better analytics. It will also put a premium on asking insightful questions to provide actionable answers for decision making. IoT devices in both consumer and business sectors are now spawning new use cases, new applications, new architectures, new protocols and ultimately will drive new standards. Companies like Octoblu are emerging to address the need for cross-device integration. These new use cases will in turn drive different customer journeys and unique value propositions that will spur the creation of innovative, new business models. These business models will open up new markets and reinvigorate existing industries through creative destruction, providing new opportunities for the entire ecosystem. Many of these breakthroughs will force some companies to morph from being pure hardware, software or systems companies into service companies that provide “whole solutions.” Companies not adapting to the new realities fast enough will be acquired or wilt away.
- Like many evolving IT and networking technologies, the Internet of Things will encounter multiple barriers to adoption. Traditional inertia, budget priorities, risk aversion and other factors will prevent some companies from adopting IoT in the near future
- Expect to see early adopters led by innovative CIOs or by business leaders identify and pursue specific opportunities to better serve their customers, open new businesses, reduce costs and provide new value that results in increased revenues. In addition to the technical challenges around power, latency, integration and storage, there are a number of other issues critical to IoT adoption. These challenges will also provide new business opportunities for technology companies, middleware and tools developers, system integrators, device builders and cross-platform integration companies.
- Security. As the IoT connects more devices together, it provides more decentralized entry points for malware. Less expensive devices that are in physically compromised locales are more subject to tampering. More layers of software, integration middleware, APIs, machine-to-machine communication, etc. create more complexity and new security risks. Expect to see many different techniques and vendors addressing these issues with policy-driven approaches to security and provisioning. Data security is a very well-documented barrier to IoT and the most critical. It will take some time for consumers to become consciously ready to allow more and more devices to capture, store and transmit their personal data. Trust and Privacy. With remote sensors and monitoring a core use case for the IoT, there will be heightened sensitivity to controlling access and ownership of data. (Note that two recent high-profile security breaches at Target and Home Depot were both achieved by going through third-party vendors’ stolen credentials to gain access to payment systems. Partner vetting will become ever more critical.) Compliance will continue to be a major issue in medical and assisted-living applications, which could have life and death ramifications. New compliance frameworks to address the IoT’s unique issues will evolve. Social and political concerns in this area may also hinder IoT adoption. It’s unclear who will own and control the access to IoT data once it grows. Multiple issues related to privacy and misuse.
- - Complexity, confusion and integration issues. With multiple platforms, numerous protocols and large numbers of APIs, IoT systems integration and testing will be a challenge to say the least. The confusion around evolving standards is almost sure to slow adoption. The rapid evolution of APIs will likely consume unanticipated development resources that will diminish project teams’ abilities to add core new functionality. Slower adoption and unanticipated development resource requirements will likely slip schedules and slow time to revenues, which will require additional funding for IoT projects and longer “runways” for startups. - Evolving architectures, protocol wars and competing standards. With so many players involved with the IoT, there are bound to be ongoing turf wars as legacy companies seek to protect their proprietary systems advantages and open systems proponents try to set new standards. There may be multiple standards that evolve based on different requirements determined by device class, power requirements, capabilities and uses. This presents opportunities for platform vendors and open source advocates to contribute and influence future standards. - Concrete use cases and compelling value propositions. Lack of clear use cases or strong ROI examples will slow down adoption of the IoT. Although technical specifications, theoretical uses and future concepts may suffice for some early adopters, mainstream adoption of IoT will require well-grounded, customer-oriented communications and messaging around “what’s in it for me.” Detailed explanations of a specific device or technical details of a component won’t cut it when buyers are looking for a “whole solution” or complete value-added service. IoT providers will have to explain the key benefits of their services or face the proverbial “so what.”
- Avoid the “technology driven” trap. Develop and test robust use cases to solicit early customer feedback and rapidly determine whether to enhance or abandon early concepts. Don’t wait for standards to gel. Provide new “minimum viable products” (MVPs) offerings now using existing tools and protocols to gain immediate feedback. Be nimble. Use flexible, extensible, development approaches that can rapidly accommodate new products, protocols and architectural changes that are sure to emerge in coming months. Track and engage with standards groups (e.g., AllJoyn, Open Internet Consortium, Thread) and seek targeted partnerships with large technology companies to secure third-party credibility they impart as well as future visibility, distribution opportunities, strategic funding and potential acquisitions. Secure adequate funding that will last at least 50 percent longer than you think you need. Slower adoption, inertia and market confusion may slow initial sales and market development requiring your company to sustain itself longer than you imagine. This will be especially true if the IoT falls into the Gartner “Trough of Disillusionment” and paralyzes the IoT market as has happened to many other emerging technology waves. Don’t succumb to the “partial solution” trap. Your customers are seeking a complete solution or service. Although most of your expertise may lie in one area and you may want to restrict your efforts to focus on and build a specific device or capability, remember that your customers want a “whole solution.” If you can’t provide it, then ensure you deliver it by partnering with other IoT players. You may need to integrate with other technologies, partner with other companies and employ a service business model in order to deliver the optimal solution for your customers.
- Further, research in IoT often relies on underlying technologies such as real-time computing, machine learning, security, privacy, signal processing, big data, and others. Consequently, the smart vision of the world involves much of computer science, computer engineering, and electrical engineering. Greater interactions among these communities will speed progress. The purpose of this section is to highlight a number of significant research needs for future IoT systems, and (ii) to raise awareness of work being performed across various research communities. Today many buildings already have sensors for attempting to save energy; home automation is occurring; cars, taxis, and traffic lights have devices to try and improve safety and transportation; people have smartphones with sensors for running many useful apps; industrial plants are connecting to the Internet; and healthcare services are relying on increased home sensing to support remote medicine and wellness.However, all of these are just the tip of the iceberg. They are all still at early stages of development. The steady increasing density of sensing and the sophistication of the associated processing will make for a significant qualitative change in how we work and live. We will truly have systems-of-systems that synergistically interact to form totally new and unpredictable services.
- 2. While each application must solve its own problems, the sharing of a sensing and actuation utility across multiple simultaneously running applications can result in many systems-of-systems interference problems, especially with the actuators. Interferences arise from many issues, but primarily when the cyber depends on assumptions about the environment, the hardware platform, requirements, naming, control and various device semantics. Previous work, in general, has considered relatively simple dependencies related to numbers and types of parameters, versions of underlying operating systems, and availability of correct underlying hardware. Research is needed to develop a comprehensive approach to specifying, detecting, and resolving dependencies across applications. This is especially important for safety critical applications or when actuators can cause harm. However, integrating multiple systems is very challenging as each individual system has its own assumptions and strategy to control the physical world variables without much knowledge of the other systems, which leads to conflicts when these systems are integrated without careful consideration. For example, a home health care application may detect depression and decide to turn on all the lights. On the other hand, the energy management application may decide to turn off lights when no motion is detected. Detecting and resolving such dependency problems is important for correctness of operation of interacting IoT systems
- 3. In an IoT world there will exist a vast amount of raw data being continuously collected. It will be necessary to develop techniques that convert this raw data into usable knowledge. For example, in the medical area, raw streams of sensor values must be converted into semantically meaningful activities performed by or about a person such as eating, poor respiration, or exhibiting signs of depression. Main challenges for data interpretation and the formation of knowledge include addressing noisy, physical world data and developing new inference techniques that do not suffer the limitations of Bayesian or Dempster-Shafer schemes. These limitations include the need to know a priori probabilities and the cost of computations. Rule based systems may be used, but may also be too ad hoc for some applications. Trust is one important aspect of the usefulness of big data. Security and privacy are essential elements of trust and these are discussed in their own sections. However, as a basis for trust it is also necessary to develop new in-field sensor calibration techniques and reliable transport protocols. 4. The required coherence (entropy) services must combine with many other approaches to produce robust system operation. This includes formal methods to develop reliable code, in-situ debugging techniques, on-line fault tolerance, in maintenance, and general health monitoring services. These problems are exacerbated due to the unattended operation of the system, the need for a long lifetime, the openness of the systems, and the realities of the physical world. The goal is for this collection of solutions to create a robust system in spite of noisy, faulty and nondeterministic underlying physical world realities. Another problem barely addressed to date is that in some IoT applications, especially safety critical ones, run time assurances must be given to authorities, e.g., to (re)certify that the system is operating as expected. Consider a fire fighting system deployed in a sky scraper office building to detect fires, alert fire stations and aid in evacuation. Periodically, it is necessary to demonstrate to certification authorities that this system meets these requirements. Such IoT applications will need services that can support run time certification.
- 5. Consider feedback control. Many sensor and actuator systems heavily utilize feedback control theory to provide robust performance. The classical methodology includes creating a model of the system and then deriving a controller using well known techniques to meet stability, overshoot, settling time and accuracy requirements. A sensitivity analysis is also possible and strongly encouraged. However, openness and scale create many difficulties for this methodology. The openness means that the model of the system is constantly changing. The human interaction is an integral aspect of openness and this makes modeling extremely difficult, and the scaling and interactions across systems also dynamically change the models and creates a need for decentralized control. While some work has been performed in topics such as stochastic control, robust control, distributed control and adaptive control, these areas are not developed well enough to support the degree of openness and dynamics expected in some IoT sytems. A new and richer set of techniques and theory is required. 6. To heal from security attacks, a system needs to detect the attack, diagnose the attack, and deploy countermeasures and repairs, but perform all of this in a lightweight manner due to the types of low capacity devices involved. Most of today’s mainframe security solutions require heavyweight computations and large memory requirements, so solutions for IoT are major research challenges. Ideally, for a quick response, given the real-time nature of many IoTs, the detection, countermeasures and repairs must run in real-time as part of a runtime self-healing architecture.
- 7. One of the more difficult privacy problems is that systems interact with other systems, each having their own privacy policies. Consequently, inconsistencies may arise across systems in the IoT world. On-line consistency checking and notification and resolution schemes are required. 8. If we were to use system identification technique to model a human being who is suffering from depressive illness, it is not clear what are the inputs, what are the states and how the state transitions occur based on different physiological, psychological and environmental factors. If there was a formal model of human behavior or even an estimated model, then by combining all the factors that affect depression, we could close the loop by changing the factors in a way that helps the patients and that is based on an established methodology rather than ad hoc rules. Clustering, data mining, inference, first principle models based on human physiology and behaviors may all be necessary techniques to be enhanced and applied for different applications. Robust systems will likely require predictive models to avoid problems before they occur. Advances to stochastic model predictive control are also required. It is also unlikely that any models developed initiallyto design the controllers will remain accurate as the system and human behaviors evolve over time. Hence, adaptive control with humans-in-the-loop will be necessary. As a summary of this section: New research problems arise due to the large scale of devices, the connection of the physical and cyber worlds, the openness of the systems of systems, and continuing problems of privacy and security. It is hoped that there is more cooperation between the research communities in order to solve the myriad of problems sooner as well as to avoid re-inventing the wheel when a particular community solves a problem.
- The Internet of Things is a new wave of technology advancement in the early stages of market development. Like many preceding waves of technology evolution it is characterized by innovation, fragmentation, confusion, competitive jostling and emerging standards. Startups are shaking up the status quo as established technology companies react and adjust. The IoT will leverage, embrace, extend and enhance cloud, big data, personal/mobile devices and social networks to provide more granular sensors and devices closer to the “edge.” As it does so, it will provide entirely new applications and use cases that will drive new business models and revenue opportunities. It will also threaten many existing industries, markets and products. It will likely collide and impact adjacent disrupting trends and markets. For example, the IoT has the potential to further accelerate the “sharing economy.” By providing new ways to track and manage smaller things, it will enable the sharing of new, smaller and less expensive items beyond houses, planes, cars and bikes. In some ways the IoT is the next logical extension of the “long tail” concept. It pushes devices and sensors to more granular levels and enables the creation of new uses, new applications, new services and new business models that were not previously economically viable. As the IoT evolves, much of the value will migrate from devices and components into “whole solutions” and services. Therein lie the opportunities for new value creation, new business models and new revenue streams for market participants. A bigger challenge than developing technology breakthroughs may be in answering the question “What problems can I solve with the IoT and what new value can I provide my customers?”
- Although many IoT startups are appearing worldwide most IoT hardware entrepreneurs come from universities and laboratories. They have been developing Arduino or Raspberry Pi hardware and embedded software using investments coming mainly from the Public Sector. Private investors are eager to enter these startups because they believe that their engineers have the knowledge that is not easy to find out there and therefore it give them a competitive advantage. Unfortunately what I have observed is that there are not big differences in their designs and therefore most of them will disappear due to absence of experience in making large-scale scalability and robustness of their designs. Design hardware for prototype is fine but design for production consumed by thousands or millions is another history.
- For startups focused on software it depends if they are focus on horizontal technology (IoT Platforms) or industry solutions. The challenge for the first is attract a great number of industry application developers to use their development tools environment and partner with M2M/IoT Service Providers (mainly Network Operators) that provide secure and robust connectivity services and can bundle IoT services with other digital services to make end users accept to pay for the bundle. The challenge for the second is that they need to either focus on building a somewhat vertically integrated “product” or focus on being an embedded background player providing services or technology for established brands. For IoT Service Providers startups the challenge is partner with ISVs and Sensor/Device/Hardware Vendor to offer useful services to specific industries and make sure the customer experience is excellent. Low margin is inherent to IoT business models, so customer loyalty is king.
- For startups focused on software it depends if they are focus on horizontal technology (IoT Platforms) or industry solutions. The challenge for the first is attract a great number of industry application developers to use their development tools environment and partner with M2M/IoT Service Providers (mainly Network Operators) that provide secure and robust connectivity services and can bundle IoT services with other digital services to make end users accept to pay for the bundle. The challenge for the second is that they need to either focus on building a somewhat vertically integrated “product” or focus on being an embedded background player providing services or technology for established brands. For IoT Service Providers startups the challenge is partner with ISVs and Sensor/Device/Hardware Vendor to offer useful services to specific industries and make sure the customer experience is excellent. Low margin is inherent to IoT business models, so customer loyalty is king.