The increasing demand for AI applications, service robots and the popularity of IoT are the main growth drivers for the intelligent hardware market. Optimum energy consumption, increased efficiency and high data and instruction transmission frequency will become the main factors predicting increased demand in the near future. The IoT market, which was valued at $6.44 billion in 2018, is expected to grow to $18.68 billion by 2024 at a compound annual rate of 19.72%.
The birth of intelligent objects
More than a decade has passed since the number of devices connected to the Internet surpassed the number of people on the planet. This milestone marked the emergence and rise of the Internet of Things (IoT) paradigm, intelligent objects, which enabled a new range of applications that leverage the data and services of the billions of connected devices.
Today, IoT applications are disrupting entire sectors in both consumer and industrial environments, including manufacturing, energy, healthcare, transportation, public infrastructure and intelligent cities.
However, despite its scalability and intelligence, most IoT implementations tend to be passive with limited interactions with the physical world, which has been a serious setback on the road to achieving IoT’s greatest potential in the next decade.
In order to enable better functionalities, there has recently been a boom and proliferation of IoT applications that take advantage of artificial intelligence and intelligent objects.
Intelligent hardware is characterized by its ability to execute logic in a semi-autonomous manner that decouples from the centralized cloud. In this way, they can reason about the environments around them and make optimal decisions that are not necessarily subject to central control. Therefore, intelligent objects can act without the need to always be connected to the cloud. However, they can conveniently connect to the cloud when necessary, to exchange information with other passive objects, including information about their status.
As mentioned above, the number of sectors in which IoT devices have been implemented is wide and varied, as are intelligent objects, among which the most prominent:
- Social work robots: providing training or assistance to special user groups, such as older people with motor problems and children with disabilities.
- Industrial robots: that complete laborious tasks (Collecting and packing, etc.) in warehouses, manufacturing floors and power plants.
- Intelligent machines: that predict and anticipate their own failure modes, while at the same time scheduling relevant maintenance and repair actions autonomously (e.g. spare parts orders, visits by programming technicians).
- Connected vehicles: which collect and exchange information about their driving context with other vehicles, pedestrians and road infrastructure, as a means of optimizing routes and increasing safety.
- Intelligent pumps: operating autonomously to identify and prevent leaks in the water management infrastructure.
Artificial Intelligence is one of the main drivers of this new paradigm of IoT implementation, as it provides the means to understand and reason about the context of intelligent objects.
While AI functionalities have existed for decades in various forms (e.g. expert systems and fuzzy logic systems), AI systems have not been adequate to support intelligent hardware that could act autonomously in open and dynamic environments, such as industrial plants and transport infrastructures.
This is likely to change due to recent advances in artificial intelligence based on the use of Deep Learning which employs advanced neural networks and provides reasoning functionalities similar to those of humans.
Over the last couple of years, the first tangible demonstrations of AI capabilities applied to real-life problems have taken place. For example, last year, Google’s Alpha AI engine managed to win a Chinese grandmaster in the game Go.
This marked an important milestone in Artificial Intelligence, as human-type reasoning was used instead of an exhaustive analysis of all possible movements, as was the norm in previous artificial intelligence systems in similar environments.
Implications of convergence AI and IoT
This convergence of IoT and AI marks a paradigm shift in the way IoT applications are developed, implemented and operated. The main implications of this convergence are:
- Changes in the architectures of IoT:
Smart objects operate autonomously and are not subject to the control of a centralized cloud. This requires revisions of conventional cloud architectures, which should be able to connect to intelligent objects on an ad-hoc basis to exchange knowledge about their state and the physical environment.
- Expanded use of Edge Computing:
Edge Computing is already implemented as a means to allow operations very close to the field, such as fast data processing and real-time control. Smart objects are also likely to connect to the very edge of an IoT implementation, leading to an expanded use of the edge computing paradigm.
The AI will enable a wide range of new IoT applications, including some killer applications such as autonomous driving and predictive maintenance of machines. It will also revolutionize and interrupt existing IoT applications. As a prominent example, the introduction of smart appliances, e.g. washing machines that are maintained and tidy with their detergent, in residential environments holds the promise of disrupting the smart residential market.
PdM is emerging as one of the most important “killer” applications for industrial IoT, which is evident not only from its potential savings but also from the increase in relevant products and services based on IoT.
- Security and privacy challenges:
Smart objects increase the volatility, dynamism and complexity of IoT environments, leading to new cybersecurity challenges. They will also enable new ways of compromising citizens’ privacy. Therefore, new ideas will be needed to safeguard security and privacy in this emerging landscape.
- New standards and regulations:
A new regulatory environment will be needed, as intelligent objects could change the state of the physical environment and cause damage, loss and potential liability that do not currently exist. New standards will also be required in areas such as security and interoperability.
This need to create new standards is given mainly by the amount of data stored, taking as a reference the latest forecasts which state that the amount of data generated by devices connected to the Internet of Things will increase to 41.6 billion by 2025, generating 79.4 zettabytes (ZB) of data.
Artificial Intelligence and intelligent objects will offer unprecedented opportunities for innovative new applications and revenue streams. These will not be limited to giant suppliers and service providers, but will extend to innovators and SMEs (small and medium-sized enterprises).
For example, the startup Plantae designs and develops wireless sensors to optimize irrigation in agriculture and professional gardening. These devices use radio frequency technology and GPRS to measure and send to the cloud in real time data on humidity, temperature and soil conductivity. The information is accessible from any mobile device, which allows to optimize the irrigation saving water and energy.
Cyberphysical systems
AI is the cornerstone of the next generation of IoT applications, which will exhibit autonomous behavior and be subject to decentralized control. These applications will be driven by advances in Deep Learning and neural networks, which will endow IoT systems with capabilities that go far beyond conventional data mining and IoT analysis.
These trends will be driven by other technological advances, including Cyberphysical Systems (CPS). CPS systems represent an important class of intelligent objects, which will increasingly be used in industrial environments.
They are the basis of the fourth industrial revolution that through the union of physical processes with digital systems controls and manages industrial processes. Currently, CPS systems have limited intelligence, which must be improved depending on the arrival and evolution of Deep Learning.
According to the latest reports, the global market for Cyberphysical Systems is expected to witness a compound annual rate of 8.7% until 2028. In 2017 the market had a value of $55,075.3 million and is expected to reach a value of $137,566.0 million by the end of 2028.
The Blockchain to the rescue again
On the other hand, Blockchain technology can provide the means to manage interactions between intelligent objects, IoT platforms and other large-scale IT systems. Blockchain can enable the establishment, auditing and execution of intelligent contracts between objects and IoT platforms, as a means of controlling the semi-autonomous behavior of the intelligent object. This will be the preferred approach to managing intelligent hardware, since the latter belongs to different administrative entities and should be able to interact directly in a scalable manner, without the need to authenticate against a trusted entity such as a centralized cloud platform. Thanks to all the benefits it offers, the market for the union between Blockchain and IoT is expected to reach 113.1 million dollars, increasing its value to 3,021 billion dollars in 2024.
Conclusions
In terms of possible applications there is no limit. Artificial Intelligence will enable innovative IoT applications that will increase automation and productivity while eliminating error-prone processes. Thanks to innovative technologies such as Cyberphysical Systems and Blockchain, intelligent objects will cover a much wider market. These technologies will facilitate implementation, providing greater security and accessibility.
As IoT continues to expand, the need for a legal framework to ensure proper behaviour in the creation, storage, use and disposal of information related to IoT projects will become increasingly important. As these new directions take root, intelligent hardware will continue to demonstrate its advantages in sectors such as manufacturing and technology.
