Merge of AI and IoT is an great tool whether you apply it in edge or cloud computing

09 Jul

Back in 2016, the Internet of Things or IoT came into the foray of the mainstream consumer market with some of the latest innovations such as smart appliances, smart thermostats and even smart home solutions.

In the same regard, Artificial Intelligence or AI is certainly not new anymore and the different company now utilizing it to improve its processes and benefitting on a daily basis. However, what about the merging of both of these technologies? This magical combination needs to get much wider attention since it is quite a powerful playbook for any AI development company.

IoT

It is essentially a collective term for different technologies of a massive global infrastructure of the information communities which make it quite possible to network different physical and virtual objects directly with each other and subsequently let them cooperate via information as well as communication technologies. 

Different functions implemented with various technologies of IoT allow the interaction between any electronic systems networked with the human along with between the systems themselves. Also, they can easily support people in their daily activities. 

Even the ever-smaller embedded computers and devices are essentially designed to encourage the people without actually distracting or attracting attention. For instance, miniaturized computers or wearables are directly integrated into the garments utilizing different sensors. An IoT app development company can leverage the growing capabilities of IoT through innovative app development processes.

AI

Basically, it is a branch of computer science which actually deals with the automation of both machine learning and intelligent behaviour. The term cannot actually be clearly defined in so far as there is certainly a lack of precise definition of the term “intelligence”. However, it is utilized in research and development. Generally, artificial intelligence actually refers to the inherent attempt to reproduce particular human decision-making structures. For instance, building as well as programming a computer in a particular way that it can easily work on different problems independently in relative terms. Also, this is also referred to as another term known as imitated intelligence, where most simple algorithms are utilized to stimulate the human’s intelligent behaviour, for instance, in computer games.

The overall understanding of this term generally reflects the actual Enlightenment ideal of popular, “man as machine”. Actually the imitation of which is aimed at the AI in order to create knowledge to easily mechanize human thinking or to readily construct as well as build a machine which reacts or behaves like a human being, in an intelligent fashion.

Fusion of AI and IoT

Essentially, the combination of AI and IoT is one of the significant keys to accelerate technological development as well as enabling disruptive services in the digital domain. 

The entire digital information collected by these machines, devices as well as sensors of the IoT can easily be efficiently analysed and even contextualized through AI technologies in cloud computing services.

It would certainly enable both the decision making as well as the provision of the entire personalized experiences to the users and will easily improve them significantly. Also, the more productive as well as fulfilling interaction between humans and even the environment can easily be promoted in a significant fashion.

Also, the rapid advances in AI which are driven by increasing computing capacity, along with the training of the data scientists as well as the availability of different machine learning tools in order to develop advanced algorithms, are now actually moving the efficient and effective use of IoT into the realms of sustainable and practical suitability.

AIoT actually involves embedding AI technology with different IoT components. This merge of both AI, as well as IoT, is quite a great tool whether you actually apply it in the edge or cloud computing. Basically, its objective is to rapidly increase operational efficiency along with improving the human-machine interactions and even upgrade data management as well as for analytics. Whenever utilized the right way, AI can easily transform the entire IoT data into quite valuable information for improvised decision-making, both remotely and on-site. AI executed at the edge provides an incredible computing approach in order to offer local data-informed and backed decision-making.

Read the blog- List of multiple ways in which IoT is changing the way transportation takes place in 2020

AI to the Edge

“AI act now” quote shows how the AI enables the devices to act as well as react to different occurrences at any given instant and in real-time. Few of the most popular AI-powered edge devices are the smart car sensors, drones, robots and surveillance cameras. To put more in perspective, AI assists self-driving cars as well as ships to manoeuvre via a busy as well as crowded traffic without actually crashing into other objects, moving or static. AI can easily detect anomalies in the entire production process before it actually starts to cost the manufacturer a huge sum of money. Also, lowering the latency is becoming important in a progressive manner. Intervening now with artificial intelligence can easily provide great outcomes.

Different systems as well as devices which monitor, diagnose and even take action on different pieces of equipment, such as home automation systems, it certainly makes a sense to perform the analysis closer to the device. Such applications can’t actually wait for data or any command from the cloud. Also, sending locally created as well as locally consumed data to the cloud often causes costly network traffic along with delays in decisions and finally the drain on battery-powered devices. A software development services company need to consider these aspects.

Owing to the vast increase in these IoT devices along with massive data volumes which are coupled with simultaneous demand for lower latency, there is a trend to move analytics from the actual cloud toward devices in the edge. It leads to analytics to be quite closer to the intelligent things along with data sources as well as the environment they are actually in.

Advantages of Edge 

Lesser Bandwidth with Faster Results

Basically, edge computing can easily avoid having to regularly send data to the cloud and actually achieve low latency, that offers a company with swifter real-time context awareness, decision-making and intelligence. It is quite important for the applications where the real-time response is vital and the devices make different AI-based decisions like autonomous driving.

Predictive Analysis

It utilizes a model trained by the entire historical data in order to predict conceivable outcomes in future. At present, IoT devices are generally used by companies to report concerns and incidents without human intervention such as equipment failure. By performing this analysis on the machines, AI is applied to the process. It allows companies to identify different potential problems before failures which enable them to take proactive measures in order to optimize uptime.

Read the blog- How An IoT Based Warehouse Management System Works?

Security

When it comes to the cloud, the security threats are always there, and the sensitive information is quite accessible from all of the endpoints. Hence, edge computing creates a much safer distance from different threats by storing the data locally. Also, an AI-powered solution can easily be utilized to identify any malicious signature at the edge of the given system. In case a cyber-attack targets few IoT devices, the entire edge AI system can easily and quickly execute different countermeasures as well as safeguard the system.

Collective intelligence

Different smart devices, along with connected environments, can easily learn from the massive network of data sources as well as each other and readily create collective intelligence. At present, there are various instances across industries which illustrate the true potential. For instance, situational awareness detection, as well as a vehicle to vehicle communication, powered smart traffic solutions for the different vehicles. Different logistic service providers with various assets can easily gather data on humidity, temperature, weight, VOC levels as well as air quality etc. in order to maintain the correct condition of the cargo. Cloud integration solutions are now rapidly adopted in this industry, owing to this collective intelligence.

Simultaneous Mapping and Localization 

Different drones can easily interpret the various unknown surroundings while flying as well as mapping the entire environment, during the loss of connection from the internet. It enables in the investigation of hazardous areas like mines, offshore operations, or difficult reachable infrastructures.

Identical Digital Twins

They are essentially virtual simulations of real-world assets like machines or even the wind turbines which are equipped with sensors. Basically, they allow the engineers as well as the people with operational responsibility to readily analyse the equipment performance in the real-world, meanwhile reducing the overall cost and different safety elements of normal equipment testing methods.

Autonomous Robotic Platforms

Such robots easily map the entire environment, detect different obstacles, other devices and even humans. They can easily drive in autonomous mode through large warehouses while picking goods off the different shelves and deliver them to the exact place and even reroute in case of an obstacle.

Conclusion

Edge computing actually creates latest possibilities for the devices and systems by acting on a large volume of data immediately, in the real-time, right at the source and without any particular security danger of transportation and even remote storage in the cloud. Also, if every system or device are different, that needs different approaches to implement AI. 

Also, you have to actually keep in mind that at times not all of the data is quite relevant or requires to be sent to the corresponding cloud. In the case of different structures, different priorities are there. Sometimes the entire complexity of the analytics is quite important, and sometimes the entire focus is on the speed. It can easily benefit different systems to analyse the data right at the edge, without actually going back and forth to the immediate data centre. 

 Also, Artificial Intelligence solutions in these devices would generally be some amount of local inference along with the algorithms running as a particular program on a given processor, utilizing dedicated accelerators, via near-memory processing computing. Edge AI is slowly becoming a reality across different applications. 

There is a great opportunity in both industrial as well as building implementations where the AI can easily offer benefits via predictive as well as preventive maintenance, along with quality control in manufacturing and different other areas. When the older devices without the implementation of AI don’t actually intuitively understand our requirements, people get frustrated as we have other devices that can offer the intuitive capability. Also, the end consumer doesn’t know what actually goes into making any AI solution work, as they just expect it to work. In this regard, merging AI with IoT can be a great tool to apply in edge or cloud computing.