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International Journal for Modern Trends in Science and Technology (ISSN 2455-3778),, Volume 4, Special Issue 3, June 2018 18

Convergence of Internet of Things with Artificial Intelligence

K.H.Anuhya

Department of Electronics, Maris Stella College, Vijayawada, India e-mail:anuhya.90@gmail.com

Abstract - The world is being swept by a tsunami of data, which continues to surge as connected people and devices produce ever more. The data tsunami represents tremendous raw material to be analyzed for actionable insights. But the volume and complexity present a monumental hurdle.Without new, inventive approaches to tackle the ever-increasing data flow, the data flood will continue to overwhelm rather than empower.Internet of Things (IoT) has steered the automation to a new high. But IoT alone has limited capability.To reap the actual benefit of IoT, it has to be intelligent.

This is where artificial intelligence (AI) has an important role to play. AI can handle data challenges that conventional analytics cannot. AI rapidlyconsumes vast quantities of structured and unstructured data, and gives it meaning by creating models of entities and concepts, and the relationships among them. They generate hypotheses, formulate possible answers to questions, and provide predictions and recommendations, which can be used to augment human intelligence and decision making.

Keywords: Internet of Things, artificial intelligence, automation, key technologies

I. INTRODUCTION

Different technologies have different capabilities and limitations. Automation can help speed up workflows, but it can't tell what the customer wants. Sensors keep a watchful finger on the pulse of the machinery, but can't actually predict a breakdown.

However, connected machines are incapable of making much sense of the data.The role of the Internet of Things, which consists of connected edge-devices equipped with sensors, is analogous to the sensory organs which perceive and gather inputs. The core components of IoTconnectivity, sensor data and robotics will ultimately lead to a requirement for almost all devices to become intelligent. To realize the full potential of IoT, there is a need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable smart devices to simulate intelligent behaviour and make wellinformed decisions with little or no human intervention.In other words, the IoT needs smart machines. The ongoing advance of AI is also having a further impact.It‟s causing AI to converge with IoT, to the extent that it‟s rapidly becoming indispensable to IoT solutions.

II. INTERNET OF THINGS

The Internet of Things (IoT) is a vision, in which objects become part of the Internet, where every object is uniquely identifiable and accessible on the Web. These objects may directly or indirectly collect, process or exchange data via data communications network. IoT essentially is the network of connected things, sensors, actuators and communication technologies with specific hardware, software and architectural approaches to get data from myriad possible devices by communicating that data within the device environment.Getting data from connected devices and leveraging that data for a valuable reason which includes enabling people and connected devices to take an action based upon the analyzed and visualized data. So, it works in two directions from the physical device (machine, actuator etc.) perspective: from data to intelligent action and typically from actionable data to action within or by a thing.

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International Journal for Modern Trends in Science and Technology (ISSN 2455-3778),, Volume 4, Special Issue 3, June 2018 19 III. ARTIFICAL INTELLIGENCE

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction.

AI is playing a starring role in IoT because of its ability to quickly wring insights from data. It hastraditionallyimpliedthesimulation of logical human thinking using computer technology. AI simulates and offers the learning abilityofhumanbeings,i.e.,learningtounderstand and solve the problem. Therefore ,learning is the central feature in AI and learning technology is the key to handlingproblems.Several forms of AI have been used for literally decades in, essentially, making sense of data, locating data and putting data at work, with a focus on the rise of unstructured data.

IV. KEY ATTRIBUTES OF AI SYSTEMS

1. Data Ingestion - AI systems deal with voluminous amounts of data, often in excess of billions of records, coming in at high velocity.

2. Adaptive - AI systems adapt to their environment with machine learning. They observe their results and learn to do better.

3. Reactive - AI systems react to the changing conditions around them. Unlike traditional applications that are more batch- oriented, AI applications continuously monitor their inputs, often from streaming data platforms, and when certain conditions apply, they invoke procedures, rules, and behaviors, or compute scores and make decisions.

4. Forward-Looking - AI systems don‟t just react they often search through a space of possible scenarios to reach an effective goal. To do this, they are projecting multiple steps into the future.

5. Concurrent - AI systems, just like traditional applications, must handle multiple people or systems interacting simultaneously.

They use techniques adopted by those developing distributed systems in the fields of operating systems and databases.

V. AI TECHNOLOGIES AND CAPABILITIES

Over the years artificial intelligence has continued to expand its horizon. AI is experienced in several different ways with capabilities such as Machine Learning, Computer Vision, Natural Language Processing, and Deep Learning and so on.

Machine learning

Machine learning is a type of artificial intelligence that has become one of the most popular technology trends in the recent times. It furnishes computers with the ability to learn, without being explicitly programmed. Machine learning facilitates artificial intelligence by providing algorithms, APIs, development and training toolkits, and data. It has the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate information such as temperature, pressure, humidity, air quality, vibration, and sound. Machine learning can have significant advantages over traditional intelligence tools for analyzing IoT data, including being able to make operational predictions up to 20 times earlier and with greater accuracy than threshold-based monitoring systems.

Computer Vision

Computer vision works on enabling computers to see, identify and process images in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. It is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly.Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and

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International Journal for Modern Trends in Science and Technology (ISSN 2455-3778),, Volume 4, Special Issue 3, June 2018 20 extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

Deep Learning

Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.AI is based on Deep Learning algorithms. Deep Learning involves automatic feature detection from data. AI techniques can be applied to a range of data types including: Images and sound (CNNs), Transactional data, Sequences (LSTMs), Text (Natural Language Processing) and Behaviour (Reinforcement learning).

Natural Language Processing

Natural language processing is the communication method for artificial intelligence. It facilitates the communication with intelligent systems utilizing a natural language such as English. This aspect of AI is concerned with the interactions between computers and humans. Statistical and machine learning techniques are devised by this technology to comprehend sentence structure and meaning, sentiment, and intent easily. Natural language processing is currently being utilized mostly for fraud detection and security. Challenges in natural-language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.

Biometrics

Biometrics is one of the most widely adopted technology trends that uses computerized techniques to recognize a person by identifying their unique physical or behavioral traits. Fingerprints and face or eye „maps‟ are considered the critical identification features for this technique. Biometrics is one of the constituting technologies laying the foundation for proper implementation of artificial intelligence. Some of the common applications of this technology include building access and laptop security for identifying IDs and passports.

Planning scheduling optimization

Planning scheduling optimization is a branch of artificial intelligence that deals with strategies or action sequences designed to be executed by intelligent agents, autonomous robots, and unmanned vehicles. Due to the complexity of these solutions, they must be discovered and optimized in a multidimensional space. Planning scheduling optimization is considered as an essential link in the AI buildup.

Robotics

Robotics is concerned with the study of designing intelligent and efficient robots. It is a mixture of several different domains such as electrical engineering, mechanical engineering, and computer science, which aids in designing and constructing robots.

Robotics is one of the most revolutionary technology trends known to man. It uses scripts and other techniques to automate human effort and support efficient business processes.

Virtual assistants

Virtual assistants provide professional, technical, creative and administrative assistance to clients. Over the last couple of years, we have seen the birth of several unique and simple virtual agents like chatbots or advanced systems that can network with humans. They are being used for customer service and support as a smart home manager.

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International Journal for Modern Trends in Science and Technology (ISSN 2455-3778),, Volume 4, Special Issue 3, June 2018 21 These myriad forms of AI have the power to sift through, process and analyze data to generate a complete puzzle from all the scattered pieces and to distil patterns that humans cannot, or at least not easily. AI has the ability to crunch the streams of data coming in, digest it to generate patterns of normal machine behavior, identify any anomalies and then extrapolate to predict when a failure is likely.

IoTsystem usingArtificial Intelligence

The actual usefulness of artificial intelligence in the concept of the Internet of Things is the information system smart-IoT. The system consists of three main elements:

1. mobile devices – smart objects

2. central server – acts as an IoT management system 3. micro-services – include elements of AI.

The system is based on the 4-layer architecture:

1. Perception layer – covers only mobile devices. Its main task is to collect the data from the environment using sensors embedded in mobile phones (GPS, accelerometer, gyroscope) and pre-treatment of this data.

2. Network layer – is responsible for transferring data between all the elements of the system using LAN and Wi-Fi communications channels.

3. Processing layer – an essential element of the whole system. This layer is responsible for the processing of information in a central server with the appropriate use of micro-services, so the methods of artificial intelligence. Its primary aim is to convert the input data and generate a specific answer for that data in the context of the service.

4. Application layer – is responsible for providing data to the end user. Its fundamental role is to ensure the validity of data (to be updated with information from the decisionmaking module).

Intelligent objects in the system are mobile devices. Their main task is to collect the relevant data from the real world by using the built-in sensors. The next stage is the initial processing of the data. All of these tasks are performed by using the dedicated mobile application. In accordance with the concept of Internet of Things, all devices must be uniquely identifiable throughout the system. For this reason, the smart-IoT system proposed the use of unambiguous numeric identifiers. Each mobile device has a given unique identifier, which is recorded in the central management server. Such an approach also allows to limit the access of unauthorized devices, because the system will support only those for which the information is stored in the database.

The main components of IoT, are represented by a central server, acting as a manager. It consists of 4 major components:

1. module of services identification

2. module of artificial intelligence management 3. decision-making module

4. database module.

VI. CONCLUSION

AI and IoT are shaping up to be a symbiotic pairing. The powerful combination of these two technologies is helping avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management. This leads to democratization of technology capabilities liberating human invention. According to a study, within the next couple of decades, AI has a high likelihood of automating 70% of today‟s jobs in the energy sector, and 65% of today‟s jobs in consumer staples.

Ongoing advances in AI will have profound impacts on jobs and skills.Artificial intelligence is and will be critical for many

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International Journal for Modern Trends in Science and Technology (ISSN 2455-3778),, Volume 4, Special Issue 3, June 2018 22 technological evolutions as it is one of many enablers of digital transformation.Already, integrating AI into IoT networks is becoming a prerequisite for success in today‟s IoT-based digital systems. When technologies converge, they create many opportunities to use data, perform complementary functions and support each other to create a comprehensive and intelligent ecosystem.

REFERENCES

[1] M. Ruggieri and H. Nikookar and O. Vermesan and P. Friess, "Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems", River Publishers, 2013

[2] A. McEwen and H. Cassimally, "Designing the Internet of Things", Wiley, 2014

[3] A. Arsénio and H. Serra and R. Francisco and F. Nabais and J. Andrade and E. Serranol, "Internet of Intelligent Things:

Bringing Artificial Intelligence into Things and Communication Networks", Springer Science, 2014

[4] P. Lynggaard, "Artificial intelligence and Internet of Things in a "smart home" context: A Distributed System Architecture", PhD dissertation, Aalborg University Copenhagen, 2013

[5] I2oT:AdvancedDirectionoftheInternetofThings YixinZhong

[6] AnetaPoniszewska-Maranda andDaniel Kaczmarek : “Selected methods of artificial intelligence for Internet of Things conception”, Federated Conference on Computer Science and Information Systems, IEEE , 2015

References

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