CU-BCA-Sem VI-IOT Based Applications

BACHELORS IN COMPUTER APPLICATION SEMESTER - VI IOT BASED APPLICATIONS 1 CU IDOL SELF LEARNING MATERIAL (SLM)

First Published in 2022 All rights reserved. No Part of this book may be reproduced or transmitted, in any form or by any means, without permission in writing from Chandigarh University. Any person who does any unauthorized act in relation to this book may be liable to criminal prosecution and civil claims for damages. This book is meant for educational and learning purpose. The authors of the book has/have taken all reasonable care to ensure that the contents of the book do not violate any existing copyright or other intellectual property rights of any person in any manner whatsoever. In the event the Authors has/ have been unable to track any source and if any copyright has been inadvertently infringed, please notify the publisher in writing for corrective action. 2 CU IDOL SELF LEARNING MATERIAL (SLM)

CONTENT UNIT - 1 Introduction To Internet Of Things (Iot)................................................................. 4 UNIT - 2 Introduction To Internet Of Things (Iot)............................................................... 15 UNIT - 3 Components In Iot ............................................................................................... 30 UNIT - 4 Embedded Systems .............................................................................................. 62 UNIT - 5 Introduction To Arduino 1 ................................................................................... 80 UNIT - 6 Introduction To Arduino 2 ................................................................................. 108 UNIT - 7 Basic Interfacing And I/O Concept .................................................................... 142 UNIT - 8 Digital................................................................................................................ 161 UNIT - 9 Analog ............................................................................................................... 196 UNIT - 10 Embedded System Applications Using Arduino 1 ............................................ 226 UNIT - 11 Embedded System Applications Using Arduino 2 ............................................ 243 3 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT - 1 INTRODUCTION TO INTERNET OF THINGS (IOT) STRUCTURE 1.0 Learning Objectives 1.1 Introduction 1.2 Trends in the Adoption of the Internet of Things (IoT) 1.3 Summary 1.4 Keywords 1.5 Learning Activity 1.6 Unit End Questions 1.7 References 1.0LEARNING OBJECTIVES After studying this unit, you will be able to:  Know the Introduction of IOT  Understand the concept of IOT  Express various components of IOT 1.1 INTRODUCTION It is a network of physical objects or things sending, receiving, or communicating using the internet or other communication technologies. The Internet of Things (IoT), as this intelligentinterconnectivity between the real and the digital world is called, will rapidly transform every aspect of how we work and do business. By connecting apps with their integrated systems, businesses are able to transform their industry significantly. Today almost 90% of all data generated by tablets, smartphones or connected appliances is never acted upon. Imagine you could change that. It seems safe to say that we have never encountered a single technological platform that combines this much complexity, global reach and novelty. Since IoT allowsdevices to be controlled remotely across the internet, thus it created opportunities to directly connect & integrate the physical world to the computer-based systems using sensors and internet. The interconnection of these multiple embedded devices 4 CU IDOL SELF LEARNING MATERIAL (SLM)

will be resulting in automation in nearlyall fields and enabling advanced applications. This is resulting in improved accuracy, efficiency and economic benefit with reduced human intervention. It encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. The major benefits of IoT are: Improved Customer Engagement – IoT improves customer experience by automating the action. For e.g. any issue in the car will be automatically detected by the sensors. The driver, as well as the manufacturer, will be notified about it. Until the time driver reaches the service station, the manufacturer will make sure that the faulty part is available at the service station. Technical Optimization – IoT has helped a lot in improving technologies and making them better. The manufacturer can collect data from different car sensors and analyze them to improve their design and make them much more efficient. Reduced Waste – Our current insights are superficial, but IoT provides real-time information leading to effective decision-making & management of resources. For example, if a manufacturer finds fault in multiple engines, he can track the manufacturingplant of those engines and can rectify the issue with manufacturing belt. 1.2 TRENDS IN THE ADOPTION OF THE INTERNET OF THINGS (IOT)  REDUCTION IN IOT COMPONENT COSTS A key trend improving enterprise opportunities for IoT is falling system costs at boththe device and network levels. Because of architectural similarities, IoT stands to significantly benefit from ongoing advancements in the smartphone market whose large volumes and increasing competition are dramatically reducing pricesthroughout the value chain. This includes key component parts such as sensors, embedded processors, memory and cellular modems whose steady price declines will continue to drive cost reductions for developing new IoT endpoint applications and thus ease capital requirements for enterprise deployment. Similarly, price reductions continue to occur in cellular communications that support IoT device connectivity. As demand for IoT devices increases, prices for both 3G and 4G networks will continue to decline, which will drive reductions in recurring operationalexpenses for enterprise solutions. Additionally, expanded use of cloud services in IoT and the benefits of their elastic pricing models will enable cost- effective computational resources that will further reduce cost of ownership and improve the value proposition for enterprise clients.  END-TO-END IOT PLATFORMS 5 CU IDOL SELF LEARNING MATERIAL (SLM)

While component price reductions will play an important role in the growth of user adoption, reducing the complexity of IoT application development and deployment will also be a major driver in the market. Unlike IT, the IoT market is a highly fragmented ecosystem where requirements for enterprise solutions can vary significantly from clientto client even in the same market vertical. This includes variability in requirements related to device functionality, communications and applications logic, which require integrating a host of different hardware and software elements. Moreover, added complexities are introduced when integration is required with client systems such as local area networks or core enterprise software, which inevitably will require upgrades that will further complicate the IoT offering. As such, these integration, installation and maintenance complexities establish significant barriers to entry for most enterprise IoT solutions, especially when large numbers of device deployments are required over large geographic regions. As such, the expense associated with development and deployment quickly erodes the solution‘s value proposition and return on investment. To address these issues, new forms of IoT platforms will emerge that will drive out the complexity of application and enterprise solution development and radically decrease cost-of- ownership. These will include what I would describe as configurable, end-to-endIoT platforms that will integrate functionality from the edge of the network into to the cloud. Inherent in the architectural design will be built-in features and functions for sensing, communications and business logic, which can be easily configured for new applications, making implementation very simple and cost effective. Moreover, reducingbarriers for the delivery of sensor information to the cloud will enable 3rd party application developers an entirely new framework from which to build advanced solutions based on cloud-to-cloud data exchange. As an example, we have released the initial version of our Quantus Sensor Fusion Engine product, an end- to-end IoT platform, which has demonstrated significant benefits related to ease-of-installation, network scalability and application configurability. Configurable IoT system approaches such as Quantus will continue totightly integrate new features throughout the network stack with the aim of reducing time tomarket and cost of ownership for new enterprise solutions. These features will also include advancements in analytics both at the device and cloud levels, which is thebasis for the 3rd trend as described below.  SENSOR ANALYTICS AND FUSION The business value of IoT at its basic level takes the form of what I would describe as improved situational awareness. This includes a better understanding of the status of remote physical systems in terms of their condition, which drives improved operational decisions, actions and efficiencies. For example, working with our partner FCC Environmental, we have deployed an IoT solution built on Quantus that includes cellularbased tank level monitoring 6 CU IDOL SELF LEARNING MATERIAL (SLM)

devices installed at automotive retail locations. Data from differential pressure sensors mounted on used oil tanks are processed using analytics at the device level to generate volume status and alert messages, which are communicated wirelessly to the cloud for storage, additional processing and reporting to desktop and mobile devices. In this case, Quantus provides operations personnel with actionable information regarding a store‘s past, current and forecasted volumes of used oil, which enables informed decisions regarding the routing of service vehicles dispatched over large geographic locations. Specifically, this includes the ability to now service automotive clients based on need as opposed to fixed routing schedules, an operational change enabled by IoT that drives significant improvements in oil collection efficiency. This form of sensor analytics is the most common in the industry today and has its rootsin the machine-to- machine (M2M) market where similar sensor solutions for wirelessly monitoring remote assets have been in service for many years. While these types of solutions deliver clear value in specific market verticals, opportunities are emerging to create completely new levels of actionable information through a process known as sensor fusion, which is the generation of new, richer forms of actionable intelligence through the application of analytical processing techniques to diverse, multi-source datasets. For example, research efforts at IBM recently demonstrated an IoT solution for the mining industry that accurately predicted machine failures using real-time analytics supplied by a mix of equipment sensors and operational data. These multi-source datasets included information regarding truckload bearing capacity, environmental conditions, repair history and other sensors, which were processed using real-time analytics and modeling techniques. The new operational insights provided by the system stands to drive significant productivity gains, which IBM predicts could be worthbillions of dollars annually. Implementation of these sensor analytics and fusion techniques and their ability to capture higher levels of actionable information represents a new stage in the technical evolution of IoT and will significantly increase the value ofinformation products delivered by these solutions. 1.1 Societal Benefits of IoT: IoT devices impact society in a meaningful way. Smart homes and offices can save energy costs by controlling the electricity or temperature when one is away from home or work, andthey 7 CU IDOL SELF LEARNING MATERIAL (SLM)

Fig 1.5. New IoT use cases across technologies can offer better security by constant surveillance and taking proactive action in case of a security breach. Smart health devices can improve health care by monitoring patients andremotely administering medication to them, and smart automobiles can request assistance ifrequired or assist in monitoring vehicle speed based on traffic. IoT is delivering positive economic and social impacts, transforming our societies, theenvironment and our food supply chains for the better. Here are just a few examples:  Monitoring And Reducing Air Pollution Cities account for approximately 70% of the world‘s harmful greenhouse gas emissions, despite comprising 2% of the global land area. Each year, more than 3million people die from air pollution. To change this, cities around the world are incorporating IoT-enabled sensors and devices throughout their infrastructure to monitor air quality and are using the data to implement new urban services that canreduce traffic congestion and associated pollution.  Improving Water Conservation Cities are also using IoT technologies to conserve water. Barcelona implemented an IoT- enabled, smartirrigation system using underground probes placed in parks throughout the city to monitor soil moisture. The remotely monitored devices upload data to the cloud and can automatically open electronic valves, watering the landscaping only when needed and when 8 CU IDOL SELF LEARNING MATERIAL (SLM)

weather conditions are right. As a result, the municipal water bill has been reduced by 25% and, more importantly, water usage hasbeen reduced.  Saving Critical Species The IoT is not just about connecting devices, sensors and machines to each other andthe internet. Even people and other living creatures can be connected. Case in point: bees. Bees are the world‘s main pollinator of food crops. However, the global bee population is declining quickly. To help reverse the trend, an international alliance of researchers and scientists is collecting data on bees using micro sensing technology. By equipping bees with tiny backpacks that use radio-frequency identification (RFID) technology, researchers use electronic readers to record the behavior of individual bees. The technology has revealed an unprecedented level of detail about not only the movements of bees but also the factors impacting their ability to pollinate, such as disease, pesticides, air pollution, water contamination, diet and extreme weather. Ciscoworked together with the researchers to use IoT technologies to gather data from the hives and make it available for a variety of applications and scientific analysis, in the hopes that we can make the changes necessary to save our most precious pollinators. 1.3 SUMMARY  we discussed how the sensor and actuator devices are working in the environment and their primary differences with proper examples. The basic concepts of networking and its types have been reviewed elaborately.  The concept of communication protocols is introduced here and thoroughly explained its types, advantages, and disadvantages of every protocol, and evaluated where, when, and how these protocols are used in the networking environment. The idea about Machine to Machine(M2M) principles was also discussed in this chapter.  How the M2M technology is efficiently used in various applications such as in manufacturing, home appliances, healthcare device management, smart utility management are examined.  And also describes how our day-to-day life is integrated with M2M technology with many examples like smart meters used in the home, smart assets tracking services, smartwatches, etc. 9 CU IDOL SELF LEARNING MATERIAL (SLM)

 Finally, the IoT (Internet of Things) came into the picture, discussed the concept of IoT and its characteristics along with its various design such as Logical, physical, communicational, and functional design in a simple way. To design a web service, IoT communication APIs such as REST and WebSocket APIs were reviewed. This will help us to understand how to design and how to frame the infrastructure of the IoT. Various layers of IoT protocols are examined along with associated protocols with each layer. In the next chapter, we will discuss how the technology slowly moving from M2M to IoT effectively. 1.4 KEYWORDS 1. IoT Cloud Platform IoT cloud platforms are customized, cloud-hosted software services that enable IoT functionality and applications. IoT providers develop these platforms based on a company’s specific connectivity needs. They include elements needed to gather, analyze and process data from IoT-enabled devices and housed in the IoT provider’s cloud storage. Access to an IoT cloud platform is typically available through a subscription model. In these models, a company pays the IoT provider for access to the platform they created. 2. Edge Computing Most IoT applications rely on devices connecting to the cloud (i.e., an external source) for computing and analytics. Technological advancements make it possible to perform those functions on devices without sacrificing budget or efficiency. They reduce the cost of backhauling all IoT data to the cloud. This ability to perform advanced on-device processing is known as “edge computing.” Consider it the complement to “cloud computing.” 3. Mobile IoT (MIoT) As IoT grows, the mobile industry has adapted by developing a cellular technology class to support MIoT requirements. MIoT refers to a cellular low-power wide-area (LPWA). Cellular LPWA will be impactful in connecting new IoT devices and servicing various industries and applications. Telit is among a handful of industry pioneers manufacturing MIoT modules. 4. Bluetooth Low Energy (BLE) 10 CU IDOL SELF LEARNING MATERIAL (SLM)

BLE is an update on traditional Bluetooth technology allowing for a safer, lower-cost short- range network. This network is commonly used to pair health-tracking wearables with users’ cell phones and install location trackers on household devices. BLE allows these devices to be found when they’re within network range. 5. IoT Protocol An IoT protocol is the language two or more machines use to communicate with each other. These languages contain rules to help them connect and decipher each other’s messages and figure out how to act on their exchanged signals. Common protocols include: Narrowband IoT Cellular LPWA LoRaWAN Zigbee TR-50 MQTT AMQP 6. Global Navigation Satellite System (GNSS) GNSS is a standard term describing satellite navigation systems providing autonomous geospatial positioning with global coverage. A device with GNSS access means technologies get accuracy, redundancy and availability at all times. If a connection to one satellite fails, another can be picked up in seconds, often without lapses or delays. 7. Narrowband IoT (NB-IoT) NB-IoT is a cellular LPWA technology developed to enable a broad range of new IoT devices and services. NB-IoT allows for low-cost IoT enablement. It broadens IoT’s potential uses, making it fiscally feasible to connect everything from parking meters to pet leashes. 1.5 LEARNING ACTIVITY 11 1. Explain sensor Networks 2. Draw a diagram of physical design of IOT CU IDOL SELF LEARNING MATERIAL (SLM)

1.6 UNIT END QUESTIONS A. Descriptive Questions Short Questions 1. Define Communication protocols? List the various types of communication protocols 2. Write a narrow description of sensor networks. 3. What do you mean by IoT? Explain its characteristics. 4. List the Link layer IoT protocols. 5. Why we need IoT? 6. Differentiate between wired and wireless sensor networks? 7. Explain shortly IoT functional block. Long Questions 1. List the basic concepts of networking and explain their types in detail. 2. Write short notes on sensor networks and their operations 3. Explain & crucial characteristics of IoT in detail 4. What is mean by communication models in IoT. Explain any one in detail 5. What is NFC? Explain its two mode in detail. 6. Explain Physical design of IoT in detail? B. Multiple Choice Questions 12 1. LAN stands for: a. Local Area Net b. Local Area Network c. Local Array Network d. Local Array net 2. Which of the following is not a topology? a. Bus b. Star CU IDOL SELF LEARNING MATERIAL (SLM)

c. pear to pear d. Ring 3. Our typical television remote uses which of the communication protocols? a. Wi-fi b. Bluetooth c. satellite d. Radio Frequency(RF) 4. IoT deals with? a. sensor, networking b. electronic c. cloud messaging d. All of the above 5. Which part of the functional model is responsible for sensing, identification, actuation functionalities? a. Communication b. Security c. Application d. Device 6. Which protocol is lightweight? a. MQTT b. HTTP c. CoAP d. SPI 7. The services provided by the transport layer are a. Error control b. Connection service c. Connection less service d. Congestion control Answers 13 CU IDOL SELF LEARNING MATERIAL (SLM)

1-b, 2-c, 3-d, 4-d, 5-d, 6-a, 7-a 1.7REFERENCES Text Books 1. Internet of Things (A Hands on Approach), By ArshdeepBahga (Author),VijayMadisetti(Author).Edition: Second Edition, Illustrated, Reprint (2014)Publisher: VPT, 2017 2. “Beginning Arduino” by Michael McRobetrs(Author).Publisher:Technology in Action Reference Books: - 1. Tim Cox, Dr. Steven Lawrence Fernandes, Sai Yamanoor, Srihari Yamanoor, Prof. DiwakarVaish,” Getting Started with Python for the Internet of Things: Leverage the full potential of Python to prototype and build IoT projects using the RaspberryPi Edition: First Edition Publisher:Packt Publisher-2019 14 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT - 2INTRODUCTION TO INTERNET OF THINGS (IOT) STRUCTURE 2.0 Learning Objectives 2.1 Introduction 2.2 The Importance of the Internet of Things (IoT) in Society 2.3 Challenges in implementing IOT 2.4 Summary 2.5 Keywords 2.6 Learning Activity 2.7 Unit End Questions 2.8 References 2.0LEARNING OBJECTIVES After studying this unit, you will be able to:  Know about The Importance of the Internet of Things (IoT) in Society  Understand the Challenges in implementing IOT 2.1 INTRODUCTION Businesses that can somehow make sense of IoT-collected data will gain a competitive edge. Marketers, for example, can gather valuable insight into how their products are used and which demographic is utilizing them the most. This information can then inform future marketing efforts and give businesses more direction on how to improve their products and services for their customers. Although businesses will certainly face many challenges in implementing the Internet of Things, those who manage to overcome them will reap all the benefits of thisburgeoning technology 2.2 IMPORTANCE OF IOT 15 Better Decision Making CU IDOL SELF LEARNING MATERIAL (SLM)

Since devices have multiple sensors, they can acquire considerable data from numerous sources, giving them more information to work with when acting on data received. A great example is smartphones. The device automatically tracks your behaviors on its interface and makes suggestions based on your activity, location, and age. The phone can also keep tabs on various activities. This includes the amount of screen time users spend each day, power consumption, and sleeping patterns. Massive amounts of data are being collected and sent back to smartphone companies each day to improve features on their devices. With the constant influx of big data, companies begin to see trends in the usage of their devices and can immediately pinpoint their strengths and weaknesses. This insight would not be possible without the help of embedded sensors and processors which analyze the data. Real-time Tracking and Monitoring The potential for web-based tracking and monitoring systems is enormous. IoT tracking provides an efficient means to track and monitor anything from vehicle fleets, stolen goods, or shipping containers. Particular devices can even detect changes in the environment. There are multiple industries where IoT trackers can immensely improve the efficiency of companies. A malfunction in these products can lead to enormous losses for the company. IoT-based trackers need to be reliable to provide the best services. These devices should provide the following: ● Real-time data analytics Fast, accurate data is required in the industry to allow for quick, informed decision-making when assets or changes in the environment are being monitored. ● Secure communication Companies usually track and monitor high-value assets. It is essential that the shared data is protected and not under the threat of hackers. ● Stable connectivity The device should securely provide helpful information on asset locations, machine functionality, and temperatures. This is required at all times and from anywhere on the planet. 16 CU IDOL SELF LEARNING MATERIAL (SLM)

Automation A big reason for the invention of IoT is convenience. Smart devices that automate daily tasks allow humans to do other activities. These devices ultimately lighten people’s workload. Smartphones allow us to connect with people from all over the world. We can schedule when to send messages and even use dictation to avoid typing ourselves. Then there are smart fridges. Imagine having one that can detect when foods are about to expire and notify the owner to eat that food before it’s too late. Perhaps the smart fridge could even register that the milk is nearly finished and automatically order more. Another example is a self-driving car, connecting to the Internet to find the quickest route to a destination. This is the ultimate convenience for humans. The room for innovation within IoT is massive. More Efficient Personal and Business Tasks Web-based devices save people money and time. This includes planning work schedules, time tracking, effective communication, and setting reminders for daily tasks. Having IoT devices track and order things for you, turn lights off automatically when you leave the room, and manage tasks for which you don’t have time is the ultimate convenience! More and more of these devices will become available for use over the coming years, with an estimated total number of IoT connections to reach 27 billion in 2024. It’s no secret that human productivity has gone up with the technological age. People are busier than ever before, thanks to IoT. It’s incredible to have the opportunity to do important things like spending time with family while an IoT device takes care of mundane activities. IoT Dangers Security and Privacy The principal idea for IoT is to have an innovative, secure system through which billions of devices can automatically communicate with each other. This system needs rock-solid security, especially with the daily transfer of data. 17 CU IDOL SELF LEARNING MATERIAL (SLM)

Technology is used for both good and evil. Cybercriminals use their skills in IT to hack into systems to steal money or access sensitive data. They can also hack IoT devices for information on what the device may be tracking or monitoring. This is highly dangerous for business owners and individuals where hackers try to intercept shipments of high-value assets or sell sensitive information on the dark web. People on the dark web then use this information to send spam emails. Luckily there are preventative measures like email address lookup, which allows people to identify the sender of phishing emails. Therefore, the IoT system requires tight security measures to keep data safe. However, it is not easy to ensure complete protection with so many devices connected. Technology companies have found that gaining the trust of individuals and industries to share their data has proven difficult. As IoT advances, the security measures will only become stricter to keep everyone’s data safe when utilizing the Internet of Things. Potential Increased Unemployment It is known that devices have made specific human jobs redundant. The introduction of artificial intelligence (AI) has sparked fear in some who are afraid to have their livelihood taken away from them by a machine. McKinsey Global Institute estimates that around 30% of the world’s current jobs will be replaced by automation by 2030. This can be daunting for some. However, the job market is constantly evolving. The creation of technology has seen the emergence of related professions like data scientists. Over-reliance on Technology It is easy to fall into the trap of relying too much on technology to meet our needs. For instance, the recent boom of social media where people worldwide can connect, share photos, stories, and essentially their lives with others on social platforms. While this is well-intentioned, many people struggle to live without social media and eventually depend on smartphones. According to recent studies, higher social media usage in young adults is linked to increased depression. 18 CU IDOL SELF LEARNING MATERIAL (SLM)

Moreover, the future could see individuals relying heavily on technology to the point that human nature shifts from reality into alternate dimensions such as the Metaverse. Compatibility IoT is expanding in many settings, with various technologies vying for supremacy. When connecting devices, this will cause problems and necessitate the deployment of additional hardware and software. Other compatibility concerns arise due to non-unified cloud services, a lack of standardized M2M protocols, and differences in IoT device firmware and operating systems. Some of these technologies will become obsolete in the coming years. This is crucial since, unlike traditional computing products with a short lifespan, IoT appliances (such as smart fridges or TVs) will last much longer and should continue to work even if their maker goes out of business. 1.3CHALLENGES IN IMPLEMENTING IOT 1. Compatibility and Interoperability of Different IoT systems As per the market analysts at McKinsey, 40% to 60% of the total values lies on our ability to achieve interoperability between different IoT systems. With numerous vendors, OEMs, and service providers, it becomes really difficult to maintain interoperability between different IoT systems. Sensors and Networking are the integral components of IoT. But not every machine is equipped with advanced sensors and networking capabilities to effectively communicate and share data. Besides, sensors of different power consumption capabilities and security standards inbuilt in legacy machines may not be capable to provide the same results. A quick workaround could be to add external sensors, but this is also challenging because determining which function and which part will communicate and share data with the network is complex. 2. Identification and Authentication of Technologies According to a report, there are around 20 billion connected devices at present, and to connect all the devices involves a lot of security risks and not just complexity. Bringing along 19 CU IDOL SELF LEARNING MATERIAL (SLM)

a large number of connected devices on one platform needs formalization and system architecture that can identify and authenticate those devices. 3. Integration of IoT Products with IoT Platforms For the successful implementation of IoT application, enterprises need to integrate various IoT connected products with right IoT platforms. Lack of proper integration could lead to abnormalities in functions and efficiency to deliver value to the customers. Research vice president at Gartner, Benoit Lheureux, says “Through 2018, half the cost of implementing IoT solutions will be spent integrating various IoT components with each other and back-end systems. It is vital to understand integration is a crucial IoT competency.” The major challenge here is too many IoT endpoints and asserts that need to be connected to aggregate the sensor data and transmit it to an IoT platform. Only with deep integration, companies can mine the huge data through Big Data technique to generate insight and to predict the outcomes. 4. Connectivity It is the part of networking challenges, as the Internet is still not available everywhere at the same speed. A global mobile satellite company Inmarsat revealed that 24% finds connectivity issue as the one of the biggest challenges in IoT deployment. Specifically, Logistics and Oil & Gas companies engaged in remote operations require robust communication networks to collect data in tough conditions and transmit back to the centre for analysis. The quality of signals collected by the sensors and to transmit over to the Networks largely depend upon the routers, LAN, MAN, and WAN. These networks have to be well-connected through different technologies to facilitate quick and quality communication. But the number of connected devices is growing at a much higher rate than the network coverage, which creates monitoring and tracking problems. 5. Handling Unstructured Data Growing connected devices will increase the challenges of handling unstructured data on the parameters of volume, velocity, and variety. However, the real challenge for the organizations is to determine which data is valuable, as only quality data is actionable data. 20 CU IDOL SELF LEARNING MATERIAL (SLM)

According to a survey, 80% of today’s data is unstructured data and so the data cannot be stored in SQL format. The unstructured data is stored in NoSQL format makes retrieval of data a bit complex. With the launch of Big Data frameworks such as Hadoop and Cassandra, the problem and complexity of handling unstructured data has somewhat reduced, but the Big Data in itself is so massive that combining it with IoT possess a great challenge. Besides, there are no standard guidelines for retention and use of data as well as metadata. 6. Data Capturing Capabilities The purpose of capturing data is to transform the information collected from various sources in a standard format that can be analyzed and automated. According to Hubspot report, sponsored by ParStream, out of 86% of business stakeholders who claim that data is integral to their IoT project, only 8% are able to capture and analyze IoT data in a consistent manner. As IoT is mainly about dependence on sensors for signals and networks for the distribution, chances are that due to certain anomalies in runtime, such as a shutdown of power, incorrect data may get recorded. 7. Intelligent Analytics At this stage, we are at the very purpose of IoT i.e. translating data into meaningful information. A flaw in data or data model could lead to false positives and false negatives. We have to understand the data in itself is not an insight, rather right questions have to be asked from the precise data to gain the insight. Based on this report by Hubspot, it is apparent that 44% of IoT stakeholders face difficulty in capturing data and 30% confirms that their analytics capabilities are not strong and flexible. Legacy systems such as traditional analytics software where not all data can be loaded at a time can limit the capabilities to manage real-time data. Here’s the list of challenges that deter intelligent analytics:  Unpredictable action of the machine during an incident  Traditional analytics software  Slow adoption of the latest technology due to the high cost 21 CU IDOL SELF LEARNING MATERIAL (SLM)

 Lack of skilled professionals in data mining, algorithms, machine learning, and complex event processing 8. Data Security and Privacy Issues Even top companies like Apple, known for big security claims, and visionaries like Elon Musk have not been spared by hackers. Recent cases of ransomware attacks have also challenged the confidence of corporate. A latest research claims that by 2020, 25% of cyber attacks will target IoT devices.  Malware infiltration: 24%  Phishing attacks: 24%  Social engineering attacks: 18%  Device misconfiguration issues: 11%  Privilege escalation: 9%  Credential theft:6% When it comes to cyber security, lapses could be from both company and consumer side, so it is essential for each party to take necessary measures to improve security. A study revealed that 54% IoT device owners do not use any third party security tool and 35% out of these do not even change default password on their devices. Here, it should be a collaborative effort between companies and customers to plan and implement collaborative data security policies for successful IoT implementation. 9. Consumer Awareness Many people are not aware of IoT, but they understand the dependence on Smart Apps like news apps, stocks applications, entertainment applications. It is not actually important for the consumers to how things work technically, but lack of basic awareness can create a fear of security and cost, which could lead to the slow adoption of technology. According to a survey of 3,000 U.S. and Canadian consumers conducted by Cisco, 53% consumers would not prefer to get their data collected, irrespective of the device. This shows the fear among users to share their data, which can act as a deterrent to the IoT 10. Delivering Value 22 CU IDOL SELF LEARNING MATERIAL (SLM)

According to Forbes Insights Survey, 29% executives feel major challenge in building IoT capabilities is the quality of IoT technology. This data reveals the struggle of IoT application development companies in bringing the value for their consumers. So, before plunging into the development of IoT applications, an enterprise must clearly define what value they are going to deliver through what capabilities. And how their solution will enhance the efficiency and productivity, while also generating customer-satisfaction. 1.4 SUMMARY  Concepts of how the IoT technology has been extremely utilized in various fields of the emerging world with proper examples.  Evaluated how technology migration from Machine to Machine to the Internet of Things(IoT).  In that evaluation reviewed the role of game-changers who are natural resource constraints, economic shifts, changing demographics, socio-economic expectation, climate change, and environmental impact, safety, security, urbanization. Trends in the ICT(Information and Communication Technology) were discussed.  In this chapter, we discussed how the sensor and actuator devices are working in the environment and their primary differences with proper examples. The basic concepts of networking and its types have been reviewed elaborately. The concept of communication protocols is introduced here and thoroughly explained its types, advantages, and disadvantages of every protocol, and evaluated where, when, and how these protocols are used in the networking environment. The idea about Machine to Machine(M2M) principles was also discussed in this chapter. How the M2M technology is efficiently used in various applications such as in manufacturing, home appliances, healthcare device management, smart utility management are examined. And also describes how our day-to-day life is integrated with M2M technology with many examples like smart meters used in the home, smart assets tracking services, smartwatches, etc.  Finally, the IoT (Internet of Things) came into the picture, discussed the concept of IoT and its characteristics along with its various design such as Logical, physical, communicational, and functional design in a simple way. To design a web service, IoT communication APIs such as REST and WebSocket APIs were reviewed. This 23 CU IDOL SELF LEARNING MATERIAL (SLM)

will help us to understand how to design and how to frame the infrastructure of the IoT. Various layers of IoT protocols are examined along with associated protocols with each layer. In the next chapter, we will discuss how the technology slowly moving from M2M to IoT effectively. 1.5 KEYWORDS  Quality of Service (QoS) A QoS measurement conveys how well a service is performed. In IoT, the term describes network performance in supporting IoT connectivity. Factors in arriving at a QoS include:  Connection availability  Transmission delay  Information loss  Mesh Network Mesh networks are multinode structures that connect through multiple routers. These connections tend to be direct, dynamic and not based on an inherent hierarchy. They are agile enough to adapt to various networking conditions to relay data efficiently. Mesh networks’ ability to self-configure means they can recover from failed nodes without needing external maintenance and handle long-range transmission. They are a good fit for enterprise IoT applications, which need to remain reliable even at a large scale.  Telematics A telematics platform is a computer system dealing with long-distance transmissions of data. An example would be the consoles installed in many automobiles that can provide GPS and satellite radio functions.  Sensors and radio frequency identification Sensors and RFID are now used in a wide range of applications around the world. assisting in the smoothing of supply and demand in different supply chains, as well as gathering environment and other localized data and transmitting it back to a centralized data collection facility These instruments serve as inputs to the I-GVC, capturing and transmitting data that is essential for the creation of information goods.  Asset Information: Data such as temperature or air quality for a certain month can be included in Asset Records. This basically refers to something to track the sensor/device.  Open Data Sets: Maps, train schedules, and demographic data for a certain region of a nation or city can be included. 24 CU IDOL SELF LEARNING MATERIAL (SLM)

 Network Information: Network information refers to information such as GPS data, facilities accessible by a mobile network, etc.  Corporate Information: The actual demand status for a certain product in the supply chain may at a certain point in time be corporate information 1.6 LEARNING ACTIVITY 1. List all the fields where IoT can be implemented 2. Explain the use of Big Data in IoT 2.3 UNIT END QUESTIONS A. Descriptive Questions Short Question 1. What are the impacts that can be observed in implementing the Internet of Things on the Urban Agriculture sector? 2. What is Open API? Explain in detail. 3. Explain simplified global value chain 4. Details about the information-driven global value chain 5. Describe in detail the Implication of IoT. Long questions 1. What are the differences between IoT and M2M?. 2. List out the drawbacks of using M2M communication. 3. What is ICT? 4. Describe Smart grids. 5. What are digital communication channels? List out the various digital communication channels used in our world. 6. What is ―Big Data‖? 7. What are the effects the Internet of Things will have on human life? B. Multiple choice questions 1. The huge number of devices connected to the internet of things have to communicate automatically, not via humans, what is this called? a. Bot to Bot(B2B) 25 CU IDOL SELF LEARNING MATERIAL (SLM)

b. M2M c. Intercloud d. Skynet 2. M2M stands for: a. MAC to MAC communication b. Machine to MAC communication c. Machine to machine communication d. MAC to machine communication 3. In an M2M ecosystem, Internet service providers: a. Provide their infrastructures for M2M device communications. b. Are responsible for devices providing raw data. c. Is an individual or company that utilizes M2M applications. d. None of these 4. Device interoperability problem is between a. Two devices b. A device and a user c. Both (a) and (b) d. None of the above 5. System-on-a-Chip (SoC) Solutions consists of a. RAM and flash memory b. I/O power c. c. IEEE 802.15.4 d. d. All the above 6. LTE stands for a. Long-Term Evolution b. Line-Term Evolution c. Long-Term Existence d. Long-Technology Evolution 7. The data gathered from sensors and is combined with smartphone information that directly identifies a particular individual activities 26 CU IDOL SELF LEARNING MATERIAL (SLM)

a. RF b. Bluetooth c. RFID d. Wi-Fi 8. Which of the following are responsible for producing data in digital forms in I- GVC. a. Service provider b. Data Factories c. Intermediaries d. Resellers 9. OSS stands for a. Operational Support Systems b. Operator Support Systems c. Open Support system d. Over Support System 10. large set of structured, unstructured and semi structured data. a. big data b. small data c. physical computing d. cloud computing 11. MEMS Stands for a. Micro Electro Mechanical Systems b. Macro Electro Mechanical Systems c. Macro Electrical Machinery System d. Micro Elective Mechanical System 12. Which is used to identify the items and they can be tracked and traced throughout the supply chain? a. NFC b. RFID c. WI-FI 27 CU IDOL SELF LEARNING MATERIAL (SLM)

d. Bluetooth 13. Which of the below component is responsible for remote connection between M2M device and application-side servers? a. Asset b. M2M device c. Network d. M2M service enablement 14. ICT stands for a. Information and Communication Technology b. Internet and Communication Technology c. Internet Common Technology d. Intranet Communication Technology 15. PLC a. Power Linked connection b. Post Line Connection c. Power line Communication d. Pocket Line Communication Answers 1-b, 2-c, 3-a, 4-a, 5-d, 6-a, 7-c, 8-b, 9-a, 10-a, 11-a, 12-b, 13-c, 14-a, 15-c 2.4 REFERENCES Reference Books 1. Tim Cox, Dr. Steven Lawrence Fernandes, Sai Yamanoor, Srihari Yamanoor, Prof. DiwakarVaish,” Getting Started with Python for the Internet of Things: Leverage the full potential of Python to prototype and build IoT projects using the RaspberryPi Edition: First Edition Publisher:Packt Publisher-2019 Text Books: - 1. Internet of Things (A Hands on Approach), By ArshdeepBahga (Author),VijayMadisetti(Author). 28 CU IDOL SELF LEARNING MATERIAL (SLM)

Edition: Second Edition, Illustrated, Reprint (2014) Publisher: VPT, 2017 2. “Beginning Arduino” by Michael McRobetrs(Author). Publisher:Technology in Action 29 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT - 3COMPONENTS IN IOT 30 STRUCTURE 3.0 Learning Objectives 3.1 Introduction 3.2 Control Units 3.3 Sensors 3.4 Communication modules 3.5 Power Sources 3.6 Communication Technologies 3.7 RFID 3.8 Bluetooth 3.9 ZigBee 3.10 Wi-Fi 3.11 Summary 3.12 Keywords 3.13 Learning Activity 3.14 Unit End Questions 3.15 References 3.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  Describe control units of IOT  Identify the components in IOT  Need of sensors  Know about communication technologies CU IDOL SELF LEARNING MATERIAL (SLM)

3.1 INTRODUCTION The Internet of Things (IoT) is the result of cooperation between them. It is a system of interconnected computing devices, mechanical and digital machines, and things that are capable of sensing and are able to communicate with each other and with machines linked to a network intelligently to take advantage of the data collected through the sensors imbedded into devices. The resulting data can be collected and analyzed in order to reveal insights and propose measures that will produce cost savings, increase efficiency or improve products and services. As IoT is expected to grow and spread rapidly in the coming years, this will improve the quality of consumers' lives and productivity of enterprises. The future is the IoT, which will change the real world objects to smart virtual entities. 3.2CONTROL UNITS IoT Open has full support for controlling and managing devices in the field. IoT Opens interface is called DeviceX / FunctionX (DevX / FuncX) which makes it easy to integrate devices from different manufacturers and different protocols. As the main data carrier, MQTT is used out to the edge where it either goes all the way to the unit or terminates in a CPE / Gateway that translates to the respective unit's protocol. This translation can also be done centrally depending on which protocol the device uses. Units can also be controlled locally at the edge. Local governance, local services and rules in edge can also work offline. Presence-controlled street lighting can thus work even if there is a temporary problem with the connection between the edge and the platform. DevX / FuncX control DevX / FuncX saves all devices and functions. A function is someone who can be measured, displayed and / or controlled, for example a temperature, carbon dioxide content, door position, detected vehicle, switch, voltage, ground humidity, fire warning etc. Each function can belong to a unit, but it is not a requirement. A device represents hardware and can have several functions. Examples of units can be a multisensor, smoke alarm, vehicle unit, etc. Arbitrary metadata can be stored on both functions and devices. For example, a function may have properties such as name, type, device, format string, icon, etc. A device can have a serial number, socket side, BIM identification (GUID), manufacturer, model, software version, etc. Only a few of the parameters are mandatory and it is possible to define your own parameters. For example, metadata on a device can be latitude / longitude, free text, vendor information, 31 CU IDOL SELF LEARNING MATERIAL (SLM)

or other applicable information. Some documented parameters are handled by the platform, while those that the platform does not know are only relevant for integration with other systems. Among the parameters are which topics on the MQTT bus are used to read and control the function, respectively. Some functions, such as temperature, only have a topic for reading, while a switch or a thermostat have topics for both reading and control. Via the MQTT bus and the platform's API, devices can be controlled with a delay of typically parts of a second. 3.3 SENSORS Sensors are used for sensing things and devices etc. A device that provides a usable output in response to a specified measurement. The sensor attains a physical parameter and converts it into a signal suitable for processing (e.g. electrical, mechanical, optical) the characteristics of any device or material to detect the presence of a particular physical quantity. The output of the sensor is a signal which is converted to a human-readable form like changes in characteristics, changes in resistance, capacitance, impedance etc. Figure 3.1 IOT HARDWARE 32 Transducer:  A transducer converts a signal from one physical structure to another. CU IDOL SELF LEARNING MATERIAL (SLM)

 It converts one type of energy into another type.  It might be used as actuators in various systems. Sensors characteristics: 1. Static 2. Dynamic 1. Static characteristics: It is about how the output of a sensor changes in response to an input change after steady state condition.  Accuracy – Accuracy is the capability of measuring instruments to give a result close to the true value of the measured quantity. It measures errors. It is measured by absolute and relative errors. Express the correctness of the output compared to a higher prior system. Absolute error = Measured value – True value Relative error = Measured value/True value  Range – Gives the highest and the lowest value of the physical quantity within which the sensor can actually sense. Beyond these values, there is no sense or no kind of response. e.g. RTD for measurement of temperature has a range of -200`c to 800`c.  Resolution – Resolution is an important specification towards selection of sensors. The higher the resolution, better the precision. When the accretion is zero to, it is called threshold. Provide the smallest changes in the input that a sensor is able to sense.  Precision – It is the capacity of a measuring instrument to give the same reading when repetitively measuring the same quantity under the same prescribed conditions. It implies agreement between successive readings, NOT closeness to the true value. It is related to the variance of a set of measurements. It is a necessary but not sufficient condition for accuracy.  Sensitivity – Sensitivity indicates the ratio of incremental change in the response of the system with respect to incremental change in input parameters. It can be found from the slope of the output characteristics curve of a sensor. It is the smallest amount of difference in quantity that will change the instrument’s reading.  Linearity – The deviation of the sensor value curve from a particular straight line. Linearity is 33 CU IDOL SELF LEARNING MATERIAL (SLM)

determined by the calibration curve. The static calibration curve plots the output amplitude versus the input amplitude under static conditions. A curve’s slope resemblance to a straight line describes the linearity.  Drift – The difference in the measurement of the sensor from a specific reading when kept at that value for a long period of time.  Repeatability – The deviation between measurements in a sequence under the same conditions. The measurements have to be made under a short enough time duration so as not to allow significant long-term drift. Dynamic Characteristics: Properties of the systems  Zero-order system – The output shows a response to the input signal with no delay. It does not include energy- storing elements. Ex. potentiometer measure, linear and rotary displacements.  First-order system – When the output approaches its final value gradually. Consists of an energy storage and dissipation element.  Second-order system – Complex output response. The output response of the sensor oscillates before steady state. Sensor Classification:  Passive & Active  Analog & digital  Scalar & vector 1. Passive Sensor – Cannot independently sense the input. Ex- Accelerometer, soil moisture, water level and temperature sensors. 2. Active Sensor – Independently sense the input. Example- Radar, sounder and laser altimeter sensors. 3. Analog Sensor – 34 CU IDOL SELF LEARNING MATERIAL (SLM)

The response or output of the sensor is some continuous function of its input parameter. Ex- Temperature sensor, LDR, analog pressure sensor and analog hall effect. 4. Digital sensor – Response in binary nature. Design to overcome the disadvantages of analog sensors. Along with the analog sensor, it also comprises extra electronics for bit conversion. Example – Passive infrared (PIR) sensor and digital temperature sensor(DS1620). 5. Scalar sensor – Detects the input parameter only based on its magnitude. The answer for the sensor is a function of magnitude of some input parameter. Not affected by the direction of input parameters. Example – temperature, gas, strain, color and smoke sensor. 6. Vector sensor – The response of the sensor depends on the magnitude of the direction and orientation of input parameter. Example – Accelerometer, gyroscope, magnetic field and motion detector sensors. 3.4COMMUNICATION MODULES Standards Compliance The Communications Module shall be compliant with 3GPP specifications [1] unless MSC1 otherwise stated within this document. MSC2 The Communications Module shall be certified by the GCF and/or the PTCRB. The Communications Module shall investigate, and meet as required, the mobile network MSC3 operator requirements for the target market(s). Network Efficiency Requirements NER1 The Communications Module shall support (dependent upon the target mobile network operator) at least one of the following requirements:1) Radio Policy Manager (as defined in section 8 ) implemented within the Radio Baseband Chipset;OR 2) Connection Efficiency requirements (as defined in section 7) implemented within the 35 CU IDOL SELF LEARNING MATERIAL (SLM)

Communication Module Firmware; OR 3) 3GPP Connection Efficiency features (as defined in section 9) implemented within the Radio Baseband Chipset. Note: Option 3 requires the target mobile network operator to have implemented the required 3GPP optional features. NER3 If the Communications Module supports more than one family of communications access technology (for example 3GPP, TD-SCDMA, Wireless LAN) the device should implement a protection mechanism to prevent frequent ‘Ping-Pong’ between these different families of communications access technologies. NER4 When camping on a cell, the Communication Module shall support the mechanism to control the number of RRC Connection Establishment and temporal offset for cell selection as defined in 3GPP TS36.331 [3] IPv6 Requirements for Communication Modules that Support IPv6 The following requirements are only applicable to Communication Modules that support IPv6. IPv6 is the only perennial solution to IPv4 address exhaustion (public and private).  The final target is IPv6 only connectivity, once most of the Internet will be IPv6.  Remaining IPv4 services will be reachable through NAT64.  Before IPv6 only connectivity stage is reached, a dual stack will be used to push migration towards IPv6.  During the dual stack period, IPv4 rationalization solutions will be used. The IoT Communications Module should not send unsolicited messages (Router Solicitation IP1 for example). IP2 The IoT Communications Module should send only a AAAA DNS Query. IP3 The IoT Communications Module management system should be IPv6 based. 36 CU IDOL SELF LEARNING MATERIAL (SLM)

The IoT Communications Module shall support the following IPv6 functionality:  Neighbour Discovery Protocol (apart from the exceptions noted in 3GPP TS 23.060 (3G) or TS 23.401 (LTE) IP4  Stateless Address Auto Configuration  ICMPv6 protocol  IPv6 addressing architecture  IPv6 address text representation The IoT Communications Module should support the following IPv6 functionality:  Privacy Extensions for Stateless Address Auto-configuration in IPv6  ROHC for IPv6 IP5  IPv6 Router Advertisement Flags Options  Path MTU discovery  IPsec version 2 tunnel mode (IKE2) Requirements for Communication Modules that Support LTE The following requirements are only applicable to Communication Modules that support LTE. If voice calling over LTE is required by the IoT Service, the Communication Module CML1 should support VoLTE (Voice over LTE). Requirements for Communication Modules that Support Fast Dormancy The following requirements are only applicable to Communication Modules that support Fast Dormancy. The Fast Dormancy algorithm within the Communications Module should be triggered based on IoT Device data inactivity following suggested time parameters: CFD1  5 to 10 (the specific value in range is to be defined by Mobile Network Operator) seconds for networks with PCH RRC State support (URA-PCH or Cell PCH)  Trigger disabled for networks without PCH RRC State support (URA-PCH or Cell PCH) 37 CU IDOL SELF LEARNING MATERIAL (SLM)

The Communications Module should ensure that background IP or IMS data flows would not be suspended by the Signalling Connection Release Indication (SCRI). Fast Dormancy best practices from GSMA TS.18 “Fast Dormancy Best Practices” [14] shall be followed. (U)SIM Interface Requirements The Communications Module shall support (U)SIM OTA management. See 3GPP MSI1 TS31.102 [4] MSI2 The Communications Module should support remote provisioning as defined in GSMA SGP.01 “Remote Provisioning Architecture for Embedded UICC Technical Specification“ [5]. Security Requirements The Communications Module shall implement a unique global IMEI and protect it against MSR1 tampering. For details, please refer to 3GPP document TS 22.016 [6]. MSR2 The Communications Module shall detect the removal of a powered UICC and terminate all network connections and services authenticated by the (U)SIM application on that UICC.Upon the removal of a powered UICC all temporary network authentication data related to the UICC should be deleted by the Communications Module. MSR3 The Communications Module shall implement appropriate security measures to prevent unauthorized management (such as diagnostics, firmware updates etc) of the Communications Module. MSR4 The Communications Module shall implement a SIM lock function which allows the IoT Device to be locked to a specific UICC or range of UICCs. The state of the lock shall be configurable. The Communications Module should support a standards based over the air device DM1 management protocol such as OMA DM [8] or OMA LightweightM2M [15]. 38 CU IDOL SELF LEARNING MATERIAL (SLM)

The Communications Module should support a standards based firmware update DM2 mechanisms such as OMA FUMO [9]. The Communications Module should support a “reset to factory settings” via remote and DM3 local connection. [Should also be a corresponding DAR requirement for the application] The Communications Module should support “time resynchronisation” via remote and DM4 local connection. [Should also be a corresponding DAR requirement for the application] Subscription Identifier Requirements Given the large potential number of IoT Devices, some national numbering and identification plans have been extended to avoid numbering exhaustion. The structure of these identifiers (MSISDN/Directory numbers, IMSIs) are defined in ITU-T Recommendations E.164 and E.212, and 3GPP TS 23.003. IR1 The Communications Module shall support 15 digit Directory Numbers/MSISDNs. The Communications Module shall support 2 and 3 digit based Mobile Network Codes IR2 IMSIs. IoT Service Provider Requirements (Normative Section) MCR1 If permissible for the IoT Service, any IoT Service Platform which communicates to multiple IoT Devices shall avoid synchronized behaviour and employ a randomized pattern for accessing the IoT Devices within the IoT Service Platform’s domain. MCR2 If the (U)SIM subscription associated with an IoT Device is to be placed in a temporarily inactive state (i.e. the subscription is to be disabled for a fixed period of time), the IoT Service Provider shall first ensure that the IoT Device is temporarily disabled to restrict the device from trying to register to the network once the SIM is disabled.Before the (U)SIM subscription associated with an IoT Device is changed to a permanently terminated state, the IoT Service Provider shall ensure that the IoT Device is permanently disabled to stop the device from trying to register to the network once the SIM is permanently disabled.Note: The IoT Service Provider should carefully consider permanently terminating IOT devices which are not easily serviceable as it would require 39 CU IDOL SELF LEARNING MATERIAL (SLM)

manual intervention (i.e. a service call) to re-enable the IoT Device. MCR3 If the IoT Service Platform uses SMS triggers to wake up its IoT Devices, the IoT Service Platform should avoid sending multiple SMS triggers when no response is received within a certain time period. The IoT Service Platform should be aware of the state of the IoT Device and only send MCR4 ‘wake up’ triggers when the IoT Device is known to be attached to the mobile network. 3.5POWER SOURCES IoT deployments require the use of smart, miniaturized silicon-chip sensors that transmit data on a regular basis. Powering these oftentimes set-it-and-forget-it devices, sometimes installed in hard-to-reach or dangerous locations, is no easy feat. Until now, batteries have been primarily viewed as a starting point for powering IoT. However, energy-harvesting technologies, which derive power from external sources such as solar, thermal, wind and vibration, have set their sights on IoT. In fact, many new IoT systems now combine batteries with energy-harvesting capabilities to take advantage of ambient energy while having the assurance of a battery backup. So, what's the best way to power an IoT system -- energy-harvesting technology or batteries or both? As with any decision, it depends. \"The powering of IoT devices varies widely by industry,\" said Christian Renaud, analyst at 451 Research. \"What works for factory equipment is different than for a semi-truck or agricultural sensor. It varies by industry and even sub-use case.\" Energy harvesting emerges as a real-world IoT power option So far, energy harvesting hasn't really taken off, despite its enormous potential to power IoT. There are plenty of IoT use cases in which batteries are a great choice, especially if they involve only a handful of sensors. But smart cities or large industrial installations with thousands of sensors with batteries that need to be changed often -- even every few years -- open the door to a risk of losing data. \"In smart buildings, where there may be retrofit or brownfield applications, we're seeing energy-harvesting use cases for occupancy sensors for lighting, heating, ventilation and air conditioning to lower energy costs for the building,\" Renaud said. Energy-harvesting technologies have come a long way in the past 10 years. Early installations for building energy management systems -- for example, turning lights off and rerouting 40 CU IDOL SELF LEARNING MATERIAL (SLM)

airflow from areas no one is using -- were efficient and saved money, but really only made sense for buildings in excess of 55,000 square feet. \"The installation costs early on were unreasonable for many players, so we decided to reduce the installation part and simplify it, which opens up a larger section of that market,\" said Christian Pennisi, director of operations at Jennova, a provider of energy-harvesting technologies. Energy harvesting offers the reliability of hardwiring, but with the versatility of simplified installation you get from a battery. \"We recommend an integrated backup battery with the energy harvester so that you're able to keep that battery above 70% capacity, and it can provide power if needed,\" Pennisi said. \"We view it as a fusion between energy harvesting and battery storage -- instead of being one or the other.\" Jennova offers a power management circuit designed to work with any energy-harvesting transducer. \"You have your transducer, which depends on how you want to harvest energy -- piezoelectric, thermoelectric, turboelectric, vibration, photovoltaic or whatever it might be -- and this one circuit board takes the input and converts it into usable power, amplifies the voltage and makes sure you're getting good DC out,\" Pennisi explained. \"It also has an integrated battery management chip that's intelligent enough to help keep the battery recharged to power the device directly when enough energy harvesting isn't available.\" While Jennova is currently still doing R&D into other types of energy harvesting, the company's moving toward what it expects to be the \"big three\" energy-harvesting platforms: photovoltaic, ambient wireless energy and vibration. \"These will probably be the winners, because we can get the costs down to a point that the market will bear,\" Pennisi said, adding that \"they'll also be the least intrusive installations.\" Enterprises are finally seeing the value of energy harvesting, so they're starting to take action. \"Instead of being hype, people are understanding the benefits and asking if there's a solution that might work for them,\" Pennisi said. \"Companies are more likely to engage in finding those solutions now.\" Don't count batteries out just yet While energy-harvesting is making inroads in IoT, many companies are taking the IoT power challenge into their own hands, lowering the power consumption of their devices to alleviate demands placed on batteries. Key areas of design affecting power consumption are connectivity and range. For example, the more remote the deployment, the longer the battery life needs to be to remove the requirement for frequent replacements. Every truck roll means lost margins and a longer ROI curve, according to Renaud. \"This is helping drive interest in low-power wide area networking, such as LoRa and [Narrowband-IoT], because traditional cellular connections would blow through even larger batteries within days or weeks,\" he explained. 41 CU IDOL SELF LEARNING MATERIAL (SLM)

In short, connectivity drives power requirements. The future of powering IoT sensors and devices In the R&D realm, a myriad of energy-harvesting scenarios for powering IoT sensors and devices are emerging, as well as systems to reduce power usage. One example of the future direction powering IoT might take comes from the work of a group of researchers at MIT to develop a fully flexible device that converts energy from Wi- Fi signals into electricity. \"We've come up with a new way to power the electronics systems of the future -- by harvesting Wi-Fi energy in a way that's easily integrated in large areas -- to bring intelligence to every object around us,\" said Tomás Palacios, a professor in the department of electrical engineering and computer science at MIT. The group created a \"rectenna\" -- a device that converts AC electromagnetic waves into DC electricity -- which uses a flexible radio frequency antenna to capture electromagnetic waves -- including those carrying Wi-Fi -- as AC waveforms. The antenna is then connected to a device made out of a two-dimensional semiconductor that's just a few atoms thick. An AC signal goes into the semiconductor, which converts it into a DC voltage that can be used to power electronic circuits or recharge batteries. This battery-free device passively captures and transforms ubiquitous Wi-Fi signals into useful DC power, and since the device is flexible, it can be fabricated in a roll-to-roll process to cover very large areas. In experiments, the group's rectenna produced about 40 microwatts of power when exposed to the typical power level of Wi-Fi, which is around 150 microwatts -- more than enough power to light up an LED or drive silicon chips. Another glimpse of the path powering IoT devices might take, thanks to researchers at the National University of Singapore (NUS), is a picowatt-power wake-up timer that cuts minimum power consumption of IoT sensor nodes by 1,000 times. This inexpensive \"battery-less\" wake-up timer is an on-chip circuit that has demonstrated power consumption down to true picowatt range -- 1 billion times lower than a smartwatch. The wake-up timer \"doesn't need any additional circuitry, as opposed to conventional technologies, which require peripheral circuits that consume at least 1,000 times more power -- for example, voltage regulators,\" said Massimo Alioto, an associate professor in the department of electrical and computer engineering at NUS. \"This is a step toward accelerating the development of IoT infrastructure and paves the way for the aggressive miniaturization of IoT devices for long-lasting operations.\" The NUS invention reduces the power consumption of wake-up timers embedded in IoT sensor nodes. \"Under typical office lighting, our novel wake-up timer can be powered by a 42 CU IDOL SELF LEARNING MATERIAL (SLM)

very small on-chip solar cell that has a diameter similar to that of a strand of human hair. It can also be sustained by millimeter-scale battery for decades,\" Alioto said. These are just two quick, recent examples of the amazing work being done by researchers around the globe to help make it easier to power the future of IoT. 3.6COMMUNICATION TECHNOLOGIES IoT is connection of devices over internet, where these smart devices communicate with each other , exchange data , perform some tasks without any human involvement. These devices are embedded with electronics, software, network and sensors which help in communication. Communication between smart devices is very important in IOT as it enables these devices to gather, exchange data which contribute in success of that IOT product/project. Types of Communications in IOT : The following are some communication types in IoT:- 1. Human to Machine (H2M) : In this human gives input to IOT device i.e as speech/text/image etc. IOT device (Machine) like sensors and actuators then understands input, analyses it and responds back to human by means of text or Visual Display. This is very useful as these machines assist humans in every everyday tasks. It is a combo of software and hardware that includes human interaction with a machine to perform a task. Fig 3.2 H2M communication Merits: This H2M has a user-friendly interface that can be quickly accessed by following the instructions. It responds more quickly to any fault or failure. Its features and functions can be customized. Examples: 43 CU IDOL SELF LEARNING MATERIAL (SLM)

 Facial recognition.  Bio-metric Attendance system.  Speech or voice recognition. 2. Machine to Machine (M2M) : In this the interaction or communication takes place between machines by automating data/programs. In this machine level instructions are required for communication. Here communication takes place without human interaction. The machines may be either connected through wires or by wireless connection. An M2M connection is a point-to-point connection between two network devices that helps in transmitting information using public networking technologies like Ethernet and cellular networks. IoT uses the basic concepts of M2M and expands by creating large “cloud” networks of devices that communicate with one another through cloud networking platforms. Fig 3.3 M2M communication Advantages – This M2M can operate over cellular networks and is simple to manage. It can be used both indoors and outdoors and aids in the communication of smart objects without the need for human interaction. The M2M contact facility is used to address security and privacy problems in IoT networks. Large-scale data collection, processing, and security are all feasible. Disadvantages – However, in M2M, use of cloud computing restricts versatility and creativity. Data security and ownership are major concerns here. The challenge of achieving interoperability between cloud/M2M IoT systems is daunting. M2M connectivity necessitates the existence of a reliable internet connection. Examples:  Smart Washing machine sends alerts to the owners’ smart devices after completion of washing or drying of clothes.  Smart meters tracks amount of energy used in household or in companies and automatically alert the owner. 44 CU IDOL SELF LEARNING MATERIAL (SLM)

3. Machine to Human (M2H) : In this machine interacts with Humans. Machine triggers information(text messages/images/voice/signals) respective / irrespective of any human presence. This type of communication is most commonly used where machines guide humans in their daily life. It is way of interaction in which humans co-work with smart systems and other machines by using tools or devices to finish a task. Fig 3.4 M2H communication Examples:  Fire Alarms  Traffic Light  Fitness bands  Health monitoring devices 4. Human to Human (H2H) : This is generally how humans communicate with each other to exchange information by speech, writing, drawing, facial expressions, body language etc. Without H2H, M2M applications cannot produce the expected benefits unless humans can immediately fix issues, solve challenges, and manage scenarios. Fig 3.5 H2H communication 45 CU IDOL SELF LEARNING MATERIAL (SLM)

For, communication of IoT devices many protocols are used. These IoT protocols are modes of communication which give security to the data being exchanged between IoT connected devices. Example bluetooth, wifi, zigbee etc. 3.7 RFID In recent years, radio frequency identification technology has moved from obscurity into mainstream applications that help speed the handling of manufactured goods and materials. RFID enables identification from a distance, and unlike earlier bar-code technology (see the sidebar), it does so without requiring a line of sight. 1 RFID tags (see figure 1) support a larger set of unique IDs than bar codes and can incorporate additional data such as manufacturer, product type, and even measure environmental factors such as temperature. Furthermore, RFID systems can discern many different tags located in the same general area without human assistance. In contrast, consider a supermarket checkout counter, where you must orient each bar-coded item toward a reader before scanning it. Fig 3.6Three different RFID tags They come in all shapes and sizes RFID principles Many types of RFID exist, but at the highest level, we can divide RFID devices into two classes: active and passive. Active tags require a power source—they're either connected toa powered infrastructure or use energy stored in an integrated battery. In the latter case, a tag's lifetime is 46 CU IDOL SELF LEARNING MATERIAL (SLM)

limited by the stored energy, balanced against the number of read operations the device must undergo. One example of an active tag is the transponder attached to an aircraft that identifies its national origin. Another example is a LoJack device attached to a car, which incorporates cellular technology and a GPS to locate the car if stolen. However, batteries make the cost, size, and lifetime of active tags impractical for the retail trade. Passive RFID is of interest because the tags don't require batteries or maintenance. The tags also have an indefinite operational life and are small enough to fit into a practical adhesive label. A passive tag consists of three parts: an antenna, a semiconductor chip attached to the antenna, and some form of encapsulation. The tag reader is responsible for powering and communicating with a tag. The tag antenna captures energy and transfers the tag's ID (the tag's chip coordinates this process). The encapsulation maintains the tag's integrity and protects the antenna and chip from environmental conditions or reagents. The encapsulation could be a small glass vial or a laminar plastic substrate with adhesive on one side to enable easy attachment to goods Fig 3.7 RFID Tags RFID tags based on near-field coupling: (a) a 128 kHz Trovan tag, encapsulated in a small glass vial that's approximately 1 cm long and (b) a 13.56 MHz Tiris tag, which has a laminar plastic substrate (approximately 5 × 5 cm) with adhesive for easy attachment to goods. Two fundamentally different RFID design approaches exist for transferring power from the reader to the tag: magnetic induction and electromagnetic (EM) wave capture. These two designs take advantage of the EM properties associated with an RF antenna—the near field and the far field. Both can transfer enough power to a remote tag to sustain its operation—typically between 10 μW and 1 mW, depending on the tag type. (For comparison, the nominal power an Intel XScale processor consumes is approximately 500 mW, and an Intel Pentium 4 consumes up to 50 W.) Through various modulation techniques, near- and far-field-based signals can also transmit and receive data. 47 CU IDOL SELF LEARNING MATERIAL (SLM)

Near-field RFID Faraday's principle of magnetic induction is the basis of near-field coupling between a reader and tag. A reader passes a large alternating current through a reading coil, resulting in an alternating magnetic field in its locality. If you place a tag that incorporates a smaller coil in this field, an alternating voltage will appear across it. If this voltage is rectified and coupled to a capacitor, a reservoir of charge accumulates, which you can then use to power the tag chip. Fig 3.7 Near-field power/communication mechanism for RFID tags operating at less than 100 MHz. Tags that use near-field coupling send data back to the reader using load modulation. Because any current drawn from the tag coil will give rise to its own small magnetic field—which will oppose the reader's field—the reader coil can detect this as a small increase in current flowing through it. This current is proportional to the load applied to the tag's coil (hence load modulation). This is the same principle used in power transformers found in most homes today—although usually a transformer's primary and secondary coil are wound closely together to ensure efficient power transfer. However, as the magnetic field extends beyond the primary coil, a secondary coil can still acquire some of the energy at a distance, similar to a reader and a tag. Thus, if the tag's electronics applies a load to its own antenna coil and varies it over time, a signal can be encoded as tiny variations in the magnetic field strength representing the tag's ID. The reader can then recover this signal by monitoring the change in current through the reader coil. A variety of modulation encodings are possible depending on the number of ID bits required, the data transfer rate, and additional redundancy bits placed in the code to remove errors resulting from noise in the communication channel. 48 CU IDOL SELF LEARNING MATERIAL (SLM)

Near-field coupling is the most straightforward approach for implementing a passive RFID system. This is why it was the first approach taken and has resulted in many subsequent standards, such as ISO 15693 and 14443, and a variety of proprietary solutions. However, near-field communication has some physical limitations. The range for which we can use magnetic induction approximates to c/2π f, where c is a constant (the speed of light) and f is the frequency. Thus, as the frequency of operation increases, the distance over which near-field coupling can operate decreases. A further limitation is the energy available for induction as a function of distance from the reader coil. The magnetic field drops off at a factor of 1/ r3, where r is the separation of the tag and reader, along a center line perpendicular to the coil's plane. So, as applications require more ID bits as well as discrimination between multiple tags in the same locality for a fixed read time, each tag requires a higher data rate and thus a higher operating frequency. These design pressures have led to new passive RFID designs based on far-field communication. Far-field RFID RFID tags based on far-field emissions (see figure 4) capture EM waves propagating from a dipole antenna attached to the reader. A smaller dipole antenna in the tag receives this energy as an alternating potential difference that appears across the arms of the dipole. A diode can rectify thispotential and link it to a capacitor, which will result in an accumulation of energy in order to power its electronics. However, unlike the inductive designs, the tags are beyond the range of the reader's near field, and information can't be transmitted back to the reader using load modulation. Fig 3.8 RFID tags based on far-field coupling: (a) a 900-MHz Alien tag (16 × 1 cm) and (b) a 2.45-GHz Alien tag (8 × 5 cm). The technique designers use for commercial far-field RFID tags is back scattering (see figure 5). If they design an antenna with precise dimensions, it can be tuned to a particular frequency and absorb most of the energy that reaches it at that frequency. However, if an impedance mismatch occurs at this frequency, the antenna will reflect back some of the energy (as tiny waves) toward the reader, which can then detect the energy using a sensitive 49 CU IDOL SELF LEARNING MATERIAL (SLM)

radio receiver. By changing the antenna's impedance over time, the tag can reflect back more or less of the incoming signal in a pattern that encodes the tag's ID. Fig 3.9 Far-field power/communication mechanism for RFID tags operating at greater than 100 MHz. In practice, we can detune a tag's antenna for this purpose by placing a transistor across its dipole and then turning it partially on and off. As a rough design guide, tags that use far-field principles operate at greater than 100 MHz typically in the ultra high-frequency (UHF) band (such as 2.45 GHz); below this frequency is the domain of RFID based on near-field coupling. A far-field system's range is limited by the amount of energy that reaches the tag from the reader and by how sensitive the reader's radio receiver is to the reflected signal. The actual return signal is very small, because it's the result of two attenuations, each based on an inverse square law—the first attenuation occurs as EM waves radiate from the reader to the tag, and the second when reflected waves travel back from the tag to the reader. Thus the returning energy is 1/ r4 (again, r is the separation of the tag and reader). Fortunately, thanks to Moore's law and the shrinking feature size of semiconductor manufacturing, the energy required to power a tag at a given frequency continues to decrease (currently as low as afew microwatts). So, with modern semiconductors, we can design tags that can be read at increasingly greater distances than were possible a few years ago. Furthermore, inexpensive radio receivers have been developed with improved sensitivity so they can now detect signals, for a reasonable cost, with power levels on the order of –100 dBm in the 2.4-GHz band. A typical far-field reader can successfully interrogate tags 3 m away, and some RFID companies claim their products have read ranges of up to 6 m. 50 CU IDOL SELF LEARNING MATERIAL (SLM)