Assignment

Figure 13.1 Full Alternative Text

8. Identify decision support possibilities.

9. Which decisions made by the company and its customers are supported by IoT?

What We Can Learn from This vignette

First, we learned how IoT provides an infrastructure for new types of applications that connect thousands of items to a decision-making center.

Second, we learned about the flow of data collected by sensors from vehicles and the environment around them and their transmittal for analytical processing.

Third, the manufacturer of the vehicles and their owners and users can enjoy tremendous benefits from using the system.

Finally, this, IoT provides an efficient communication and collaboration framework for decision makers, the manufacturer’s organization, and the users of the purchased equipment.

In this chapter, we elaborate on the technologies involved and the process of the IoT operation. We also describe its major application in enterprises, homes, smart cities, and autonomous (smart) vehicles.

13.2 Essentials of IoT

The  Internet of Things (IoT)  is an evolving term with several definitions. In general, IoT refers to a computerized network that connects many objects (people, animals, devices, sensors, buildings, items) each with an embedded microprocessor. The objects are connected, mostly wirelessly, to the Internet forming the IoT. The IoT can exchange data and allow communication among the objects and with their environments. That is, the IoT allows people and things to be interconnected anytime and anyplace. Embedded sensors that collect and exchange data make up a major portion of the objects and the IoT. That is, IoT uses ubiquitous computing. Analysts predict that by the year 2025, more than 50 billion50 billion devices (objects) will be connected to the Internet, creating the backbone of IoT applications. The challenges and opportunities of this disruptive technology (e.g., for cutting costs, creating new business models, improving quality) are discussed in an interview with Peter Utzschneider, vice president of product management for Java at Oracle (see Kvitka, 2014). In addition, you can join the conversations at iotcommunity.com. For Intel’s vision of a fully connected world, see Murray (2016).

Embedding computers and other devices that can be switched on and off into active items anywhere and connecting all devices to the Internet (and/or to each other) permit extensive communication and collaboration between users and items. By connecting many devices that can talk to each other, one can create applications with new functionalities, increase the productivity of existing systems, and drive the benefits discussed later. This kind of interaction opens the door to many applications. For business applications of the Internet of Things, see Jamthe (2016). In addition, check the “Internet of Things Consortium” (iofthings.org) and its annual conferences. For an infographic and a guide, see intel.com/content/www/us/en/internet-of-things/infographics/guide-to-iot.html.

Definitions and Characteristics

There are several definitions of IoT.

Kevin Ashton, who is credited with the term the “Internet of Things,” provided the following definition: “The Internet of Things means sensors connected to the Internet and behaving in an Internet-like way by making open, ad hoc connections, sharing data freely, and allowing unexpected applications, so computers can understand the world around them and become humanity’s nervous system” (term delivered first in a 1999 oral presentation. See Ashton, 2015).

Our working definition is:

The IoT is a network of connected computing devices including different types of objects (e.g., digital machines). Each object in the network has a unique identifier (UID), and it is capable of collecting and transferring data automatically across the network.

The collected data has no value until it is analyzed, as illustrated in the opening vignette.

Note that the IoT allows people and things to interact and communicate at any time, any place, regarding any business topic or service.

According to Miller (2015), the IoT is a connected network in which:

· Large numbers of objects (things) can be connected.

· Each thing has a unique definition (IP address).

· Each thing has the ability to receive, send, and store data automatically.

· Each thing is delivered mostly over the wireless Internet.

· Each thing is built upon machine-to-machine (M2M) communication.

Note that, in contrast with the regular Internet that connects people to each other using computing technology, the IoT connects “things” (physical devices and people) to each other and to sensors that collect data. In Section 13.4, we explain the process of IoT.

Simple Examples

A common example of the IoT is the autonomous vehicle (Section 13.9). To drive on its own, a vehicle needs to have enough sensors that automatically monitor the situation around the car and take appropriate actions whenever necessary to adjust any setting, including the car’s speed, direction, and so on. Another example that illustrates the IoT phenomenon is the company Smartbin. It has developed trash containers that include sensors to detect their fill levels. The trash collection company is automatically notified to empty a trash container when the sensor detects that the bin has reached the fill level.

A common example people give to illustrate IoT is the idea that a refrigerator could automatically order food (e.g., milk) when it detects that the food has run out! Clorox introduced a new Brita filter so that a Wi-Fi–enabled mechanism can order water filters by itself when it detects that it is time to change them. In these examples, a human does not have to communicate with another human or even with a machine.

IoT is Changing Everything

According to McCafferty (2015), the IoT is changing everything. This has been verified by a 2016 survey reported by Burt (2016). For how manufacturing is revolutionized by IoT, see Greengard (2016). Here are a few examples that he provided:

· “Real-time systems make it possible to know where anyone is at any moment, which is helpful to secured locations as military bases and seeking to push promotions to consumers.”

· “Fleet tracking systems allow logistics and transport firms to optimize routing, track vehicle speeds and locations, and analyze driver and route efficiencies.”

· “Owners and operators of jet engines, trains, factory equipment, bridges, tunnels, etc., can stay ahead of repairs through machines that monitor for preventive maintenance.” (opening case)

· “Manufacturers of foods, pharmaceuticals and other products monitor temperature, humidity and other variables to manage quality control, receiving instant alerts when something goes wrong.”

These changes are facilitated by AI systems, which enhance analytics and automate or support decision making.

The IoT Ecosystem

When billions of things are connected to the Internet with all the supporting services and connected IT infrastructure, we can see a giant complex, which can be viewed as a huge ecosystem. The  Internet of Things ecosystem  refers to all components that enable users to create IoT applications. These components include gateways, analytics, AI algorithms, servers, data storage, security, and connectivity devices. A pictorial view is provided in Figure 13.1 in which applications are shown on the left side and the building blocks and platforms on the right side. An example of an IoT application is provided in the opening vignette. It illustrates a network of sensors that collects information, which is transmitted to a central place for processing and eventually for decision support. Thus, the IoT applications are subsets of the IoT ecosystem.

A basic discussion, terms, major companies, and platforms is provided by Meola (2018).

Structure of IoT Systems

Things in IoT refers to a variety of objects and devices ranging from cars and home appliances to medical devices, computers, fitness tracers, hardware, software, data, sensors, and much more. Connecting things and allowing them to communicate is a necessary capability of an IoT application; but for more sophisticated applications, we need additional components: a control system and a business model. The IoT enables the things to sense or be sensed wirelessly across the network. A non-Internet example is a temperature control system in a room. Another non-Internet example is a traffic signal at intersections of roads where camera sensors recognize the cars coming from each direction and a control system adjusts the time for changing the lights according to programmed rules. Later, we will introduce the reader to many Internet-based applications.

IoT Technology Infrastructure

From a bird’s-eye view, IoT technology can be divided into four major blocks. Figure 13.2 illustrates them.

1. HARDWARE: This includes the physical devices, sensors, and actuators where data are produced and recorded. The devices are the equipment that needs to be controlled, monitored, or tracked. IoT sensor devices could contain a processor or any computing device that parses incoming data.

2. CONNECTIVITY: There should be a base station or hub that collects data from the sensor-laden objects and sends those data to the “cloud” to be analyzed. Devices are connected to a network to communicate with other networks or other applications. These may be directly connected to the Internet. A gateway enables devices that are not directly connected to the Internet to reach the cloud platform.

3. SOFTWARE BACKEND: In this layer, the data collected are managed. Software backend manages connected networks and devices and provides data integration. This may very well be in the cloud.

4. APPLICATIONS: In this part of IoT, data are turned into meaningful information. Many of the applications can run on smartphones, tablets, and PCs and do something useful with the data. Other applications can run on the server and provide results or alerts through dashboards or messages to the stakeholders.

To assist with the construction of IoT systems, one may use IoT platforms. For information, see Meola (2018).

IoT Platforms

Because IoT is still evolving, many domain-specific and application-specific technology platforms are also evolving. Not surprisingly, many of the major vendors of IoT platforms are the same ones who provide analytics and data storage services for other application domains. These include Amazon AWS IoT, Microsoft Azure IoT suite, Predix IoT Platform by General Electric (GE), and IBM Watson IoT platform (ibm.com/us-en/marketplace/internet-of-things-cloud). Teradata Unified Data Architecture has similarly been applied by many customers in the IoT domain.

Section 13.2 Review Questions

1. What is IoT?

2. List the major characteristics of IoT.

3. Why is IoT important?

4. List some changes introduced by IoT.

5. What is the IoT ecosystem?

6. What are the major components of an IoT technology?

13.3 Major Benefits and Drivers of IoT

The major objective of IoT systems is to improve productivity, quality, speed, and the quality of life. There are potentially several major benefits from IoT, especially when combined with AI, as illustrated in the opening case. For a discussion and examples, see Jamthe, 2015.

Major Benefits of IoT

The following are the major benefits of IoT:

· Reduces cost by automating processes.

· Improves workers’ productivity.

· Creates new revenue streams.

· Optimizes asset utilization (e.g., see the opening vignette).

· Improves sustainability.

· Changes and improves everything.

· May anticipate our needs (predictions).

· Enables insights into broad environments (sensors collect data).

· Enables smarter decisions/purchases.

· Provides increased accuracy of predictions.

· Identifies problems quickly (even before they occur).

· Provides instant information generation and dissemination.

· Offers quick and inexpensive tracking of activities.

· Makes business processes more efficient.

· Enables communication between consumers and financial institutions.

· Facilitates growth strategy.

· Fundamentally improves the use of analytics (see the opening vignette).

· Enables better decision making based on real-time information.

· Expedites problem resolution and malfunction recovery.

· Supports facility integration.

· Provides better knowledge about customers for personalized services and marketing.

Major Drivers of IoT

The following are the major drivers of IoT:

· The number of “things”—20 to 50 billion50 billion—may be connected to the Internet by 2020–2025.

· Connected autonomous “things”/systems (e.g., robots, cars) create new IoT applications.

· Broadband Internet is more widely available, increasing with time.

· The cost of devices and sensors is continuously declining.

· The cost of connecting the devices is decreasing.

· Additional devices are created (via innovations) and are interconnected easily (e.g., see Fenwick, 2016).

· More sensors are built into devices.

· Smartphones’ penetration is skyrocketing.

· The availability of wearable devices is increasing.

· The speed of moving data is increasing to 60 THz.60 THz.

· Protocols are developing for IoT (e.g., WiGig).

· Customer expectations are rising; innovative customer services are becoming a necessity.

· The availability of IoT tools and platforms is increasing.

· The availability of powerful analytics that are used with IoT is increasing.

Opportunities

The benefits and drivers just listed create many opportunities for organizations to excel in the economy (e.g., Sinclair, 2017), in many industries and in different settings.

McKinsey Global Institute (Manyika et al., 2015) provides a comprehensive list of settings where IoT is or can be used with examples in each setting. A 2017 study (Staff, 2017) revealed a dramatic increase in the capabilities and benefits of IoT.

How Big Can an IoT Network Be?

While there will be billions of things connected to the Internet soon, not all of them will be connected in one IoT network. However, an IoT network can be very large, as we show next.

Example: World’s Largest IoT Is Being Built in India (2017)

This network is being constructed by Tata Communications of India and HP Enterprises (HPE) of the United States, over the HPE Universal IoT Platform. The things to be connected exist in 2,000 communities and include computing devices, applications, and IoT solutions, connected over the Lo Ra network, a wireless communication protocol for wide area networks. The things are in smart buildings, utilities, university campuses, security systems, vehicles and fleets, and healthcare facilities.

The project is to be implemented in phases with proof-of-concept applications to be tested first. The network will bring services to 400 million400 million people. For details, see Shah (2017).

Section 13.3 Review Questions

1. List the benefits of IoT for enterprises.

2. List the benefits of IoT for consumers.

3. List the benefits of IoT for decision making.

4. List the major drivers of IoT.

13.4 How IoT Works

IoT is not an application. It is an infrastructure, platform, or framework that is used to support applications. The following is a comprehensive process for IoT applications. In many cases, IoT follows only portions of this process.

The process is explained in Figure 13.3. The Internet ecosystem (top of the figure) includes a large number of things. Sensors and other devices collect information from the ecosystem. The collected information can be displayed, stored, and processed analytically (e.g., by data mining). This analysis converts the information into knowledge and/or intelligence. Expert systems or machine learning may help in turning the knowledge into decision support (made by people and/or machines), which is evidenced by improved actions and results.

Figure 13.3 The Process of IoT.

Figure 13.3 Full Alternative Text

The generated decisions can help in creating innovative applications, new business models, and improvements in business processes These result in “actions,” which may impact the original scenario or other things. The opening vignette illustrates this process.

Note that most of the existing applications are in the upper part of the figure, which is called sensor to insight, meaning up to the creation of knowledge or to the delivery of new information. However, now, the focus is moving to the entire cycle (i.e., sensor to action).

The IoT may generate a huge amount of data (Big Data) that needs to be analyzed by various business intelligence methods, including deep learning, or advanced AI methods.

IoT and Decision Support

As stated earlier, the IoT creates knowledge and/or intelligence, which is submitted as support to decision makers or is inputted to automated decision support entities. The transition from data collection to decision support may not be simple due to the large amount of data, some of which are irrelevant. Large-scale IoT usually needs to filter the collected data and “clean” them before they can be used for decision support, particularly if they are used as a base for automated decision making.

Section 13.4 Review Questions

1. Describe the major components of IoT.

2. Explain how the IoT works following the process illustrated in Figure 13.3.

3. How does IoT support decision making?

13.5 Sensors and Their Role in IoT

As illustrated in the opening vignette to this chapter, sensors play a major role in IoT by collecting data about the performance of the things that are connected to the Internet and monitoring the surrounding environment, collecting data there too if necessary. Sensors can transmit data and sometimes even process it prior to transmission.

Brief Introduction to Sensor Technology

A  sensor  is an electronic device that automatically collects data about events or changes in its environment. Many IoT applications include sensors (see the opening vignette). The collected data are sent to other electronic devices for processing. There are several types of sensors and several methods for collecting data. Sensors often generate signals that are converted to human-readable displays. In addition to their use in IoT, sensors are essential components in robotics and autonomous vehicles. Each sensor usually has a limit on the maximum distance that it can detect (nominal range). Sensors of a very short range known as proximity sensors are more reliable than those that operate in larger ranges. Each IoT network may have millions of sensors. Let us see how sensors work with IoT in Application Case 13.1.

Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens, Greece, International Airport

The Problem

Over 20 million20 million passengers use the airport annually, and their number increases by more than 10 percent every year. Obviously, the number of flights is large and also increasing annually. The growth increases air pollution as well. The airport has a strong commitment to environmental protection, so management has looked for an environmental control solution. The objective was to make the airport carbon neutral. The large number of planes in the air and on the ground and the fact that airplanes frequently move require advanced technologies for the solution.

The Solution

A reasonable way to deal with moving airplanes was to use IoT, a technology that when combined with AI-based sensors enables environmental monitoring, analysis, and reporting, all of which provide the background information for decisions regarding minimizing the air pollution.

Two companies combined their expertise for this project: EXM of Greece, which specializes in IoT prediction analytics and innovative IoT solutions, and Libelium of the United States, which specializes in AI-related sensors, including those for environmental use. The objective of the project was to properly monitor air quality inside and outside the airport and to identify, in real time, the aircraft location on the ground and to take corrective actions whenever needed.

Ad Hoc Air Quality Monitoring and Analysis

The airport now has an air quality monitoring network. The solution includes Libelium’s sensor platform connected in a cost-effective manner. The different sensors measure temperature, humidity, atmosphere pressure, ozone level, and particulate matter. The readings of the sensors are transmitted to IoT for reporting and then analysis. The sensors were improved by using AI features. Therefore, their accuracy increased. In addition, security and energy consumption are also being controlled.

Aircraft Location at the Airport

To identify the exact location of the aircrafts during takeoff and landing, the project uses acoustic measurement mechanisms. This is accomplished by using noise sensors placed in different locations. The sensors measure real-time noise level, which is evaluated by analytics. Overall, the system provides a noninvasive IoT solution.

Placement of sensors was difficult due to safety, security, and regulation considerations. Therefore, the sound monitoring subsystem had to be self-managed (autonomous), bearing solar panels and batteries that provided the electricity. In addition, the system utilizes a dual wireless communication system (known as GPPS).

The collected noise data are correlated with types of airplane and flights at the IoT backend. All data are analyzed by the airport environmental department and used for decisions regarding improvements of pollution control.

Technology Support

The solution combines an IoT system with AI-based analytics, visualization, and reporting and is executed in the cloud. In addition, the system has on-site sensors and communication infrastructures. Low-power wireless sensors monitor water and gas consumption indoors as well as air quality in the parking sites. Vendors’ products, such as Microsoft Azure and IBM Bluemix, support the project and provide the necessary flexibility.

Sources: Compiled from Hedge (2017) and Twentyman (2017).

Questions for Case 13.1

1. What is the role of IoT in the project?

2. What is the role of sensors?

3. What are the benefits of the project?

How Sensors Work with IoT

In large-scale applications, sensors collect data that are transferred to processing in the “cloud.” Several platforms are used for this process as discussed in Application Case 13.2.

Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures

Rockwell Automation is one of the world’s largest providers of industrial automation and information solutions. It has customers in more than 80 countries worldwide and around 22,500 employees. One of its business areas of focus is assisting oil and gas companies in exploration. An example is Hilcorp Energy, a customer company that drills oil in Alaska. The equipment used in drilling, extracting, and refining oil is very expensive. A single fault in the equipment can cost the company around $100,000 to $300,000$100,000 to $300,000 per day in lost production. To deal with this problem, it needed technology to monitor the status of such piece of equipment remotely and to predict failures that are likely to happen in the future.

Rockwell Automation considered the opportunity to expand its business in oil and gas industries by gathering data from the exploration sites and analyzing them to improve preventive maintenance decision making regarding the critical equipment, thus, minimizing downtime and drive better performance. The company utilizes its vision of Connected Enterprise with Microsoft’s software to monitor and support oil and gas equipment placed in remote areas. Rockwell is now providing solutions to predict failure of equipment along the entire petroleum supply chain, monitoring its health and performance in real time, and to prevent failures in the future. Solutions are provided in the following areas.

· DRILLING: Hilcorp Energy has its pumping equipment stationed in Alaska where it drills for oil 24 hours a day. A single failure in equipment can cost Hilcorp a large amount of money. Rockwell connected electrical variable drives of pumping equipment to be processed in the “cloud,” to control its machines thousands of miles away from the control room in Ohio. Sensors capture data, and through Rockwell’s control gateway, these data are passed to Microsoft Azure Cloud. The solutions derived reach Hilcorp engineers through digital dashboards that provide real-time information about pressure, temperature, flow rate, and dozens of other parameters that help engineers monitor the equipment’s health and performance. These dashboards also display alerts about any possible issues. When one of Hilcorp’s pieces of pumping equipment failed, it was identified, tracked, and repaired in less than an hour, saving six hours of tracing the failure and the large cost of lost production.

· BUILDING SMARTER GAS PUMPS: Today, some delivery trucks use liquid natural gas (LNG) as fuel. Oil companies are updating their filling stations to incorporate LNG pumps. Rockwell Automation installed sensors and variable frequency drives at these pumps to collect real-time data about equipment operations, fuel inventory, and consumption rate. This data are transmitted to Rockwell’s cloud platform for processing. Rockwell then generates interactive dashboards and reports using Microsoft Azure (an IoT platform). Results are forwarded to the appropriate stakeholders, giving them a good idea about the health of their capital assets.

The Connected Enterprise solution by Rockwell has accelerated growth for many oil and gas companies like Hilcorp Energy by bringing their operations data to the cloud platform and helping them reduce costly downtime and maintenance. It has resulted in a new business opportunity for industrial age stalwarts like Rockwell Automation.

Sources: customers.microsoft.com (2015); Rockwell Automation: Fueling the Oil and Gas Industry with IoT; https://customers.microsoft.com/Pages/CustomerStory.aspx?recid=19922; Microsoft.com. (n.d.). “Customer Stories | Rockwell Automation,” https://www.microsoft.com/en-us/cloud-platform/customer-stories-rockwell-automation (accessed April 2018).

Questions for Case 13.2

1. What type of information would likely be collected by an oil and gas drilling platform?

2. Does this application fit the three V’s (volume, variety, velocity) of Big Data? Why or why not?

3. Which other industries (list five) could use similar operational measurements and dashboards?

Sensor Applications and Radio-Frequency Identification (RFID) Sensors

There are many types of sensors. Some measure temperature; others measure humidity. Many sensors collect information and transmit it as well. For a list of 50 sensor applications with a large number of related articles, see libelium.com/resources/top_50_iot_sensor_applications_ranking/.

A well-known type of sensor that plays an important role in IoT is radio-frequency identification.

RFID Sensors

Radio-frequency identification (RFID)  is part of a broader ecosystem of data capture technologies. Several forms of RFID in conjunction with other sensors play a major role in IoT applications. Let us see first what RFID is, as discussed in Technology Insights 13.1.

Technology Insights 13.1 RFID Sensors

RFID is a generic technology that refers to the use of radio-frequency waves to identify objects. Fundamentally, RFID is one example of a family of automatic identification technologies that also includes ubiquitous barcodes and magnetic strips. Since the mid-1970s, the retail supply chain (among many other areas) has used barcodes as the primary form of automatic identification. RFIDs can store a much larger amount of data than barcodes. Also, they can be accessed from a longer distance wirelessly. These potential advantages of RFID have prompted many companies (led by large retailers such as Walmart and Target) to aggressively pursue it as a way to improve their supply chains and thus reduce costs and increase sales. For details, see Chapter 8 in Sharda et al. (2018).

How does an RFID work? In its simplest form, an RFID system consists of a tag (attached to the product to be identified), an interrogator (i.e., RFID reader), one or several antennae attached to the reader, and a computer program (to control the reader and capture the data). At present, the retail supply chain has primarily been interested in using passive RFID tags. Passive tags receive energy from the electromagnetic field created by the interrogator (e.g., a reader) and backscatter information only when it is requested. The passive tag remains energized only while it is within the interrogator’s magnetic field.

In contrast, active tags have a battery to energize themselves. Because active tags have their own power source, they do not need a reader to energize them; instead, they can initiate the data transmission process on their own. As compared to passive tags, active tags have a longer read range, better accuracy, more complex rewritable information storage, and richer processing capabilities. On the negative side, their batteries cause active tags to have a limited life span, be larger in size than passive tags, and be more expensive. Currently, most retail applications are designed and operated with passive tags, each of which costs only a few cents. Active tags are most frequently found in defense and military systems, yet they also appear in technologies such as EZ Pass whose tags (called transponders) are linked to a prepaid account that, for example, enables drivers to pay tolls later, by driving past a reader rather than stopping to pay at a tollbooth.

NOTE: There are also semipassive tags with limited active tag capabilities.

The most commonly used data representation for RFID technology is the Electronic Product Code (EPC), which is viewed by many in the industry as the next generation of the Universal Product Code (UPC), most often represented by a barcode. Like the UPC, the EPC consists of a series of numbers that identifies product types and manufacturers across the supply chain. The EPC also includes an extra set of digits to uniquely identify items.

Use of RFID and Smart Sensors in IoT

Basic RFID tags, either active or passive, are not sensors. The purpose of the tags is to identify objects and determine their location (e.g., for the purpose of counting objects). To make them useful for most IoT applications, the tags need to be upgraded (e.g., by adding on-board sensors). These RFIDs called RFID sensors have more capabilities than RFID tags, or basic sensors. For a detailed discussion about the role of RFID in the IoT, see Donaldson (2017).

RFID sensors are wireless sensors that communicate, via mash networks or conventional RFID readers, and they include identifiable ID. The RFID reader sends token information into gateways, such as AWS IoT service. This confirmation can be processed, resulting in some action.

Smart Sensors and IoT

There are several types of smart sensors with different levels of capabilities when integrated into IoT. A  smart sensor  is one that senses the environment and processes the input it collects by using its built-in computing capabilities (e.g., a microprocessing). The processing is preprogrammed. Results are passed on. Depending on the internal computing quality, smart sensors can be more automated and accurate than other sensors and can filter out unwanted noise and compensate for errors before sending the data.

Smart sensors are crucial and an integral element in the IoT. They can include special components, such as amplifiers, analog filters, and transducers, to support IoT. In addition, smart sensors for IoT can include special software for data conversion, digital processing, and communication capability to external devices.

According to a major study (Burkacky et al., 2018), sensors are getting smarter. Those on vehicles are examples. Vehicles can make the transition from being a hardware-driven machine to being a software-driven electronic device. Software can cost over 35 percent of the cost of vehicle production.

For further information, see Scannell (2017), Gemelli (2017), and Technavio (2017).

Section 13.5 Review Questions

1. Define sensor.

2. Describe the role of sensors in IoT.

3. What is RFID? What is a RFID sensor?

4. What role does the RFID perform in IoT?

5. Define smart sensor and describe its role in IoT.

13.6 Selected IoT Applications

We start with a well-known example: Imagine that your refrigerator can sense the amount of food in it and send you a text message when inventory is low (sensor-to-insight in Figure 13.3). One day refrigerators will also be able to place an order for items that need replenishment, pay for them, and arrange delivery (sensor-to-action). Let us look at some other, less futuristic enterprise applications.

A Large-scale IoT in Action

Existing contribution of IoT has centered on large organizations.

Example French National Railway System’s Use of IoT

SNCF, the French national railway system, uses IoT to provide quality, availability, and safety for its nearly 14 million14 million passengers. The companysncf.com improved its operations using IoT (Estopace, 2017a). To manage 15,00015,000 trains and 30,00030,000 kilometers of tracks is not simple, but IBM Watson, using IoT and analytics, helped to do just that. Thousands of sensors that are installed on the trains, tracks, and train stations gather data that Watson processes. In addition, all business process operations were digitized to fit into the system. Information concerning possible cyberattacks was also programmed into the system. All collected Big Data were prepared for decision support. IBM Watson’s platform is scaleable and can handle future expansions.

To understand the magnitude of this IoT network, consider that the mass transit lines in Paris alone required 2,000 sensors forwarding information from more than 7,000 data points each month. The systems enable engineers to remotely monitor 200 trains at a time for any mechanical and electrical operations and malfunctions while trains are moving. In addition, by using a predictive analytic model, the company can schedule preventive maintenance to minimize failures. Therefore, if you are one of the train travelers, you can relax and enjoy your trip.

Examples of Other Existing Applications

The following examples of the use of IoT applications are based on information from Koufopoulos (2015):

· HILTON HOTEL. Guests can check in directly to their rooms with their smartphones (no check-in lobby is needed, no keys are used). Other hotel chains follow suit.

· FORD. Users can connect to apps by voice. Autopaying for gas and preordering drinks at Starbucks directly from Ford’s cars are in development.

· TESLA. Tesla’s software autonomously schedules a valet to pick up a car and drive it to Tesla’s facility when a car needs repair or schedule service. Tesla trucks, managed by IoT, will be driverless one day.

· JOHNNIE WALKER. The whiskey company connected 100,000 of its bottles to the Internet for Brazil’s Father’s Day. Using smart labeling, buyers can create personalized videos to share with their fathers on social networks. Fathers also get promotions to buy more whiskey if they like it.

· APPLE. Apple enables users of iPhones, Apple Watches, and Home kits to streamline shopping with Apple Pay.

· STARBUCKS CLOVER NET IN THE CLOUD. This system connects coffee brewers to customers’ preferences. It also monitors employee’s performance, improves recipes, tracks consumption patterns, and so on.

A large number of consumer applications of IoT is reported by Jamthe (2016) and Miller (2015). For a list of IoT applications related to IBM Watson, see ibm.com/internet-of-things/.

Many companies are experimenting with IoT products for retailing (business to consumer, or B2C) and business to business (B2B) in areas such as operations, transportation, logistics, and factory warehousing. For the approaches of Apple and Amazon, see appadvice.com/post/apple-amazons-smart-home-race/736365/.

NOTE: For many case studies and examples of the IoT, see ptc.com/en/product-lifecycle-report/services-and-customer-success-collide-in-the-iot, divante.co/blog/internet-e-commerce, and Greengard (2016). IoT is also used for many applications inside enterprises (see McLellan, 2017a), and military purposes (see Bordo, 2016).

How IoT is Driving Marketing

According to Durrios (2017), IoT can drive marketing opportunities in the following four ways:

1. DISRUPTIVE DATA COLLECTION. IoT collects more data about customers from more data sources than other technologies do. This includes data from wearables, smart homes, and everything consumers do. In addition, IoT provides data about changes in consumer preferences and behavior.

2. REAL-TIME PERSONALIZATION. IoT can provide more accurate information about specific customers buying decisions, for example. IoT can identify customer expectations and direct customers to specific brands.

3. ENVIRONMENTAL ATTRIBUTION. IoT can monitor environments regarding ad delivery for specific places, customers, methods, and campaigns. IoT can facilitate research of business environment; factors such competition, pricing, weather conditions, and new government regulations are observed.

4. COMPLETE CONVERSATION PATH. IoT initiatives expand and enrich the digital channel of conversations between customers and vendors, especially those using wireless digital engagement. IoT also provides insight on consumer purchasing paths. In addition, marketers will receive improved customized market research data (e.g., by following the manner of customers’ engagement and how customers react to promotions).

Of all the consumer-related IoT initiatives, three types are most well-known: smart homes and appliances (Section 13.7), smart cities (Section 13.8), and autonomous vehicles (Section 13.9). For more on IoT and customers, see Miller (2018).

Section 13.6 Review Questions

1. Describe several enterprise applications.

2. Describe several marketing and sales applications.

3. Describe several customer service applications.

13.7 Smart Homes and Appliances

The concept of the smart home has been in the limelight for several years, even before the concept of the IoT took a front stage.

A  smart home  is a home with automated components that are interconnected (frequently wirelessly), such as appliances, security, lights, and entertainment, and are centrally controlled and able to communicate with each other. For a description, see techterms.com/definition/smart_home.

Smart homes are designed to provide their dwellers with comfort, security, low energy cost, and convenience. They can communicate via smartphones or the Internet. The control can be in real time or at any desired intervals. Most existing homes are not yet smart, but they can easily and inexpensively be equipped to for at least partial smartness. Several protocols enable connections; well-known ones are XIO, UPB, Z-Wave, and EnOcean. These products offer scalability, so more devices can be connected to the smart home over time.

For an overview, see techterms.com/definition/smart_home, smarthomeenergy.co.uk/what-smart-home, and Pitsker (2017).

In the United States followed by other countries, thousands of homes are already equipped with such systems.

Typical Components of Smart Homes

The following are typical components in smart homes:

· LIGHTING. Users can manage their home lighting from wherever they are.

· TV. This is the most popular component.

· ENERGY MANAGEMENT. Home heating and cooling systems can be fully automated and controlled via a smart thermostat (e.g., see Nestnest.com/works-with-nest about its product Nest Learning Thermostat).

· WATER CONTROL. WaterCop (watercop.com) is a system that reduces water damage by monitoring water leaks via a sensor. The system sends a signal to a valve, causing it to close.

· SMART SPEAKER AND CHATBOTS (SEE  CHAPTER 12 ). Most popular are Echo and Alexa, and Google Assistant.

· HOME ENTERTAINMENT. Audio and video equipment can be programmed to respond to a remote control device. For instance, a Wi-Fi–based remote control for a stereo system located in a family room can command the system to play on speakers installed anywhere else in the house. All home automation devices perform from one remote site and one button.

· ALARM CLOCK. This tells kids to go back to sleep or to wake up.

· VACUUM CLEANER. Examples are iRobot Roomba, and LG Roboking vacuum; see Chapter 2).

· CAMERA. This allows residents to see what is going on in their homes anytime from anywhere. Nest Cam Indoor is a popular product. Some smart cameras can even know how residents feel. See tomsguide.com/us/hubble-hugo-smart-home-camera,news-24240.html.

· REFRIGERATOR. An example of this is Instaview from LG, which is powered by Alexa.

· HOME SECURITY AND SAFETY. Such systems can be programmed to alert owners to security-related events on their property. As noted, some security can be supported by cameras for remote viewing of property in real time. Sensors can be used at home to detect intruders, keep an eye on working appliances, and perform several additional activities.

The major components of smart homes are illustrated in Figure 13.4.

Figure 13.4 The Components of a Smart Home.

Figure 13.4 Full Alternative Text

Note that only a few homes have all of these components. Most common are home security, entertainment, and energy management.

Example: iHealthHome

Security measures are common in assisted living facilities in senior communities and for seniors who live independently. For example, the iHealthHome Touch screen system collects data and communicates with caregivers using the company’s software. The system provides caregivers and physicians remote access to a person’s health data. Using this technology, the iHealthHome program also reminds seniors of daily appointments and when to take their medicine. The system also reminds people when to self-measure their blood pressure and how to stay in touch with their caregivers.

Smart Appliances

A  smart appliance  includes features that can remotely control the appliance operations, based on the user preferences. A smart appliance may utilize a Home Network or the Internet to communicate with other devices in the smart home.

McGrath (2016) provides an overview of smart appliances that includes all appliances from Haier (a large China-based manufacturer). Its goal is to make everything in a house communicate across other device makers. Examples are smart refrigerators, air conditioners, and washing machines. Haier offers a control board for all appliances regardless of their manufacturers. Apple is working on a single control for all smart appliances in a home.

Google’s Nest

A leading manufacturer of IoT smart home applications is Google’s Nest. The company is a producer of programmable self-learning, sensor-driven, Wi-Fi–enabled products. In the spring of 2018, the company had three major products:

· LEARNING THERMOSTAT. This device learns what temperature and humidity level that people like and controls the air conditioner/heating system accordingly. Google claims that its products provide an average energy savings of 13 percent, which could pay for the device in two years; see nest.com/thermostats/nest-learning-thermostat/overview/?alt=3.

· SMOKE DETECTOR AND ALARM. This device, which is controlled from a smartphone, tests itself automatically and lasts for about a decade. For details, see nest.com/smoke-co-alarm/overview/.

· NEST.COM. This Webcam-based system allows users to see what is going on in their homes from any location via smartphone or any desktop computer. The system turns itself on automatically when nobody is at home. It can monitor pets, babies, and so on. A photo recorder allows users to go back in time. For details, see nest.com/cameras/nest-cam-indoor/overview/. For how Nest can use a phone to find out when individuals leave home, see Kastrenakes (2016). For more on Nest, see en.wikipedia.org/wiki/Nest_Labs.

Examples of Available Kits for Smart Homes

Two popular smart-home starter kits are (Pitsker, 2017):

1. AMAZON ECHO. This includes Amazon Echo, Belkin Wemo Mini, Philips Hue white starter kit, Ecobee Lite, and Amazon Fire TV stick with Alexa voice remote. Total cost on October 2017 was $495.$495.

2. GOOGLE HOME. This includes Google Home, Smart Speaker, Belkin Wemo Mini, Philips Hue white starter kit, Nest learning thermostat, and Google Chromecast (for entertainment). Total cost on October 2017 was $520.$520.

Home Appliances in Consumer Electronic Show (CES) 2016–2018

The following smart appliances, some of which were exhibited at the CES show in Las Vegas in January 2016 (Morris 2016), 2017, and 2018, are:

· SAMSUNG SMART FRIDGE. Cameras check content; sensors check temperature and humidity.

· GOURMET ROBOTIC COOKER. It does interesting cooking.

· 10 IN 1 DEVICE FOR THE KITCHEN. This stirs food such as scrambled eggs and has 10 cooking styles (e.g., baking, sauce making).

· LG HUM-BOT TURBO+. This can focus on an area in the home that needs special attention. A camera monitors the home remotely while the owner is away (similar to Google’s Nest).

· HAIER R3D2 REFRIGERATOR. According to Morris (2016), this refrigeration is not the most practical one, but it has much of entertainment value. It looks like R3D2 in Star Wars. It can serve you a drink as well as provide lights and sounds.

· INSTAVIEW REFRIGERATOR FROM LG. Powered by Alexa (enabled by voice), this includes a 29-inch LCD touch screen display. It provides functions such as determining the expiration dates of food and notifying the user. For details, see Diaz (2017).

· WHIRLPOOL’S SMART TOP LOAD WASHER. This fully automated machine has smart controls. It saves energy and even encourages philanthropy by sending a small amount of money to “Habitat for Humanity” each time washer is loaded.

· LG LDT8786ST DISHWASHER. This machine has camera whose sensors keep track of what has already been cleaned in order to save water. In addition, it provides flexibility in operations.

The following are smart home trends:

· TVs that can be used as a smart Hub for home appliances is coming from Samsung.

· Dolby Atmos products include speakers, receivers, and other entertainment items.

· DIY home smart security cameras make sure there is an intruder, not just the cat, before alerting the police.

· Water controls for faucets, sprinklers, and flood detectors are available. In addition, a robot can teach users how to save water indoors (hydrao.com/us/en/).

For more about home automation, see smarthome.com/sh-learning-center-what-can-i-control.html. Various apps used for home control can be found at smarthome.com/android_apps.html.

Smart components for the home are available at home improvement stores (e.g., Lowes) and can be purchased directly from manufacturers (e.g., Nest).

To facilitate the creation of smart components for the home, Amazon and Intel Corp. partnered in 2017 to provide developers with platforms to advance the smart home ecosystem. For details, see pcmag.com/news/350055/amazon-intel-partner-to-advance-smart-home-tech/.

For smart appliances at CES 2018, watch the video at youtube.com/watch?v=NX-9LivJh0/.

A Smart Home Is Where the Bot Is

The virtual personal assistant that we introduced in Chapter 12 enables people to converse by voice with chatbots such as Alexa/Echo and Google Assistant. Such assistants can be used to manage appliances in smart homes.

In a comprehensive smart home, devices not only meet household needs but also are able to anticipate them. It is predicted that in the near future, an AI-based smart home will feature an intelligent and coordinated ecosystem of bots that will manage and perform household tasks and may even be emotionally connected with people. For a prediction of the future bots, see Coumau et al. (2017). Amazon and Intel joined forces to develop such smart home ecosystems that include NLP capabilities.

Smart homes will also have smart robots that can serve people snacks, help take care of people who are handicapped, and even teach children different skills.

Barriers to Smart Home Adoption

The potential of smart homes is attractive, but it will take some time before there will be many of them. The following are some limiting barriers, per Vankatakrishnan (2017).

· COMPATIBILITY. There are too many products and vendors to choose from, making potential buyers confused. Many of these products do not “speak” to each other, so more industry standards are needed. In addition, it is difficult to match the products with consumers’ needs.

· COMMUNICATION. Different consumers have different ideas on what the smart home should be. Therefore, the capabilities and benefits of a smart home need to be clearly communicated to users.

· CONCENTRATION. Brands need to concentrate on population segments that are most interested in smart homes (e.g., Gen Y).

In addition are the issues of cost justification, invasion of privacy, security, and ease of use. For the future of smart homes, including the role of Amazon and Walmart, and how the smart home will shop for itself, see Weinreich (2018).

Smart homes, appliances, and buildings can be featured in smart cities, the subject of our next section.

Section 13.7 Review Questions

1. Describe a smart home.

2. What are the benefits of a smart home?

3. List the major smart appliances.

4. Describe how Nest works.

5. Describe the role of bots in smart homes.

13.8 Smart Cities and Factories

The idea of smart cities took off around 2007 when IBM launched its Smart Planet project and Cisco began its Smart Cities and Communities program. The idea is that in  smart cities , digital technologies (mostly mobile based) facilitate better public services for citizens, better utilization of resources, and less negative environmental impact. For resources, see ec.europa.eu/digital-agenda/en/about-smart-cities. Townsend (2013) provides a broad historical look and coverage of the technologies. In an overview of his book, he provides the following examples: “In Zaragoza, Spain, a ‘citizen card’ can get you on the free city-wide Wi-Fi network, unlock a bike share, check a book out of the library, and pay for your bus ride home. In New York, a guerrilla group of citizen-scientists installed sensors in local sewers to alert you when storm water runoff overwhelms the system, dumping waste into local waterways.” According to a prediction made by Editors (2015), smart cities would use 1.6 billion1.6 billion connected things in 2016. Finally, smart cities can have several smart entities such as universities and factories (see Lacey, 2016). For more on smart cities, see Schwartz (2015). In addition, watch the video “Cisco Bets Big on ‘Smart Cities’” at money.cnn.com/video/technology/2016/03/21/cisco-ceo-smart-cities.cnnmoney. Another video to watch is “Smart Cities of the Future” (3:56 min.)(3:56 min.) at youtube.com/watch?v=mQR8hxMP6SY. A more detailed video on San Diego (44:06 minutes)(44:06 minutes) is at youtube.com/watch?v=LAjznAJe5uQ.

Cities cannot become smart overnight, as illustrated in Application Case 13.3, which presents the case of Amsterdam and its evolution into a smart city.

Application Case 13.3 Amsterdam on the Road to Become a Smart City

In over seven years, the city of Amsterdam (The Netherlands) was transformed into a smart city using information technologies. This case describes the steps the city took from 2009 to 2016 to become a smart city, as reported by MIT Sloan School of Management. The city initiative included projects in the following categories: mobility, quality of living, transportation, security, health, and economy as well as infrastructure, big and open source data, and experimental living labs.

The major findings of the MIT team regarding Amsterdam’s transformation were:

· PRIVATE-SECTOR DATA ARE CRITICAL FOR CHANGING POLICY. The major categories of the project involved nongovernmental entities (e.g., using a GPS provider to manage traffic). For example, the private sector was involved in a project to change traffic situations (reduction of 25 percent in the number of cars and an increase of 100 percent in the number of scooters, in five years).

· IT IS NECESSARY TO HAVE CHIEF TECHNOLOGY OFFICERS IN SMART CITIES. Smart cities require the collection of large amounts of data using several tools and algorithms. Issues such as cost and security are critical.

· EXPECTATIONS OF THE CONTRIBUTION OF THE IOT, BIG DATA, AND AI, NEED TO BE MANAGED. Citizens expect rapid changes and improvement in areas ranging from parking to traffic. Data collection is slow, and changes are difficult to implement.

· SMART CITY INITIATIVES MUST START WITH DATA INVENTORY. The problem in Amsterdam was that data were stored in 12,000 databases across 32 departments. These were organized differently on different hardware, so data inventory was needed. This initial activity was boring and tedious and had no immediate visible payoff.

· PILOT PROJECTS ARE AN EXCELLENT STRATEGY. PILOT PROJECTS PROVIDE LESSONS FOR FUTURE PROJECTS. The city had over 80 pilot projects, for example, collecting different types of trash and placing them in different colored bags. Successful projects are scaled up in size.

· CITIZEN INPUT IS A CRITICAL SUCCESS FACTOR. There are several ways to encourage citizens to provide input. Involvement of universities and research institutions is also critical. In addition, social media networks can be used to facilitate citizens’ engagement.

The smart city initiative may be only in its beginning, but it is already improving the quality of life of residents and increasing the economic growth of the city. A critical success factor of the initiative was the willingness of the city officials to share their data with technology companies.

IoT was a major component in the projects. First, it enabled the flow of data from sensors and databases for analytic processing. Second, IoT enables autonomous vehicles of all kinds, which contribute to the reduction of pollution, vehicle accidents, and traffic jams. Finally, IoT provides real-time data that help decision makers develop and improve policies. In April 2016, the city won Europe’s “Capital of Innovation” award (a prize of 950,000 euros950,000 euros).

Sources: Compiled from Brokaw (2016), Fitzgerald (2016), amsterdamsmartcity.com, and facebook.com/amsterdamsmartcity.

Questions for Case 13.3

1. Watch the video at youtube.com/watch?v=FinLi65Xtik/ and comment on the technologies used.

2. Get a copy of the MIT case study at sloanreview.mit.edu/case-study/data-driven-city-management/. List the steps in the process and the applications that were likely used in IoT.

3. Identify the smart components used in this project.

In many countries, governments and others (e.g., Google) are developing smart city applications. For example, India has begun to develop 100 smart cities (see enterpriseinnovation.net/article/india-eyes-development-100-smart-cities-1301232910).

Smart Buildings: From Automated to Cognitive Buildings

IBM’s Cognitive Buildings

In a white paper (IBM, 2016), IBM discussed the use of IoT to make cognitive buildings, which are able to learn the behavior of a building’s system in order to optimize it. The cognitive building does so by autonomously integrating the IoT devices with the IoT operation. Such integration enables the creation of new business processes and increases the productivity of existing systems. Based on the concept of cognitive computing (Chapter 6), IBM describes the maturity of the technology as a continuation of the phase that started with automated buildings (1980 to 2000), the creation of smart building (2000 to 2015), and finally, cognitive building (beginning in 2015). The process is illustrated in Figure 13.5. The figure also shows the increased capabilities of buildings over time.

Figure 13.5 IBM’s Cognitive Building Maturity Framework.

Source:  IBM. “Embracing the Internet of Things in the new era of cognitive buildings.” IBM Global Business Services, White Paper, 2016. Courtesy of International Business Machines Corporation, © International Business Machines Corporation.Used with permission.

Figure 13.5 Full Alternative Text

The highlights of a cognitive building are:

· By applying advance analytics, buildings can provide insights in near real time.

· It learns and reasons from data and interacts with humans. The system can detect and diagnose abnormal situations and propose remedies.

· It has the ability to change building temperature subject to humans’ preferences.

· It is aware of its status and that of its users.

· It is aware of its energy status and adjusts it to be comfortable to dwellers.

· Its users can interact with the building via text messages and voice chatting.

· Robots and drones are starting to operate inside and outside the building without human intervention.

A major collaborator of IBM is Siemens (from Germany). The companies concentrate on global issues related to the use of IoT to enhance building performance.

Smart Components in Smart Cities and Smart Factories

The major objective of smart cities is to automate as many as possible public services such as transportation, utilities, social services, security, medical care, education, and economy. So, in the smart city overall project one may find several subprojects, some of which are independent of the master project.

Example

Hong Kong has a project called a smart mobility for the improvement of road safety. A consortium of private and public organizations has introduced Intelligent Transport Services, including a warning mechanism for collision, and control assistance for finding parking. The system also manages speed and lane violations and traffic congestion. All of these increase safety and efficiency. For details, see Estopace (2017b).

Transportation is a major area in which analytics and AI can make cities smarter. Other areas include economic development, crime fighting, and healthcare. For details, see SAS (2017).

Other examples of smart city components can be found in a smart university, smart medical centers, smart power grid, and in airports, factories, ports, sport arenas, and smart factories. Each of these components can be treated as an independent IoT project, and/or as a part of the smart city overall project.

Smart (Digital) Factories

Automation of manufacturing has been with us for generations. Robots are making thousands of products from cars to cellphones. Tens of thousands of robots can be found in Amazon’s distribution centers. Therefore, it is not surprising that factories are getting smarter with AI technologies and IoT applications. As such they may be considered a component of smart cities and may be interrelated with other components, such as clean air and transportation.

A  smart factory , according to Deloitte University Press, is “a flexible system that can self-optimize performance across a broader network, self-adapt to and learn from new conditions in real or near real time, and autonomously run entire production processes.” For details, see the free Deloitte e-book at DUP_The-smart-factory.pdf. For a primer, see https://www2.deloitte.com/insights/us/en/focus/internet-of-things/technical-primer.html.

Tomás (2016) provides a vision of what industrial production will look like in the future. It will be essentially fully digitized and connected, fast, and flexible. The major idea is that there will be a command center in a factory equipped with AI technologies. The AI, combined with IoT sensors and information flow, will enable optimal organization and sequencing of business processes. The entire production chain, from raw material suppliers, logistics, and manufacturing to sales, will be connected to IoT systems for planning, coordination, and control. Planning will be based on analytic predictions of demand.

Production processes will be automated as much as possible and wirelessly controlled. Logistics will be provided on demand quickly, and quality control will be automated. IoT combined with sensors will be used for both predictive and preventive maintenance. Some of these elements exist in advanced factories, and more factories will be smarter in the future.

For more on smart factories, see Libelium (2015) and Pujari (2017). For the smart factory of the future, read belden.com/blog/industrial-ethernet/topic/smart-factory-of-the-future/page/0.

The use of IoT in the factory is illustrated in the video “Smart Factory Towards a Factory of Things” at youtube.com/watch?v=EUnnKAFcpuE (9:10 min.).(9:10 min.).

Smart factories will have different business processes, new technology solutions, different people-machine interactions, and a modified culture. For the transformation process to a smart factory, see Bhapkar and Dias (2017). The accounting firm Deloitte (dupress.deloitte.com/smart-factory) provides a diagram that illustrates “the major characteristics of a smart factory” (Figure 13.6).

Source: Burke, Hartigan, Laaper, Martin, Mussomeli, Sniderman, “The smart factory: Responsive, adaptive, connected manufacturing,” Deloitte Insights (2017), https://www.deloitte.com/insights/us/en/focus/industry-4-0/smart-factory-connected-manufacturing.html. Used with permission.

Figure 13.6 Full Alternative Text

Example: Smart Bike Production in a Smart Factory

The world demand for smart bikes is increasing rapidly, especially in smart cities. Mobike is the world’s first and largest bike-sharing company. To meet the demand, the company is working with Foxconn Technology Group to make the bike production smarter. The smart manufacturing involves the creation of a global supply chain from raw materials to production to sales. Foxconn is known for its high-technology expertise in providing efficient manufacturing processes in a cost-efficiency production. It optimizes Internet-driven smart manufacturing. The production output is expected to double in the near future. For details, see Hamblen (2016) and enterpriseinnovation.net/article/foxconn-drives-mobike-smart-bike-production-1513651539.

Examples of Smart City Initiatives

Smart city initiatives are diversified, as explained earlier. For examples, see Application Case 13.4.

Application Case 13.4 How IBM Is Making Cities Smarter Worldwide

IBM has been supporting smart city initiatives for several years. The following examples are compiled from Taft’s slide show (eweek.com/cloud/how-ibm-is-making-cities-smarter-worldwide).

· MINNEAPOLIS (UNITED STATES). The initiative supports more effective decisions for the city’s resource allocation. In addition, it aligns the operations of multiple departments working on the same project. IBM is providing AI-based pattern recognition algorithms for problem solving and performance improvement.

· MONTPELLIER (FRANCE). IBM’s software is helping the city in its initiatives of water management, mobility (transportation), and risk management (decision making). The rapidly growing city must meet the increasing demand for services. To do this efficiently, IBM provides data analysis and interpretation of activities, research institutions, and other partners in the region.

· STOCKHOLM (SWEDEN). To reduce traffic problems, IBM technologies are optimally matching demands and supplies. The initiative uses sensors and IoT to alleviate the congestion problem.

· DUBUQUE (UNITED STATES). Several initiatives were conducted for efficient use of resources (e.g., utilities) and management of transportation problems.

· CAMBRIDGE (CANADA). The city is using IBM’s “Intelligent Infrastructure Planning” for conducting business analytics and decision support technologies. Using AI-based algorithms, the city can make better decisions (e.g., repair or replace assets). In addition, IBM smart technologies help to improve project coordination.

· LYON (FRANCE). Transportation management is a major project in any big city and a target for most smart city initiatives. Smart technologies provide transportation staff with effective real-time decision support tools. This helped reduce traffic congestion. Using predictive analytics, future problems can be forecasted, so, if they occur, they can be solved quickly.

· RIO DE JANEIRO (BRAZIL). To manage and coordinate the operations of 30 city departments is a complex undertaking. IBM technologies support a central command center for the city that plans operations and handles emergencies in all areas.

· MADRID (SPAIN). To manage all its emergency situations (fire, police transportation, hospitals), the city created a central response center. Data are collected by sensors, GPS, surveillance cameras, and so on. The center was created after Madrid’s 2004 terrorist attack and is managed with the support of IBM smart technologies.

· ROCHESTER (UNITED STATES). The city police department is using IoT and predictive analysis to forecast when and where crimes is likely to be committed. This AI-based system has proven to be accurate in several other cities.

These examples illustrate the utilization of IBM’s Smarter Cities framework in several areas by smart city initiatives. Note that IBM Watson is using IoT for many of its own projects.

Questions for Case 13.4

1. List the various services that are improved by IoT in a smart city.

2. How do the technologies support decision making?

3. Comment on the global nature of the examples.

A major area of improvement in a smart city is transportation.

Improving Transportation in the Smart City

A major problem in many cities is the increased number of vehicles and the inability to accommodate all of them effectively. Building more roads could add more pollution and lead to traffic jams. Public transportation can help alleviate the problem but may take years to complete. Quick solutions are needed. In the opening case to Chapter 2, we introduced Inrix. The Inrix company uses AI and other tools to solve transportation problems. It collects data from stationary sensors along roads and from other sources. In some smart cities, innovators have already placed air quality sensors on bicycles and cars. Sensors also are taking data from cars on the roads to help generate data that can analyzed and results are transmitted to drivers. An example of another innovative project is provided in the following examples.

Example 1

Valerann, an Israeli start-up, developed smart road studs to replace the reflective studs of today’s technology. Smart studs can transmit information of what they sense about what is occurring on the roads. Eventually, the studs will be incorporated with autonomous vehicles. The smart studs cost more than reflective studs but have a longer life. For details, see Solomon (2017).

Example 2

Smart Mobility Consortium (Hong Kong) works on mobility in the smart city of Hong Kong. More than 10 million10 million people there use the public and private transportation systems every day. This transportation project includes several smart subsystems for parking, collision warning, and alerts for speeders and lane changing violators. For details, see Estopace (2017b).

Combining Analytics and IoT in Smart City Initiatives

Like in many IoT initiatives, it is necessary to combine analytics and IoT. A notable example is IBM Watson. Another one is the SAS platform.

Example: The SAS Analytics Model for Smart Cities

The amount of data collected by IoT networks in cities can be enormous. Data are collected from many sensors, computer files, people, databases, and so on. To make sense of these data, it is necessary to use analytics, including AI algorithms. SAS is using a seven-step process divided into three major phases: Sense, Understand, and Act. The following are definitions of these (condensed from SAS, 2017).

· SENSE. Using sensors, sense anything that matters. SAS analyzes the collected data. The data go through intelligent filters for cleanliness so that only relevant data go to the next phase. IoT collects and transfers the data from the sensors.

· UNDERSTAND THE SIGNALS IN THE DATA. Using data mining algorithms, the entire relevant ecosystem is analyzed for pattern recognition. The process can be complex as the data collected by IoT sensors are combined with data from other sources.

· ACT. Decisions can be made quickly as all relevant data are in place. SAS decision management tools can support the process. Decisions range from alerts to automated actions.

The SAS process is illustrated in Figure 13.7. For more on analytics and IoT combination, see SAS Analytics for IoT at https://www.sas.com/en_us/insights/big-data/internet-of-things.html. For additional information, see Henderson (2017).

Figure 13.7 SAS Supports the Full IoT Analytics Life Cycle for Smart Cities (SAS).

Source: Courtesy of SAS Institute Inc. Used with permisison.

Figure 13.7 Full Alternative Text

Bill Gates’ Futuristic Smart City

In November 2017, Bill Gates purchased 60,000 acres of land west of Phoenix, Arizona, where he plans to construct a futuristic city from scratch. The city will be a model and place for research.

Technology Support for Smart Cities

A large number of vendors, research institutions, and governments are providing technology support for smart cities. Here are few examples.

Technology Support by Bosch Corp. and Others

Bosch Corp (of Germany), a major supplier of automotive parts, presented several innovations related to smart cities at CES 2018.

According to Editors (2018), revenues of global smart cities with IoT technology will exceed $60 billion$60 billion by 2026.

Finally, in smart cities, connected and self-driven vehicles will be everywhere (see Hamblen, 2016 and the next section).

Section 13.8 Review Questions

1. Describe smart city.

2. List some benefits of a smart city to the residents.

3. What is the role of IoT in smart city initiatives?

4. How are analytics combined with IoT? Why?

5. Describe smart and cognitive buildings.

6. What is a smart factory?

7. Describe technology support to smart cities.

13.9 Autonomous (Self-Driving) Vehicles

Autonomous vehicles , also known as driverless cars, robot-driven cars, self-driving cars, and autonomous cars, are already on the roads in several places. The first commercial autonomous car project was initiated by Google (named Google Chauffeur) and is becoming a reality, with several U.S. states preparing to allow them on the road. France, Singapore, China, and several other countries already have these cars and buses on their roads. These cars are electric, and they can create a revolution by reducing emissions, accidents, fatalities (an estimate of about 30,000 fatalities a year, worldwide), and traffic jams (e.g., see Tokuoka, 2016). Thus far, these cars are being tested in several cities worldwide and in some cities are already on the roads. Experts estimate that 10 million10 million such cars will be on the roads in the United States by 2020, and China is planning for 30 million30 million cars by 2021.

The Developments of Smart Vehicles

The initial efforts to commercialize a self-driving car were started by Google in the 1990s. These efforts can be seen today in Waymo’s story in Application Case 13.5.

Application Case 13.5 Waymo and Autonomous Vehicles

Waymo is a unit of Alphabet (previously called Google) that is fully dedicated to the Google self-driving car project. Almost 20 years ago, Google, with the help of Stanford University, started to work on this project. The idea received a boost in 2005 when DARPA awarded its Grand Challenge prize to the project. Then, the U.S. Department of Defense awarded it a $2$2 million prize. Google pioneered physical experiments in 2009 after conducting computer simulation for several years when it ran self-driving cars 2.5 billion2.5 billion virtual miles. The next step was to get legislation to allow autonomous vehicles on the roads. By 2018, 10 states had passed such laws. Some allow robot-driven cars only in certain areas. Self-driving cars with robot-only chauffeurs were tested in early 2018 by Waymo in the Phoenix, Arizona, area. First, corporate engineering will be in the driver’s seat; but, around November 2018, the cars were expected to be completely driverless. The company was ready to start running commercial minivans in five states in 2018. By the end of 2018, Waymo vans were expected to pick up regular passengers who volunteered to take the service (called Early Rider Program), although most travelers are still skeptic.

This works in the following way. Company technicians, acting like regular riders, order service via a mobile app. The AI mechanism figures out how the vehicle will get to the requested caller as well as how it will self-drive to the requested destination.

Waymo, the pioneer of autonomous vehicles, collaborated with Chrysler (using Chrysler Pacifica minivans). The computing power is provided by Intel (with its Mobileye division). The high cost of the cars will limit their use initially to commercial uses. However, Waymo already has agreed to manage Avis’s fleet of self-driving minivans. Also, realizing the power of ride-sharing services, Waymo is working with Lyft on new autonomous vehicles. Finally, Waymo is partnering with AutoNation to provide maintenance and road services for Waymo cars.

NOTE: On the legal dispute involving Uber, see the opening case of Chapter 14.

Sources: Compiled from Hawkins (2017), Ohnsman (2017), and Khoury (2018).

Questions for Case 13.5

1. Why did Waymo first use simulation?

2. Why was legislation needed?

3. What is the Early Rider Program?

4. Why will it take years before regular car owners will be able to enjoy a ride in the back seat of their self-driving cars?

5. Why are Lyft, Uber, and Avis interested in self-driving cars?

An example of how Nvidia works with Toyota’s initiative is presented in Technology Insights 13.2.

Technology Insights 13.2 Toyota and Nvidia Corp. Plan to Bring Autonomous Driving to the Masses

It is not surprising that Toyota is interested in smart cars. As a matter of fact, the company’s cars are expected to be on the market in 2020. Toyota plans to produce several types of autonomous vehicles. One type will be for elderly and disabled people. Another type will have the ability to drive completely autonomously or be an assistant (with a mechanism called “guardian angel”) to drivers. For example, it will have the ability to take full control when the driver falls asleep, or when it senses that an accident is coming. A tired driver will be able to use Alexa (or a similar device) to tell the guardian angel to take over.

Autonomous vehicles need a smart control system, and this is where Nvidia enters the picture. Autonomous cars need to process a vast amount of data collected by sensors and cameras in real time. Nvidia pioneered a special AI-based supercomputer (called Drive PX2PX2) for this purpose. The computer includes a special processor (called Xavier) that can power the autonomous driving gear of the cars. The partnership with Toyota enables Nvidia to leverage the power of its processor to apply AI to the autonomous cars.

Nvidia’s supercomputer has an AI algorithm-based special operating system that includes a cloud-based 3D map with high definition. With these capabilities, the car’s “brain” can comprehend its driving surroundings. Since a car can also exactly identify its own location, it will know about any potential hazard (e.g., road work or a vehicle coming toward it). The operating system is being constantly updated, so it makes the car smarter (AI learning capability).

The Xavier system provides the car’s “brain” on a special chip (called Volta), which can deliver 30 trillion30 trillion deep learning operations per second. Thus, it can process complex AI algorithms involving machine learning. Nvidia is expected to use Volta to open a new, powerful era in AI computing.

Source: Compiled from Korosec (2017) and blogs.nvidia.com/blog/2016/09/28/Xavier/.

Questions for Discussion

1. What does a car need to have in order to be autonomous?

2. What is the contribution of Nvidia to self-driving cars?

3. What is the role of Xavier?

4. Why does the process use a supercomputer?

Despite the required complex technology, several car manufacturers are ready to sell or operate such cars soon (e.g., BMW, Mercedes, Ford, GM, Tesla, and of course—Google).

Developments related to driverless vehicles follow:

· Uber and other ride-sharing companies plan for self-driving cars.

· Mail is delivered to homes by self-driving cars; see uspsoig.gov/blog/no-driver-needed.

· Driverless buses are being tested in France and Finland. Watch money.cnn.com/video/technology/2016/08/18/self-driving-buses-hit-the-road-in-helsinki.cnnmoney about self-driving buses in Helsinki.

· Self-driving taxis already operate in Singapore.

The Self Drive Act is the first national law in the United States pertaining to self-driving cars. It aims to regulate the safety of the passengers in autonomous vehicles. It opens the door for the production of 100,000 cars per year by 2021.

Flying Cars

While autonomous vehicles on the road may have considerable difficulties, there is research on flying cars. As a matter of fact, drones that can carry people already exist. As long as there is not much traffic in the air, there will be no traffic problem. However, the navigation of a large number of flying cars may be a problem. Airbus created a flying taxi demo in 2016 and Uber developed the concept and summarized it in a 98-page report released in October 2016. Toyota is also working on making a flying car. In January 2018, at the Las Vegas CES, Intel showed an autonomous passenger drone named Volocopter. This machine can be developed as an air taxi one day. For flying taxis in New Zealand, see Sorkin (2018).

Implementation Issues in Autonomous Vehicles

Autonomous vehicles such as cars, trucks, and buses are already on the roads in several cities worldwide. However, before we will see millions of them on the roads, it will be necessary to deal with several implementation issues. The following are reasons why full commercialization is going to take time:

· The cost of real-time 3D map technologies needs to be reduced and their quality needs to be increased.

· AI software must be nimble and its capabilities increased. For example, AI needs to deal with many unexpected conditions, including that of the behavior of drivers of other cars.

· Bray (2016) posted an interesting question: “Are customers, automakers and insurers really ready for self-driving cars?” Customers seem to acknowledge that such cars are coming. But they resist boarding one. However, some daring people expect these cars to do a better job than humans in driving.

· The technology needs more research, which is very expensive. One reason is that the many sensors in the cars and on the road need to be improved and their cost need to be reduced.

· The IoT is connecting many objects for autonomous vehicles, including those in clouds. The IoT systems themselves need to be improved. For example, data transmission delays must be eliminated.

For more IT/AI generic implementation issues, see Chapter 14.

Section 13.9 Review Questions

1. What are self-driving vehicles? How are they related to the IoT?

2. What are the benefits of self-driving vehicles to drivers, society, and companies?

3. Why are Uber and similar companies interested in self-driving vehicles?

4. What AI technologies are needed to support autonomous vehicles?

5. What are flying cars?

6. List some implementation issues of autonomous vehicles.

13.10 Implementing IoT and Managerial Considerations

In this chapter, we presented a number of successful IoT-based applications. The results so far are more than encouraging, especially in areas such as monitoring equipment performance to improve its operation and maintenance (e.g., CNH in the opening vignette and the IBM Watson case of elevators in Chapter 1). However, this is only the tip of the iceberg. As we indicated earlier, the IoT can change everything. In this section, we present some of the major issues that are related to successful IoT implementation. Although there is considerable excitement about the growth and the potential of the IoT, there are that managers should be aware of.

Major Implementation Issues

McKinsey’s Global Institute (Bughin et al., 2015) has put together a comprehensive Executive’s Guide to the Internet of Things. This guide identifies the following issues:

· ORGANIZATIONAL ALIGNMENT. Although it is true of several other technology initiatives, with IoT, the opportunities for operational improvements and creating new business opportunities means that IT and operational personnel have to work as one team rather than separate functions. As noted by the guide’s authors, “IoT will challenge other notions of organizational responsibilities. Chief financial, marketing, and operating officers, as well as leaders of business units, will have to be receptive to linking up their systems.”

· INTEROPERABILITY CHALLENGES. Interoperability is a huge detriment thus far in the growth of IoT applications. Few IoT devices connect seamlessly with each another. Second, there are many technological issues regarding connectivity. Many remote areas do not yet have proper Wi-Fi connection. Issues related to Big Data processing are also responsible for slow progress in IoT adoption. Companies are trying to reduce data at the sensor level so that only a minimal amount goes into clouds. Current infrastructure hardly supports the huge amount of data collected by IoT. A related problem is retrofitting sensors on devices to be able to gather and transmit data for analysis. In addition, it will take time for consumers to replace their analog objects with new IoT digital smart products. As an example, it is easier for people to replace mobile phones than a car, kitchen appliances, and other things that can benefit from having a sensor and being connected to IoT.

· SECURITY. Security of data is an issue in general, but it is an even bigger one in the context of IoT. Each device that is connected to IoT becomes another entry point for malicious hackers to get into a large system or at the least operate or corrupt a specific device. There are stories of hackers being able to breach and control automated functions of a car or to control a garage door opener remotely. Such issues require that any large-scale adoption of IoT involve security considerations from the very beginning.

Given that the Internet is not well secured, applying IoT networks requires special security measures, especially in the wireless sections of the networks. Perkins (2016) summarizes the situation as follows: “IoT creates a pervasive digital presence connecting organizations and society as a whole. New actors include data scientists; external integrators; and exposed endpoints. Security decision makers must embrace fundamental principles of risk and resilience to drive change.” For a free e-book about IoT, see McLellan (2017b).

Additional issues follow.

· PRIVACY. To ensure privacy, one needs a good security system plus a privacy protection system and policy (see Chapter 14). Both may be difficult to construct in IoT networks due to the large size of the networks and the use of the less protected Internet. For advice from top security experts, see Hu (2016).

· CONNECTION OF THE SILOS OF DATA. There are millions of silos of data on the Internet and many of them need to be interconnected in specific IoT applications. This issue is known as the need for a “fabric” and connectivity. This can be a complex issue for applications that involve many different silos belonging to different organizations. Connectivity is needed in machine to machine, people to people, people to machines, and people to services and sensors. For a discussion, see Rainie and Anderson (2017) and machineshop.io/blog/the-fabric-of-the-internet-of-things. For how the connection is done at IBM Watson, see ibm.com/Internet-of-things/iot-solutions/.

· PREPARATION OF EXISTING IT ARCHITECTURES AND OPERATING MODELS FOR IOT CAN BE A COMPLEX ISSUE IN MANY ORGANIZATIONS. For a complete analysis and guide on this subject, see Deichmann et al. (2015). Integrating IoT into IT is critical for the data flow needed by the IoT and IoT-processed data to flow back to actions.

· MANAGEMENT. As in the introduction of any new technology, the support of top management is necessary. Bui (2016) recommends hiring a chief data officer in order to succeed in IoT due to the need to deal with silos of data described earlier. Using such a top manager can facilitate information sharing across all business functions, roles, and levels. Finally, it solves departmental struggles to own and control the IoT.

· CONNECTED CUSTOMERS. There is evidence of an increased use of IoT in marketing and customer relationships. In addition, the IoT drives increased customer engagement. According to Park (2017), a successful deployment of IoT for customers requires “connected customers.” The connection needs to be for data, decisions, outcomes, and staff related to any contacts relevant to the IoT and marketing. The Blue Hill research organization provides a free report on this issue (see Park). IoT enables a better connection with key clients and improves customer service. Of special considerations are hospitality, healthcare, and transportation organizations.

Finnaly, Chui et al. (2018) provided suggestions in a recent study on how to succeed in IoT implementation.

With so many implementation issues, an implementation strategy is necessary.

Strategy for Turning Industrial IoT into Competitive Advantage

IoT collects large amounts of data that can be used to improve external business activities (e.g., marketing) as well as internal operations. SAS (2017) proposed a strategy cycle that includes the following steps:

1. SPECIFY THE BUSINESS GOALS. They should be set with perceived benefits and costs so the initiatives can be justified. This step involves a high level of planning and examination of resources. Initial return on investment (ROI) analysis is advisable.

2. EXPRESS AN ANALYTIC STRATEGY. To support ROI and prepare a business case, it will be necessary to plan how Big Data will be analyzed. This involves the selection of an analytic platform, which is a critical success factor. An examination of emerging AI technologies, such as deep learning, may be conducted. An appropriate selection will ensure a powerful IoT solution.

3. EVALUATE THE NEEDS FOR EDGE ANALYTICS. Edge analytics is a technology that is needed for some, but not all, applications. It is designed to introduce real-time capabilities to the applications. It also filters data to enable automated decision making, frequently in real time because only relevant data results from the filtering.

4. SELECT APPROPRIATE ANALYTICS SOLUTIONS. There are numerous analytic solutions on the market offered by many vendors. In using one or several for IoT, it is necessary to consider several criteria such as fitness for IoT, ease of deployment, ability to minimize project risks, sophistication of the tools, and connection to existing IT systems (e.g., the quality of IoT gateways). Sometimes it is a good idea to look at a group of vendors that offer combined products (e.g., SAS and Intel). Finally, appropriate infrastructures, such as high-performance cloud servers and storage systems, need to be examined. These must work together as a scalable, effective, and efficient platform.

CONTINUES IMPROVEMENT CLOSES THE LOOP. Like in any strategy cycle, performance should be monitored, and improvements in various steps of the process need to be considered, especially since IoT is evolving and changing rapidly. The extent of goal achievement is an important criteria and upgrading the goals should be considered.

Weldon (2015) suggests the following steps for successful IoT implementation:

· Develop a business case to justify the IoT project including a cost-benefit analysis and a comparison with other projects.

· Develop a working prototype. Experiment with it. Learn and improve it.

· Install the IoT in one organizational unit; experiment with it. Learn lessons.

· Plan an organization-wide deployment if the pilot is a success. Give special attention to data processing and dissemination.

The Future of the IoT

With the passage of time, we see an increasing number of IoT applications, both external and internal to organizations and enterprises. Because all IoT networks are connected to the Internet, it will be possible to have some of the networks connected to each other, creating larger IoTs. This will create growth and expansion opportunities for many organizations.

AI Enhancement of IoT

There are several areas of potential development. One area where AI will enhance IoT is in its ecosystem. Many IoT applications are complex and could be improved with machine learning that can provide insights about data. In addition, AI can help in creating devices (“things”) that can self-diagnose problems and even repair them. For further discussion, see Martin (2017). Another future benefit of AI when combined with IoT is “shaping up to be a symbiotic pairing” (Hupfer, 2016). This pairing can create cognitive systems that are able to deal with and understand data that conventional analytics cannot handle. The AI and IoT combination can create an embodied cognition that injects AI capabilities into objects (such as robots and manufacturing machines) to enable the objects to understand their environments and then self-learn and improve their operation. For details, see Hupfer (2017). Finally, AI can help the integration of IoT with other IT systems.

A final word! By now you are probably interested to know about getting a job in IoT. Yes, there is a shortage of IoT experts, and annual salaries can range from $250,000 to $500,000.$250,000 to $500,000. For 2017 data, see Violino (2017).

Chapter Highlights

· The IoT is a revolutionary technology that can change everything.

· The IoT refers to an ecosystem in which a large number of objects (such as people, sensors, and computers) are interconnected via the Internet (frequently wirelessly). By the years 2020 to 2025, there could be as many as 50 billion50 billion connected objects. Subsystems of such connected things can be used for many purposes.

· Use of the IoT can improve existing business processes and create new business applications.

· Billions of things will be connected to the Internet, forming the IoT ecosystem.

· Things on the IoT will be able to communicate, and the structure will enable a central control to manipulate things and support decision making in IoT applications.

· The IoT enables many applications in industry, services, and governments.

· IoT applications are based on analysis of data collected by sensors or other devices that flow over the Internet for processing.

· Sensors can collect data from a large number of things (e.g., over 1 million elevators in the opening case of Chapter 1).

· Major efforts are needed to connect the IoT with other IT systems.

· IoT applications can support decisions made by equipment manufacturers and by the users of equipment. (See the opening vignette of this chapter.)

· IBM Watson is a major provider of IoT applications in many industries and services (e.g., medical research). It was projected to reach over 1 billion1 billion users by the end of 2018.

· Smart appliances and homes are enabled by IoT.

· Smart city projects worldwide are supported by IoT, increasing the quality of life for residents of the cities and supporting the decision making of city planners and technology providers.

· Self-driven cars may reduce accidents, pollution, traffic jams, and transportation costs. Self-driving cars are not fully implemented yet, but some were introduced in 2018.

· Smart homes and appliances are popular. For a small cost, owners can use several applications from home security to controlling appliances in their homes.

· The concept of smart cities is being developed globally with projects in countries such as India, Germany, and the United States and the city-state of Singapore. The objective of smart cities is to provide a better life for their residents. Major areas covered are transportation, healthcare, energy saving, education, and government services.

Questions for Discussion

1. Compare the IoT with regular Internet.

2. Discuss the potential impact of autonomous vehicles on our lives.

3. Why must a truly smart home have a bot?

4. Why is the IoT considered a disruptive technology?

5. Research Apple Home Pod. How does it interact with smart home devices?

6. Alexa is now connected to smart home devices such as thermostats and microwaves. Find examples of other appliances that are connected to Alexa and write a report.

7. Discuss the objective of smart cities to conserve the earth’s limited resources.

8. What are the major uses of IoT?

9. Accidents involving driverless cars slow down the implementation of the technology. Yet, the technology can save hundreds of thousands of lives. Is the slowdown (usually driven by politicians) justifiable? Discuss.

Exercises

1. Go to theinternetofthings.eu and find information about the IoT Council. Write a summary of it.

2. Go to https://www.ptc.com/en/resource-center or other sources, and select three IoT implemented cases. Write a summary of each.

3. AT&T is active in smart city projects. Investigate their activities (solutions). Write a summary.

4. It is said that the IoT will enable new customer service and B2B interactions. Explain how.

5. The IoT has a growing impact on business and e-commerce. Find evidence. Also read Jamthe (2016).

6. Find information about Sophia, a robot from Hanson Robotics. Summarize her capabilities.

7. Examine the Ecobee thermostat and its integration with Alexa. What are the benefits of the integration? Write a report.

8. Enter smartcitiescouncil.com. Write a summary of the major concept found there; list the major enablers and the type of available resources.

9. Find the status of Bill Gates’s futuristic smart city. What are some of its specific plans?

10. City Brain is the name of Alibaba’s platform for smart cities. One project has been adopted in China and Malaysia. Find information and write a report.

11. Find the status of delivering pizza by self-driving cars. Check Domino’s Pizza news.

12. India has many IoT applications, including projects for 100 smart cities. Read the 2016 status report atenterpriseinnovation.net/article/internet-things-next-big-wave-india-1270947471/ and find more recent information about it. Why do you think IoT is so widespread in India? Write a report.

13. Read the Blue Hill report (Park, 2017) and summarize all the issues related to IoT.

14. Find the status of smart cities as it is related to IoT and Cisco. Write a report.

15. Watch the video atyoutube.com/watch?v=ZJr0X3XBMmA (14:36 min.).(14:36 min.). Write a summary about the five smart devices.

16. Watch the video “Smart Manufacturing” (22 min.)(22 min.) at youtube.com/watch?v=SfVUkGoCA7s and summarize the lessons learned.

17. The competition for creating and using autonomous cars is intensifying globally. Find 12 companies that are competing in this field.

18. Enter McKinsey Global Institute mckinsey.com/mgi/overview and find recent studies on IoT. Prepare the summary.

19. AT&T is trying to connect autonomous vehicles to smart cities. Find information on the progress of this project. Identify the benefits and the difficulties.