Pattern Implementation in the Smart City Domain

There is a huge focus on enhancing the infrastructure of existing cities to make it smarter and more efficient. Adding sensing and actuation capabilities to parts of a city’s infrastructure, such as utilities (lower wastage and minimal disruptions), power plants (safer operations and predictive behavior), traffic systems (for controlling congestion), law enforcement (crime prevention), educational institutions (enhanced engagement for students and automation of regular tasks for teachers), and healthcare services (timely emergency response), and integrating the required communication technologies will make cities safer, more intelligent, environmentally sustainable, and energy-efficient, resulting in improvement in living conditions of city dwellers.

The chapter introduces a few use cases that are relevant to a smart city and how these can be implemented by leveraging the IoT patterns described in earlier chapters. Specifically...

A smart speaker for modernizing education

The capability of smart speakers to recognize voices and their ability to process information at the edge and integrate with central server services can be a potent combination for revolutionizing education and providing a level playing field for all students.

In addition to smart speakers and central server services, a mini solar plant is required to power smart speakers and the related hardware (such as routers) on the school premises, as the electricity supply is generally choppy in developing countries.

This use case can be further subdivided into the following two sub-use cases:

• Transforming education by providing benefits to students and teachers: Students gain by improving their verbal communication skills, resulting in enhanced confidence, and teachers stand to gain by automating mundane educational tasks:
  • Improvement in pronunciation for non-vernacular languages: An example of this is English in the Indian context. Input...

Monitoring the condition of perishable goods

In this use case, a fleet of trucks carries perishable goods from one location to another and the state of the goods being ferried is continuously monitored.

For tracking the state of goods being transported in near real time, a set of sensors (e.g., for temperature, moisture, gas, etc.) will be installed/placed near the goods and will send the current state to the central server along with the current location (the truck’s GPS location). The data received is analyzed at the central server and suitable action(s) are relayed back to the specific truck (or to the complete fleet in special scenarios) so that appropriate action can be taken.

Some amount of analysis (and action) needs to be performed locally, especially in scenarios where connectivity with the backend is erratic. For example, if the condition of goods has deteriorated beyond an acceptable limit, it would be prudent to discard the shipment rather than complete the...

Driver behavior monitoring

A significant number of road fatalities could be averted if a driver’s overt/covert behavior is monitored in real time. Overt abnormal behavior such as that related to drowsiness (or driving in an intoxicated state) can be detected by a camera with the ability to analyze video feeds locally at a DG installed in the vehicle. Trained ML models for analyzing video feeds are pushed periodically from a central server. These models gauge and report driver behavior, along with a confidence level. Analysis of behavioral patterns can be further augmented by obtaining additional data from the driver’s wearable device (such as pulse rate and last night’s sleep quality).

Processing needs to be done locally (at the edge) as video feeds can’t be sent to the central server as it would hog the communication channel’s bandwidth, as well as the response not being within expected time limits.

Along with the video feed, other crucial...

Automatic replenishment of consumables and raw materials

Consumer devices and appliances are getting smarter and more connected and we are not far off from the day when they will be able to detect and order the required consumables or raw materials by themselves. These appliances will detect whether they are running low on raw materials/consumables and then notify the user about the consumables that need to be replenished and leave the final purchasing decision to the owner. Over time, the system can learn someone’s behavior/preferences and even make the purchasing decision on their own. Appliances will have their own processing logic and will connect to a mobile device (using Bluetooth Low Energy (BLE) or a similar protocol) to send their statuses to a central server.

The key components of this use case are illustrated in the following figure:

Figure 5.4 – Realization of an automatic consumable ordering use case by leveraging IoT patterns

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Additional use cases

Some of the additional use cases that can be implemented in a smart city are shown in the following diagram:

Figure 5.5 – Possible smart city use cases

These applications can be developed to make life of the city dweller more meaningful and interesting.

Summary

The scope of the smart city is much wider than what is covered in this chapter and will touch the lives of city residents in multiple ways.

This chapter demonstrated how IoT patterns can be used to realize some of the use cases. Additionally, the chapter provided details related to implementation nuances (sensors, actuators, connectivity, etc.) and how these change from one use case to another. The list of use cases provided is by no means exhaustive and the reader is encouraged to use the knowledge gained in this chapter to devise solutions that would solve particular problems/issues that are prevalent in their city.

In the next chapter, we will continue this journey and examine some use cases that are relevant in the retail domain and how they can be implemented using architectural patterns.