Brazenbury Smart Community: Evaluating Data for Smart Infrastructure

This case study analyzes the proposed smart community of Brazenbury, focusing on the trade-offs between personal data and the convenience of smart living. It explores arguments for and against trading personal data, considering the benefits of health monitoring and financial incentives against potential privacy breaches and discrimination. The case study also proposes a smart public transport system for Brazenbury, highlighting its potential advantages in balancing demand and supply, as well as the risks associated with user participation. The success of the system hinges on residents' willingness to actively engage with the technology, either through in-house devices or mobile apps. This document provides a comprehensive overview of the challenges and opportunities presented by smart city initiatives, with Desklib offering further resources for students to explore similar topics and access solved assignments.

a. Arguments for trading personal data for the convenience of living in a smart
community
In the current setting, trading personal data for the convenience of living in a smart community
will be of greater benefit to the user. The primary data to be shared will not only help users in
monitoring common chronic health conditions, but will also aid in early detection of potential
health issues such as post-natal depression, thus aiding in early preventative intervention.
Secondly, the financial benefits will be great; a 25% price reduction translates to a huge financial
reprieve. Considering the convenience of living in a smart community, and the financial benefits
in acquiring a smart home, then the trade-offs are balanced and it’s worth living in the smart city.
b. Arguments AGAINST trading personal data under these circumstances
One of the major drawbacks of trading personal data is the fact that user-privacy is not assured.
The nature of data collected makes it possible to personally identify users from the data. Medical
data is also shared; meaning confidentiality of one’s medical condition can easily be
compromised.
A second problem with trading personal data is that the same information can be used to
discriminate individuals in some cases. If the continuous monitoring and early health-conditions
detection data falls in the wrong hands, individuals can be discriminated particularly on health
insurance, based on their predicted level of risk.
Question 2
A potential Smart Infrastructure project that could be tested in Brazenbury
In any city a reliable public transport system is a critical requirement. We propose a smart public
transport system for Brazenbury; the proposed system employs an information driven and user
notification system. Convectional public transport busses and light trains are time scheduled.
However, the approach is constrained when the number of people travelling is higher than
expected.
To address the challenge, we propose a system when each house within the Brazenbury city will
be filled with a special knob, and an alternative mobile app. Residences will be encouraged to
press on the knob when leaving the house for bus stage, when they need to use public transport.
User can also send the notification using a mobile app. The system will also make use of sensors
and cameras at public transport stations to estimate the number of travelers at any given moment.
Based on the number of users who need public transport, the system can increase fleet to a given
route, re-allocate vehicles to routes or reduce number of vehicles.
The convectional time-scheduled bus and train service will be offered; the proposed system will
complement the convectional approach, by ensuring more vehicles and trains are availed when
demand is high. Additionally, the busses will use special mass transit routes that will be free of
traffic jams.

Potential Advantage of the system
The main advantage with the proposed data-driven mass transit system is the fact that it will
increase reliability of public transport systems and help in balancing demand and supply of the
transport services. Users and particularly the residents of the city will have a reliable system that
responds to their needs.
Potential risk
One of the biggest risks with the system is user-buy-in. A greater part of the data that informs on
the demand is dependent on user’s willingness to participate, by either pressing the in-house
nobble or tapping on a mobile app, to indicate that they intend to use public transport system at a
given time. As such, the success of the system relies on the willingness of the users to participate.