Data from individuals and connected technologies is used to inform society, businesses, industry and governments. Smartphones can be used to collect data and contributes to a person’s digital footprint. While this data may benefit the broader community it also raises privacy concerns about personal information. Problems and challenges faced by society can provide a useful context for examining existing data-driven digital solutions. Autonomous cars provide a useful context to examine the data required to enable this technology to work safely and become a reality on our roads.
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Data from individuals and connected technologies is used to inform society, businesses, industry and governments.
Data acquired from mobile phone usage can reveal much about a person such as their location, who they communicate with and how, and personal shopping habits including what they search for and buy online. Smartphone usage contributes to a person’s digital footprint and raises privacy issues.
Consumer data collected from mobile phone tracking is used to improve customer experience.
Companies can use technology to track consumer behaviour. A consumer’s actions reveal what they desire, how they shop and why they buy.
While this data may benefit consumers it also raises privacy concerns about personal information.
Problems and challenges faced by individuals, communities, industries, local businesses and governments can provide a useful context to examine existing digital solutions.
Defining a problem (‘problem identification’) is often the first step in the process of coming up with possible solutions.
In the case of an autonomous car, the vehicle can guide itself without human control. To enable the ‘driverless’ car to do this it must use various technologies for example sensors that build up a 3D map of the car’s surroundings. There are three main types of hardware in the driverless car model: sensors, processors, and actuators. The autonomous car collects, processes and transmits huge amounts of data. The amount and type of data raises concerns about how the data from driverless cars might be used.
Automation is already permeating into the agricultural industry. A large portion of farming related tasks are labor-intensive and often comprised of repetitive tasks. These tasks are ideal to be replaced by robotics and automation. Agricultural robots (AgBots) perform tasks such as planting and watering, harvesting and sorting the produce. Autonomous tractors are becoming more prevalent on ‘smart farms’. Technology on the tractors include cameras and machine vision systems, GPS for navigation. Connectivity enables remote monitoring and operation. The use of radar and LiDAR enables object detection and avoidance.