Who's Home
Who’s Home detects who is at home and notifies the account holder via push notifications when members arrive or leave. Using WiFi-based tracking ensures seamless and automated household management.
MY ROLE
I was responsible for creating logic flows, analyzing user feedback provided by the research team, facilitating ideation sessions with the Product Owner and Tech Lead, and ensuring accessibility compliance.
Agile Design with Fast User Feedback
Who’s Home is integrated into Vodafone HubApp, the leading flagship platform, just like other Vodafone applications. Users can view household members' statuses on the dashboard and check if the service has been activated.
The project was finalized within two months, including user interviews and testing to ensure an optimal user experience.
Accessibility Compliance for Upcoming EU Regulations 2025
Key Accessibility Improvements I Implemented:
Improved Touch Targets & Navigation
Ensuring that interactive elements are large enough and well-spaced helps users with motor impairments navigate the app easily.
Ensuring that interactive elements are large enough and well-spaced helps users with motor impairments navigate the app easily.
Voice & Screen Reader Support
I worked on optimizing the app for screen readers, ensuring that visually impaired users receive clear, structured feedback when interacting with different elements.
I worked on optimizing the app for screen readers, ensuring that visually impaired users receive clear, structured feedback when interacting with different elements.
Alternative Input Methods
Implementing voice commands and gesture-based interactions to assist users who have difficulty typing or using precise touch gestures.
Implementing voice commands and gesture-based interactions to assist users who have difficulty typing or using precise touch gestures.
Cognitive Load Reduction
Simplifying language, layout, and workflows to support users with cognitive disabilities, making the interface intuitive and easy to follow.
Simplifying language, layout, and workflows to support users with cognitive disabilities, making the interface intuitive and easy to follow.
By integrating these inclusive design principles, I ensured that Who’s Home is accessible not just for those with color vision deficiencies, but also for users with varied abilities, making the experience truly universal and inclusive.
Team Ideation: Problem Statement for Duplicate Device Detection and Scoring
In many network environments, the device list often contains duplicate entries representing the same physical device.
These duplicates can arise from MAC address randomization, multiple network interfaces, varying compatibility modes, and other factors.
This clutter complicates network management and accurate tracking of device activity.
Objective:
Develop a scoring solution that calculates the likelihood that two or more device entries in a Wi-Fi network represent the same physical device.
Develop a scoring solution that calculates the likelihood that two or more device entries in a Wi-Fi network represent the same physical device.
This scoring system should automate the detection of duplicates and provide a confidence rating to inform whether devices can be auto-merged or need manual review.
Key Attributes for Scoring:
◦ OUI (Manufacturer): The first 24 bits of the MAC address, indicating the device's manufacturer.
◦ Device Name: The label assigned to the device, which can suggest a match if identical or highly similar.
◦ Network Type (SSID): The network or Wi-Fi band the device connects to; similar SSIDs can indicate a duplicate.
◦ Connection Proximity (Time): The time difference between connection events; a short time gap suggests the same device.
◦ Bluetooth Address: If available, a matching Bluetooth MAC address can confirm duplicate entries.
◦ Device Type / OS: Information about the device’s operating system or type (e.g., iOS, Android) further supports matching.
◦ OUI (Manufacturer): The first 24 bits of the MAC address, indicating the device's manufacturer.
◦ Device Name: The label assigned to the device, which can suggest a match if identical or highly similar.
◦ Network Type (SSID): The network or Wi-Fi band the device connects to; similar SSIDs can indicate a duplicate.
◦ Connection Proximity (Time): The time difference between connection events; a short time gap suggests the same device.
◦ Bluetooth Address: If available, a matching Bluetooth MAC address can confirm duplicate entries.
◦ Device Type / OS: Information about the device’s operating system or type (e.g., iOS, Android) further supports matching.
Approach: Each attribute will be assigned a specific weight, contributing to an overall duplicate possibility score expressed as a percentage.
◦ Device Name (30%): A strong match in device naming can indicate the same device, especially when user-assigned names are used consistently.
◦ OUI (25%): The Organizationally Unique Identifier, representing the manufacturer, provides critical context for verifying device origin.
◦ Network Type (15%): Matching SSIDs and network types help confirm that devices are connecting through similar channels.
◦ Connection Proximity (15%): Devices that connect within a short time window (e.g., ≤ 5 minutes apart) are likely to be the same physical device.
◦ Bluetooth Address (10%): A matching Bluetooth MAC address can offer additional confirmation, as these addresses are often static.
◦ Operating System (5%): Consistency in OS information (device type/OS version) supports duplicate identification, though it is less discriminative.
◦ OUI (25%): The Organizationally Unique Identifier, representing the manufacturer, provides critical context for verifying device origin.
◦ Network Type (15%): Matching SSIDs and network types help confirm that devices are connecting through similar channels.
◦ Connection Proximity (15%): Devices that connect within a short time window (e.g., ≤ 5 minutes apart) are likely to be the same physical device.
◦ Bluetooth Address (10%): A matching Bluetooth MAC address can offer additional confirmation, as these addresses are often static.
◦ Operating System (5%): Consistency in OS information (device type/OS version) supports duplicate identification, though it is less discriminative.
Based on the cumulative score:
◦ 85% and above indicates a Very High Confidence Duplicate (auto-merge).
◦ Between 70% – 85% indicates a High Confidence Duplicate.
◦ 70% and below suggests a low confidence
◦ 85% and above indicates a Very High Confidence Duplicate (auto-merge).
◦ Between 70% – 85% indicates a High Confidence Duplicate.
◦ 70% and below suggests a low confidence
Expected Outcome:
The solution will enable network administrators to
◦ Automatically group and merge duplicate device entries,
◦ Reduce clutter in the device list,
◦ Enhance overall network management accuracy,
◦ Quickly identify devices that require manual confirmation for merging.
◦ Reduce clutter in the device list,
◦ Enhance overall network management accuracy,
◦ Quickly identify devices that require manual confirmation for merging.
This scoring system aims to simplify duplicate detection, streamline device management, and ensure that tracking reflects the actual state of the network.
