# Optional: Notify admin for review if adminReviewEnabled: sendToReviewQueue(sensorID, description)
## 4 Implementation Details
This design enables users to jump from one timestamped entry to another without losing contextual meaning, enhancing the experience for users with visual impairments who rely on quick and logical content segmentation. Figure 5: (**a**) Bullet trend list view in AccessiDashboard, listing recent sensor readings as bullet points. Each entry (with timestamp) shows values like Temperature, Humidity, AQI, Energy, and Device Status in a concise textual form with descriptive adjectives; (**b**) Data Grid View in AccessiDashboard, presenting a table of the latest sensor readings. Columns include Timestamp, Temperature (\({}^{\circ}\)C), Humidity (\(\%\)), Air Quality (AQI), Energy Consumption (kWh), Device Status, GPS Latitude, GPS Longitude. A note below the table mentions automated formatting and potential effects on data display. To address performance and scalability, especially in deployments involving multiple sensors or frequent updates, the system was designed to leverage modular components and microservices principles. The AI description engine functions independently and fetches raw sensor data via RESTful APIs. This decoupled design means that enhancements to AI processing or front-end display logic can occur without disrupting the rest of the system, supporting scalability and long-term extensibility. Caching strategies and database indexing were applied to ensure that, even as data volumes increase, the rendering of the last 10 entries remains performant and responsive across devices. We also append a note at the bottom (as seen in the screenshot) clarifying that these bullet points are generated from real data and that AI was used to improve readability, cautioning that, while accuracy is aimed for, the AI-generated descriptions may simplify or slightly interpret the data. Figure 5b presents the Data Grid View. Here, the interface is a table titled \"Sensor Data Grid (Latest 10 Readings)\". Each row is a timestamp (e.g., \"2025-04-26 09:25:36\") followed by the readings at that time for temperature, humidity, AQI, energy, status, and location coordinates. In constructing the Data Grid View, we followed web accessibility best practices by employing semantic HTML elements (<table>, <thead>, <body>, <th>, and <td>) and incorporating ARIA roles where necessary. Column headers are linked with their respective data cells using scope attributes and accessible labels to ensure coherence when the table is navigated with assistive tools. This design enables users to either explore the data row by row or use screen reader shortcuts to scan vertically through a particular metric, such as air quality trends over time. We took care to ensure this table is scrollable on smaller screens and is marked up accessibly (with <th> for each column like \"Temperature (\"C)\", etc.). For a screen reader, reading this table cell by cell is possible, or the user can use table navigation commands to move vertically down a column (to hear all temperature values, for instance). The inclusion of latitude and longitude in this example is for completeness; not all use cases would include location per reading, but we show it to demonstrate multi-dimensional data. A warning note below the table informs that the data is directly from sensors and that, while formatting was performed for accessibility, automated tools might affect ordering or emphasis (this is to hint that, for example, sorting might not be applied or that the display is static for now). Given the increased volume and density of information in this view, we optimized for both responsiveness and legibility. The table structure supports horizontal scrolling for smaller viewports, maintaining accessibility without requiring horizontal zoom or loss of readability. The Data Grid view implementation involved formatting data values consistently (e.g., to two decimal places for temperature) and possibly highlighting out-of-range values.