What This Document Is
This document presents a detailed exploration of the BikeNet Mobile Sensing System, a project focused on cyclist experience mapping. It’s a deep dive into the design and implementation of a system leveraging mobile sensors and networking principles to gather and analyze data related to cycling conditions and rider experience. This material originates from an advanced computer networks course (EEL 6788) at the University of Central Florida, indicating a technical and research-oriented approach.
Why This Document Matters
This resource is ideal for students and professionals interested in the intersection of mobile computing, sensor networks, and data analysis within a real-world application. Individuals studying advanced networking concepts, particularly those relating to opportunistic networking, data aggregation, and distributed systems, will find this particularly valuable. It’s also relevant for those exploring the development of people-centric sensing applications and the challenges of operating in disconnected or intermittently connected environments. Understanding the principles behind BikeNet can inform the design of similar systems for other mobile applications.
Topics Covered
* Mobile Sensing System Architecture
* Opportunistic Networking Principles
* Data Collection and Analysis from Mobile Sensors
* Sensor Fusion and Inferred Measurements
* System Design for Disconnected Environments
* Spatio-Temporal Data Analysis
* Mobile Sensor Hardware and Software Integration
* Data Visualization and Presentation
* Performance Trend Analysis in Mobile Networks
What This Document Provides
* A comprehensive overview of the BikeNet system’s tiered architecture (Mobile Sensor, SAP, and Server tiers).
* Detailed descriptions of the various sensors utilized within the BikeNet system, including both performance/fitness and environmental/experience sensors.
* An examination of the data exchange mechanisms employed within the network, including tasking, uploading, and muling.
* Visual representations of the system’s components and data flow.
* Examples of sensor data and potential applications for analysis.
* References to related work in the field of people-centric sensing.
