Mobile sensing and data collection QIUXI ZHU

Mobile sensing and data collection – Background IoT systems depend heavily on network infrastructure, which is not uniformly and continuously accessible. Providing complete coverage by blanketing the entire region with sensors is costly, and difficult in certain regions and terrains. Some forms of sensed information may only be useful in rare events. Flexible approaches for instrumentation based on need is essential to enhancing the resilience of current large-scale IoT systems. Mobile sensing and data collection is a promising solution! Mobile sensing and data collection is a promising solution!

Mobile sensing and data collection – SCALECycle A SCALE multi-sensor box on a bike, with ◦GPS & USB battery pack ◦Sensors Conducted measurements on two real testbeds ◦UCI campus ◦Victory Court Senior Apartments in Montgomery County, MD Collected Wi-Fi RSSI/quality and air quality.

Upload planning problem – Formulation Data chunks have size, priority, and deadline. Upload opportunities have varying uploading rates. How do we utilize knowledge about IoT deployments and network infrastructure availability, to make data collection more efficient (to maximize data utility and reduce task execution time)? How do we adapt to dynamics in environment and networks during task execution?

The upload planning problem – Approach and results Two-phase approach Two-phase approach, combined with static planning (at beginning) using Balanced Deadline-Opportunity-Priority and dynamic adaptation (during task execution) using Lyapunov control. Select a data chunk a with earliest deadline (EDF) If a can be schedule to be on time: Schedule it at the fastest possible opportunity Else: While a is not scheduled: Select a previously scheduled low-priority chunk Mark it as sacrificed If total utility of sacrificed data is lower than a: If a can be scheduled to be on time: Put back all sacrificed data chunks Schedule a at the fastest possible opp. Else: Clear the marks and allow a to be late Static planning using Balanced DOP

Mobile sensing and data collection – Possible directions 1.Explore potentially different mobility models (e.g. drones). 2.Implement a mobile crowdsourcing system. 3.…