In this paper, we develop effective solutions for enabling mobile sensing/data collection in community IoT deployments where sensing/communication coverage is intermittent and varying. Specifically, we address the optimized upload planning problem, i.e. determine optimal schedules for upload of gathered information to enable timely data collection in dynamic settings. We develop a two-phase approach and associated policies, where an initial upload plan is generated with prior knowledge of upload opportunities and data needs, and a subsequent runtime adaptation phase alters the plan based on dynamic network and data conditions. To validate our approach, we designed and built SCALECycle, a prototype mobile data collection platform and deployed it in real world community settings; measurements from testbeds in Rockville, MD and Irvine, CA are used to drive extensive simulations. Experimental results indicate that a judicious combination of policies in the two phases of upload planning (a balanced delay-opportunity-priority method with Lyapunov-inspired upload adaptation) can result in a 30-60% improvement in overall utility of collected data compared with opportunistic operation along with 30% reduction in collection delays /overheads.
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