On the application of pipelining in aggregation convergecast scheduling

We consider the problem of scheduling wireless transmissions in a sensor network to perform aggregation convergecast. In contrast to studies that attempt to shorten the schedule for completing a single data collection cycle, we aim to increase the frequency at which updates are collected (higher throughput). To achieve higher throughput, we use a form of pipelined concurrent collection of multiple data snapshots through the network. To attain high performance pipelining, we “expand” the time in which precedence constraints need to be satisfied such that they span over multiple schedule cycles. Our approach involves the unconventional approach of constructing the schedule before finalizing the exact form of the precedence constraints, i.e., before determining the data aggregation tree, which in turn requires that the schedule construction phase guarantees that every node can reach the sink. We compare our results using pipelining against a previously proposed algorithm that also uses pipelining, as well as against an algorithm that, although lacking pipelining, exhibits the ability to produce very short schedules. The results confirm the potential to achieve a substantial throughput increase at the cost of increased latency.