Chase++: Fountain-Enabled Fast Flooding in Asynchronous Duty Cycle Networks

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload’s size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase ++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase ++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload’s continuous loss. Moreover, Chase ++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase ++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase ++ in TinyOS with TelosB nodes. We further evaluate Chase ++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.