BOND: Exploring Hidden Bottleneck Nodes in Large-Scale Wireless Sensor Networks

In a large-scale wireless sensor network, thousands of sensor nodes periodically generate and forward data back to the sink. In our recent outdoor deployment, we observe that some bottleneck nodes can greatly determine other nodes' data collection ratio, and thus affect the whole network performance. To figure out the importance of a node in data collection, the manager needs to understand the interactive behaviors among the parent and child nodes. To address this issue, we present a management tool BOND (Bottleneck Node Detector). We introduce the concept of Node Dependence to characterize how much a node relies on each of its parent nodes. BOND models the routing process as a Hidden Markov Model, and uses a machine learning approach to learn the state transition probabilities in this model based on the observed traces. BOND utilizes Node Dependence to explore the hidden bottleneck nodes in the network. Moreover, we can predict how adding or removing the sensor nodes would impact the data flow, thus avoid data loss and flow congestion in redeployment. We implement our tool on real hardware and deploy it in an outdoor system. Our extensive experiments show that BOND infers the Node Dependence with an average accuracy of more than 85%.