In a large-scale wireless sensor network, hundreds and thousands of sensors sample and forward data back to the sink periodically. In two real outdoor deployments GreenOrbs and CitySee, we observe that some bottleneck nodes strongly impact other nodes’ data collection and thus degrade the whole network performance. To figure out the importance of a node in the process of data collection, system manager is required to understand interactive behaviors among the parent and child nodes. So we present a management tool BOND (BOttleneck Node Detector), which explains the concept of Node Dependence to characterize how much a node relies on each of its parent nodes, and also models the routing process as a Hidden Markov Model and then uses a machine learning approach to learn the state transition probabilities in this model. Moreover, BOND can predict the network dataflow if some nodes are added or removed to avoid data loss and flow congestion in network redeployment. We implement BOND on real hardware and deploy it in an outdoor network system. The extensive experiments show that Node Dependence indeed help to explore the hidden bottleneck nodes in the network, and BOND infers the Node Dependence with an average accuracy of more than 85%.

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在一个大规模的无线传感器网络中，成百上千个传感器周期性地对数据进行采样并将数据转发回接收器。在两个真实的户外部署 GreenOrbs 和 CitySee 中，我们观察到一些瓶颈节点强烈影响其他节点的数据收集，从而降低了整个网络的性能。为了弄清楚节点在数据收集过程中的重要性，系统管理员需要了解父节点和子节点之间的交互行为。所以我们提出了一个管理工具 BOND（BOttleneck Node Detector），它解释了节点依赖来描述一个节点对其每个父节点的依赖程度，并将路由过程建模为隐马尔可夫模型，然后使用机器学习方法来学习该模型中的状态转移概率。此外，如果添加或删除一些节点，BOND 可以预测网络数据流，以避免网络重新部署时的数据丢失和流量拥塞。我们在真实硬件上实现 BOND，并将其部署在室外网络系统中。大量实验表明，节点依赖确实有助于探索网络中隐藏的瓶颈节点，并且 BOND 推断节点依赖平均准确率超过 85%。