Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, we propose to use power as a new dimension for interference graph estimation (IGE) and integrate IGE with concurrent flooding such that IGE can be done simultaneously with flooding using the same frequency-time resources. With controlled and real-world experiments, we show that it is feasible to efficiently achieve IGE via concurrent flooding on the commercial off-the-shelf (COTS) devices by controlling the transmit powers of nodes. We believe that efficient IGE would be a key enabler for the practical use of the existing scheduling algorithms assuming known interference graphs.

翻译：传统网络管理智慧分别分配网络资源用于测量任务和数据传输任务。繁重的测量任务可能占用数据传输资源，显著降低网络性能。因此，被认为会带来巨大测量开销的干扰图在无线网络中实际应用面临挑战。为解决无线传感器网络中的这一挑战，我们提出将功率作为干扰图估计的新维度，并将干扰图估计与并发洪泛相结合，使得干扰图估计能够与洪泛在同一时频资源上同时进行。通过受控实验和真实环境实验，我们证明通过控制商用现成设备的节点发射功率，利用并发洪泛高效实现干扰图估计是可行的。我们相信，高效的干扰图估计将成为现有基于已知干扰图假设的调度算法得以实际应用的关键推动因素。