Efficient Interference Graph Estimation via Concurrent Flooding
This work addresses the problem of resource inefficiency in network management for wireless sensor networks, enabling practical use of existing scheduling algorithms by reducing measurement overhead.
The paper tackles the challenge of heavy measurement overhead in interference graph estimation (IGE) for wireless networks by proposing to use power as a new dimension and integrate IGE with concurrent flooding, enabling simultaneous execution with data transmission. Results from controlled and real-world experiments show it is feasible to efficiently achieve IGE on commercial off-the-shelf devices by controlling transmit powers.
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.