This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth co...This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth constraints, a space-efficient packet marking scheme is first introduced. The scheme uses a Bloom filter as a compression tool so that path information can bc piggybacked by data packets. Based on the path information, LoNI then adopts a fast algorithm to detect lossy nodes. The algorithm formulates the inference problem as a weighted set-cover problem and solves it using a greedy approach with low complexity. Simulations show that LoNI can locate about 80% of lossy nodes when lossy nodes are rare in the network. Furthermore, LoNI performs better for the lossy nodes near the sink or with higher loss rates.展开更多
文摘This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth constraints, a space-efficient packet marking scheme is first introduced. The scheme uses a Bloom filter as a compression tool so that path information can bc piggybacked by data packets. Based on the path information, LoNI then adopts a fast algorithm to detect lossy nodes. The algorithm formulates the inference problem as a weighted set-cover problem and solves it using a greedy approach with low complexity. Simulations show that LoNI can locate about 80% of lossy nodes when lossy nodes are rare in the network. Furthermore, LoNI performs better for the lossy nodes near the sink or with higher loss rates.
