Peer-to-Peer Compressive Sensing for Network Monitoring

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

“…is the 1 norm of X. A collaborative and distributed framework according to which nodes cooperate with each other in order to monitor the status of the entire network (i.e., efficiently recover any k-sparse networked data vector) is presented in [43]. Every node, apart from its own measurements utilizes measurements generated by the rest nodes, with the goal of reducing the number of overall required measurements.…”

“…Fragouli et al [27] binary trees Sattari et al [28] m-ary trees, M-by-N DAGs Jithin et al [30] M-by-N DAGs, asynchronous sources Yao et al [31] RLNC, NRSC, passive tomography Mohammad et al [33] RLNC, congested link location Gui et al [34] mesh DAG topologies Gui et al [35] mesh DAG topologies, minimum probe size Shah-Mansouri et al [36] WSN, RLNC, subspace property, virtual sources Sattari et al [37] trees and general topologies Sattari et al [38] trees with multiple sources Fan et al [52] weighted 1 minimization, expander graphs Chen et al [53] link congestion probabilities, greedy iterative algorithm Takemoto et al [54] measurement paths construction, low-quality link detection Morita et al [56] wireless multihop networks, GFT, spatially dependent channels Bandara et al [57] scalable, adaptive fault localization Firooz et al [41] k-identifiability, expander graphs Fattaholmanan et al [43] collaborative distributed framework, individual matrix for every node Wang et al [44] expander graphs, 1 minimization Fan et al [45] synchronization errors, constrained 1 − 2 optimization Nakanishi et al [46] no clock synchronization, reflective NT Nakanishi et al [47] synchronization errors, differential routing matrix Kinsho et al [48] mobile networks, GFT & passive measurements Wei et al [49] dynamic networks, line graph model Table 2. Overview of key points, advantages, and performance of selected tomographic methods.…”

A Review of Applications and Approaches of Network Monitoring

“…This itself generates a lot of overhead in the network. A model is proposed in which nodes pick up information from measurements generated by other nodes, an upper bound is derived for the number of measurements that each node must generate, such that the expected number of measurements observed by each node is sufficient to provide a global view of the entire networked data (27).…”