A symmetric adaptive algorithm for speeding-up consensus

Performing distributed consensus in a network has been an important research problem for several years, and is directly applicable to sensor networks, autonomous vehicle formation, etc. While there exists a wide variety of algorithms that can be proven to asymptotically reach consensus, in applications involving time-varying parameters and tracking, it is often crucial to reach consensus “as quickly as possible”. In [1] it has been shown that, with global knowledge of the network topology, it is possible to optimize the convergence time in distributed averaging algorithms via solving a semi-definite program (SDP) to obtain the optimal averaging weights. Unfortunately, in most applications, nodes do not have knowledge of the full network topology and cannot implement the required SDP in a distributed fashion. In this paper, we present a symmetric adaptive weight algorithm for distributed consensus averaging on bi-directional noiseless networks. The algorithm uses an LMS (Least Mea...

• Type: Journal
• Year: 2010
• Where: ICASSP
• Authors: Daniel Thai, Elizabeth Bodine-Baron, Babak Hassibi
• Keywords: Algorithm, Convergence Time, ICASSP 2010, Network Topology, Signal Processing