TY - GEN
T1 - Robust distributed least-squares estimation in sensor networks with node failures
AU - Zhou, Qing
AU - Kar, Soummya
AU - Huie, Lauren
AU - Poor, H. Vincent
AU - Cui, Shuguang
PY - 2011
Y1 - 2011
N2 - Algorithms are studied for distributed least-squares (DLS) estimation of a scalar target signal in sensor networks. Due to the observation locality and the limited sensing ability, the individual sensor estimates are far from being reliable. To obtain a more reliable estimate of the target signal, the sensors could collaborate by iteratively exchanging messages with their neighbors, to refine their local estimates over time. Such an iterative DLS algorithm is investigated in this paper with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists between the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology.
AB - Algorithms are studied for distributed least-squares (DLS) estimation of a scalar target signal in sensor networks. Due to the observation locality and the limited sensing ability, the individual sensor estimates are far from being reliable. To obtain a more reliable estimate of the target signal, the sensors could collaborate by iteratively exchanging messages with their neighbors, to refine their local estimates over time. Such an iterative DLS algorithm is investigated in this paper with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists between the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology.
KW - Distributed least-squares estimation
KW - node failures
KW - sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84857208572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857208572&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2011.6133690
DO - 10.1109/GLOCOM.2011.6133690
M3 - Conference contribution
AN - SCOPUS:84857208572
SN - 9781424492688
T3 - GLOBECOM - IEEE Global Telecommunications Conference
BT - 2011 IEEE Global Telecommunications Conference, GLOBECOM 2011
T2 - 54th Annual IEEE Global Telecommunications Conference: "Energizing Global Communications", GLOBECOM 2011
Y2 - 5 December 2011 through 9 December 2011
ER -