This paper proposes a novel in-network estimation approach for state estimation within delay constraints in wireless sensor networks. The approach jointly designs in-network estimation operations and an aggregation scheduling algorithm. The in-network estimation performed at relays optimally fuses estimates from different sensors and predicts estimates from upstream sensors that cannot be aggregated to the sink before deadlines. The proposed estimate aggregation scheduling algorithm is able to aggregate estimate information to the sink within delay constraints in an interference-free manner. Simulation results show the approach obtains significant estimation accuracy gains under different network settings.

1. IN-NETWORK ESTIMATION WITH DELAY CONSTRAINTS IN
WIRELESS SENSOR NETWORKS
ABSTRACT:
The use of wireless sensor networks (WSNs) for closing the loops between the cyberspace and
the physical processes is more attractive and promising for future control systems. For some real-
time control applications, controllers need to accurately estimate the process state within rigid
delay constraints. In this paper, we propose a novel in-network estimation approach for state
estimation with delay constraints in multihop WSNs. For accurately estimating a process state as
well as satisfying rigid delay constraints, we address the problem through jointly designing in-
network estimation operations and an aggregation scheduling algorithm.
Our in-network estimation operation performed at relays not only optimally fuses the estimates
obtained from the different sensors but also predicts the upper stream sensors' estimates which
cannot be aggregated to the sink before deadlines. Our estimate aggregation scheduling
algorithm, which is interference free, is able to aggregate as much estimate information as
possible from the network to the sink within delay constraints. We proved the unbiasedness of
in-network estimation, and theoretically analyzed the optimality of our approach. Our simulation
results corroborate our theoretical results and show that our in-network estimation approach can
obtain significant estimation accuracy gain under different network settings.
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