In this paper, an analytical model is proposed to compute the optimal number of clusters that minimizes the energy consumption of multi-hop wireless sensor networks. In the proposed analytical model, the average hop count between a general node (GN) and its nearest clusterhead (CH) is obtained assuming a uniform distribution. How the position of the sink impacts the optimal number of clusters is also discussed. A numerical simulation is carried out to validate the proposed model in various network environments.

This letter presents new delayed perturbation bounds (DPBs) for stabilizing receding horizon H∞ control (RHHC). The linear matrix inequality (LMI) approach to determination of DPBs for the RHHC is proposed. We show through a numerical example that the RHHC can guarantee an H∞ norm bound for a larger class of systems with delayed perturbations than conventional infinite horizon H∞ control (IHHC).

We propose a stability-guaranteed horizon size (SgHS) for stabilizing receding horizon control (RHC). It is shown that the proposed SgHS can be represented explicitly in terms of the known parameters of the given system model and is independent of the terminal weighting matrix in the cost function. The proposed SgHS is validated via a numerical example.

This letter propose a new H∞ smoother (HIS) with a finite impulse response (FIR) structure for discrete-time state-space models. This smoother is called an H∞ FIR smoother (HIFS). Constraints such as linearity, quasi-deadbeat property, FIR structure, and independence of the initial state information are required in advance. Among smoothers with these requirements, we choose the HIFS to optimize H∞ performance criterion. The HIFS is obtained by solving the linear matrix inequality (LMI) problem with a parametrization of a linear equality constraint. It is shown through simulation that the proposed HIFS is more robust against uncertainties and faster in convergence than the conventional HIS.

This letter discusses the prediction of the time-varying bit error rate (BER) for a transmitting channel using recent transmissions and retransmissions. Depending on the predicted BER, we propose a maximum frame size control to improve the goodput in wireless networks. It is shown, using simulation, that when the maximum frame size is controlled relative to the time-varying BER the goodput of the network is improved.

This letter presents parametric uncertainty bounds (PUBs) for stabilizing receding horizon H∞ control (RHHC). The proposed PUBs are obtained easily by solving convex optimization problems represented by linear matrix inequalities (LMIs). We show, by numerical example, that the RHHC can guarantee a H∞ norm bound for a larger class of uncertain systems than conventional infinite horizon H∞ control (IHHC).

This paper concerns a problem of on-line model parameter estimations for multiple time-delay systems. In order to estimate unknown model parameters from measured state variables, we propose two schemes using Lyapunov's direct method, called parallel and series-parallel model estimators. It is shown through a numerical example that the proposed parallel and series-parallel model estimators can be effective when sufficiently rich inputs are applied.

This letter presents robustness bounds (RBs) for receding horizon controls (RHCs) of uncertain systems. The proposed RBs are obtained easily by solving convex problems represented by linear matrix inequalities (LMIs). We show, by numerical examples, that the RHCs can guarantee robust stabilization for a larger class of uncertain systems than conventional linear quadratic regulators (LQRs).

In this letter, we propose a new type of recursive least squares (RLS) algorithms without using the initial information of a parameter or a state to be estimated. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS algorithms are simpler and more numerically stable. It is shown through simulation that the proposed RLS algorithms have better numerical stability for digital computations than existing algorithms.