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A weighted stochastic gradient algorithm is proposed for cost‐efficient tracking of unknown, correlated spatial signals from randomly distributed sensor observations in localized wireless sensor field. The algorithm is implemented in spatial modelling and spatial tracking phases. In spatial modelling phase, the algorithm finds the model parameters, and in spatial tracking phase, it updates these parameters. The spatial signal is modelled with its M iso‐contour lines at equally spaced levels {ℓ}k=1M and the sensors with sensor observations in Δ margin of these contour levels report to the fusion centre (FC) for spatial monitoring purpose. Based on progressive learning and in successive iterations, the algorithm improves its findings of the signal strength's range, and the spatial, temporal and spectral attributes of the signal. To reduce the cost, in each iteration, only a subset of wireless sensors transmits the observations to the FC, in response to its query. In this article, the percentage of the reporting sensors to the FC is defined as the algorithm's cost. With importance sampling perspective, the sample space is reduced to those sensors whose observations are within a Δ margin of atleast one of these M contour levels. The Δ margin is pruned or enhanced using the proposed weighted stochastic gradient algorithm, dynamically in order to reduce the spatial tracking cost. The evaluation results show that after spatial modelling, spatial tracking is drastically of low cost and its performance is better than that of the conventional stochastic gradient method. The modelling error, the cost and the convergence of the proposed algorithm are investigated extensively, in this article. Spatial correlation in signal distribution and the coordinates of the wireless sensors are the only initial assumptions in spatial monitoring of the unknown signal distribution. The main purpose of this algorithm is low‐cost identification of unknown correlated spatial signals from sensor observations, over time. An example for application of the proposed algorithm is environmental monitoring using wireless sensor observations.

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In this paper, a control theoretic framework for the rate control of up-link (also known as reverse-link) of IS856 (also known as cdma2000 1xEV-DO) is proposed. This control theoretic framework provides strong tools to analyze IS856 up-link rate control, and also addresses its stability and performance issues. Using linear and nonlinear discrete-time control theory, optimal blocks for both the base-station as well as the mobile stations are designed to achieve the performance goals for the up-link rate control mechanism.