A significantly low cost and tractable progressive learning approach is proposed and discussed for efficient spatiotemporal monitoring of a completely unknown, two dimensional correlated signal distribution in localized wireless sensor field. The spatial distribution is compressed into a number of its contour lines and only those sensors that their sensor observations are in a @math margin of the contour levels are reporting to the information fusion center (IFC). The proposed algorithm progressively finds the model parameters in iterations, by using extrapolation in curve fitting, and stochastic gradient method for spatial monitoring. The IFC tracks the signal variations using these parameters, over time. The monitoring performance and the cost of the proposed algorithm are discussed, in this letter.
Contour line detection in wireless sensor networks, which is the first step in modeling the spatial distribution has been addressed in several researches, including @cite_13 @cite_31 @cite_1 @cite_17 @cite_18 @cite_15 @cite_26 @cite_30 @cite_12 @cite_20 @cite_16 . Most of these mentioned researches addressed to distributed-contour-detection that is based on collaboration among sensors for detection of the contour lines. In this letter, we propose a cost efficient centralized algorithm based on the approach proposed in @cite_13 .
@cite_30 @cite_18 @cite_26 @cite_15 @cite_1 @cite_16 @cite_31 @cite_20 @cite_13 @cite_12 @cite_17
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An algorithm to extract contour lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, contour lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the contour line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm. An algorithm to extract contour lines using wireless sensor networks is proposed in this work. In con- trast with previous work on edge detection that is primarily concerned with the region of a certain phenomenon, contour lines offer more detailed information about the underlying phenomenon such as signal's amplitude, density and source location. A distributed algorithm to extract the contour lines information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Sim- ulation results are provided to demonstrate the efficiency of the proposed algorithm. Furthermore, a thorough per- formance analysis is conducted to understand the effects of the sensor density and the background noise power on the performance of the system. A robust filter-based approach is proposed for wireless sensor networks for detecting contours of a signal distribution over a 2-dimensional region. The motivation for contour detection is derived from applications where the spatial distribution of a signal (such as temperature, soil moisture level, etc.) is to be determined over a large region with minimum communication cost. The proposed scheme applies multi-level quantization to the sensor signal values to artificially create an edge and then applies spatial filtering for edge detection. The spatial filter is localized and is based on an adaptation of the Prewitt filter used in image processing. Appropriate mechanisms are introduced that minimizes the cost for communication required for collaboration. Simulation results are presented to show the error performance of the proposed contour detection scheme and the associated communication cost (single-hop communications with immediate neighborhood in average) in the network. This paper presents algorithms for efficiently detecting the variation of a distributed signal over space and time using large scale wireless sensor networks. The proposed algorithms use contours for estimating the spatial distribution of a signal. A contour tracking algorithm is proposed to efficiently monitor the variations of the contours with time. Use of contours reduces the communication cost by reducing the participation of sensor nodes for the monitoring tasks. The proposed schemes use multi-sensor collaboration techniques and non-uniform contour levels to reduce the error in reconstructing the signal distribution. Results from computer simulations are presented to demonstrate the performance of the proposed schemes.