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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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. 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. 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. A class of energy efficient schemes is proposed for detection and tracking of the boundaries of correlated spatial distributions, such as air pollution in large cities at a known threshold level, using large scale wireless sensor network. It is shown that without having the exact statistics of the signal, distributed collaborative space and time oriented filtering can be used to efficiently detect the boundary of the threshold level of a correlated spatial distribution over time. Collaborative signal processing is used to reduce the effect of noisy observations and distributed space and time domain filtering is applied for energy conservative threshold level tracking. It is shown that by using spatial and time oriented filtering, the operating mode of the wireless sensor network is shifted from communication dominant mode toward computation and sensing dominant mode, which significantly saves the in-network energy. The performance of the discussed approach for a few correlated random spatial distributions is evaluated using computer simulations. We study the problem of data-driven routing and navigation in a distributed sensor network over a continuous scalar field. Specifically, we address the problem of searching for the collection of sensors with readings within a specified range. This is named the iso-contour query problem. We develop a gradient based routing scheme such that from any query node, the query message follows the signal field gradient or derived quantities and successfully discovers all iso-contours of interest. Due to the existence of local maxima and minima, the guaranteed delivery requires preprocessing of the signal field and the construction of a contour tree in a distributed fashion. Our approach has the following properties: (i) the gradient routing uses only local node information and its message complexity is close to optimal, as shown by simulations; (ii) the preprocessing message complexity is linear in the number of nodes and the storage requirement for each node is a small constant. The same preprocessing also facilitates route computation between any pair of nodes where the the route lies within any user supplied range of values. 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. A novel on-demand, adaptive compressed sensing approach is proposed and discussed for spatial monitoring of correlated massive data in dense wireless sensor field. The spatial monitoring is based on collecting the sensor observations of a subset of sensors that their reading is in Δ margin of a set of contour levels. The contour level set is adaptively updated in order to reduce the mean square error of reconstruction of the spatial distribution. The reconstruction error and the number of involved sensing nodes as cost are used to compare the proposed approach with spatial monitoring using equally spaced contour levels. We propose a distributed scheme called Adaptive-Group-Merge for sensor networks that, given a parameter k, approximates a geometric shape by a k-vertex polygon. The algorithm is well suited to the distributed computing architecture of sensor networks, and we prove that its approximation quality is within a constant factor of the optimal. We also show through simulation that our scheme outperforms several other alternatives in preserving important shape features, and achieves approximation quality almost as good as the optimal, centralized scheme. Because many applications of sensor networks involve observations and monitoring of physical phenomena, the ability to represent complex geometric shapes faithfully but using small memory is vital in many settings.