An Efficient Accelerated Learning Algorithm For Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks

2020 ◽  
Author(s):  
Hadi Alasti
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ad Hoc ◽  
Learning Algorithm ◽  
Accelerated Learning ◽  
Wireless Sensor ◽  
Spatially Correlated ◽  
Correlated Signals

scholarly journals Communication-Efficient Tracking of Unknown, Spatially Correlated Signals in Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5175
Author(s):  
Hadi Alasti
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spatial Modeling ◽  
Ad Hoc ◽  
Low Cost ◽  
Stochastic Gradient ◽  
Wireless Sensor ◽  
Contour Lines ◽  
Correlated Signals

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for climate and environmental monitoring. In this paper, the percentage of wireless sensors that initiate a communication attempt is assumed as cost. The performance evaluation results show that the proposed spatial tracking approach is low-cost and can model the spatial signal over time with the same performance as that of spatial modeling.

scholarly journals Communication-Efficient Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

2021 ◽  
Author(s):  
Hadi Alasti
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spatial Modeling ◽  
Ad Hoc ◽  
Low Cost ◽  
Stochastic Gradient ◽  
Wireless Sensor ◽  
Contour Lines ◽  
Correlated Signals

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for environmental monitoring. In this paper, the percentage of the communication attempts of wireless sensors is assumed as cost. Performance evaluation results show that the proposed spatial tracking approach is low cost and can model the spatial signal over time with the same performance as that of spatial modeling.

scholarly journals Self-Organization of Activity in Wireless Sensor Networks

2005 ◽  
Vol 1 (2) ◽  
pp. 245-252 ◽  
Author(s):  
P. Davis ◽  
A. Hasegawa ◽  
N. Kadowaki ◽  
S. Obana
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Wireless Sensors ◽  
Ad Hoc ◽  
Wireless Sensor ◽  
Self Organization ◽  
Sensor Systems ◽  
Low Resolution ◽  
Sensing Data

We propose a method for managing the spontaneous organization of sensor activity in ad hoc wireless sensor systems. The wireless sensors exchange messages to coordinate responses to requests for sensing data, and to control the fraction of sensors which are active. This method can be used to manage a variety of sensor activities. In particular, it can be used for reducing the power consumption by battery operated devices when only low resolution sensing is required, thus increasing their operation lifetimes.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

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