A Tiny and Efficient Wireless Ad-hoc Protocol for Low-cost Sensor Networks

2007 ◽  
Author(s):  
Pawel Gburzynski ◽  
Bozena Kaminska ◽  
Wlodek Olesinski
Keyword(s):  
Sensor Networks ◽  
Ad Hoc ◽  
Low Cost ◽  
Ad Hoc Protocol

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.

Two-hop hello protocol for mobile Ad Hoc and sensor networks

2011 ◽  
Vol 30 (12) ◽  
pp. 3158-3160
Author(s):  
Jia YU ◽  
Ying-you WEN ◽  
Hong ZHAO
Keyword(s):  
Sensor Networks ◽  
Ad Hoc ◽  
Mobile Ad Hoc

scholarly journals Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review

Vibration ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 551-584
Author(s):  
Samir Mustapha ◽  
Ye Lu ◽  
Ching-Tai Ng ◽  
Pawel Malinowski
Keyword(s):  
Sensor Networks ◽  
Health Monitoring ◽  
Data Transmission ◽  
System Integration ◽  
Structural Integrity ◽  
Low Cost ◽  
Data Communication ◽  
Total Cost ◽  
Design And Optimization ◽  
Successful Case

The development of structural health monitoring (SHM) systems and their integration in actual structures has become a necessity as it can provide a robust and low-cost solution for monitoring the structural integrity of and the ability to predict the remaining life of structures. In this review, we aim at focusing on one of the important issues of SHM, the design, and implementation of sensor networks. Location and number of sensors, in any SHM system, are of high importance as they impact the system integration, system performance, and accuracy of assessment, as well as the total cost. Hence we are interested in shedding the light on the sensor networks as an essential component of SHM systems. The review discusses several important parameters including design and optimization of sensor networks, development of academic and commercial solutions, powering of sensors, data communication, data transmission, and analytics. Finally, we presented some successful case studies including the challenges and limitations associated with the sensor networks.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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