Physical sensor difference-based method and virtual sensor difference-based method for visual and quantitative estimation of lower limb 3D gait posture using accelerometers and magnetometers

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

Download Full-text

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud

Sensors ◽

10.3390/s20071836 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1836

Author(s):

Ming-Zheng Zhang ◽

Liang-Min Wang ◽

Shu-Ming Xiong

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Virtual Sensor ◽

Virtual Sensors ◽

Machine Learning Methods ◽

Changing Trends ◽

Physical Sensors ◽

The Reformation ◽

Physical Sensor ◽

Sensor Cloud

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.

Download Full-text

Corrigendum to: “The relationship between foot posture and lower limb kinematics during walking: A systematic review” [Gait Posture 38 (2013) 363–372]

Gait & Posture ◽

10.1016/j.gaitpost.2014.05.008 ◽

2014 ◽

Vol 40 (4) ◽

pp. 735-736 ◽

Cited By ~ 1

Author(s):

Andrew K. Buldt ◽

George S. Murley ◽

Paul Butterworth ◽

Pazit Levinger ◽

Hylton B. Menz ◽

...

Keyword(s):

Systematic Review ◽

Lower Limb ◽

Foot Posture ◽

Limb Kinematics ◽

The Relationship ◽

Gait Posture ◽

Lower Limb Kinematics

Download Full-text

Data Imputation in Wireless Sensor Networks Using a Machine Learning-Based Virtual Sensor

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9020025 ◽

2020 ◽

Vol 9 (2) ◽

pp. 25

Author(s):

Michael Matusowsky ◽

Daniel T. Ramotsoela ◽

Adnan M. Abu-Mahfouz

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Missing Values ◽

Optimal Solution ◽

Sensor Nodes ◽

Wireless Sensor ◽

Data Imputation ◽

Virtual Sensor ◽

Virtual Sensors ◽

Physical Sensor

Data integrity in wireless sensor networks (WSN) is very important because incorrect or missing values could result in the system making suboptimal or catastrophic decisions. Data imputation allows for a system to counteract the effect of data loss by substituting faulty or missing sensor values with system-defined virtual values. This paper proposes a virtual sensor system that uses multi-layer perceptrons (MLP) to impute sensor values in a WSN. The MLP was trained using a genetic algorithm which efficiently reached an optimal solution for each sensor node. The system was able to successfully identify and replace physical sensor nodes that were disconnected from the network with corresponding virtual sensors. The virtual sensors imputed values with very high accuracies when compared to the physical sensor values.

Download Full-text

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Sensors ◽

10.3390/s22020454 ◽

2022 ◽

Vol 22 (2) ◽

pp. 454

Author(s):

German Sternharz ◽

Jonas Skackauskas ◽

Ayman Elhalwagy ◽

Anthony J. Grichnik ◽

Tatiana Kalganova ◽

...

Keyword(s):

Sensor Network ◽

Short Term Memory ◽

Model Output ◽

Validation Data ◽

Virtual Sensor ◽

Physical Sensor ◽

Signal Sample ◽

Electronic Hardware ◽

Self Protection

This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

Download Full-text