Ground reaction force sensor fault detection and recovery method based on virtual force sensor for walking biped robots

This paper presents a novel method for ground force sensor faults detection and faulty signal reconstruction using Virtual force Sensor (VFS) for slow walking bipeds. The design structure of the VFS consists of two steps, the total ground reaction force (GRF) and its location estimation for each leg based on the center of mass (CoM) position, the leg kinematics, and the IMU readings is carried on in the first step. In the second step, the optimal estimation of the distributed reaction forces at the contact points in the feet sole of walking biped is carried on. For the optimal estimation, a constraint model is obtained for the distributed reaction forces at the contact points and the quadratic programming optimization method is used to solve for the GRF. The output of the VFS is used for fault detection and recovery. A faulty signal model is formed to detect the faults based on a threshold, and recover the signal using the VFS outputs. The sensor offset, drift, and frozen output faults are studied and tested. The proposed method detects and estimates the faults and recovers the faulty signal smoothly. The validity of the proposed estimation method was confirmed by simulations on 3D dynamics model of the humanoid robot SURALP while walking. The results are promising and prove themselves well in all of the studied fault cases

Fault Detection In Wireless Sensor Network Using Distributed Approach

In recent days, Wireless Sensor Networks are emerging as a promising and interesting area. Wireless Sensor Network consists of a large number of heterogeneous/homogeneous sensor nodes which communicates through wireless medium and works cooperatively to sense or monitor the environment. The number of sensor nodes in a network can vary from hundreds to thousands. The node senses data from Environment and sends these data to the gateway node. Mostly WSNs are used for applications such as military surveillance and disaster monitoring. We propose a distributed localized faulty sensor detection algorithm where each sensor identifies its own status to be either ”good” or ”faulty” which is then supported by its neighbors as they also check the node behavior. Finally, the algorithm is tested under different number of faulty sensors in the same area. Our Simulation results demonstrate that the time consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large number of faulty sensors existing in the network

Distributed Fault Detection In Wireless Sensor Network

In recent days, WSNs are emerging as a promising and interesting area. Wireless Sensor Network consists of a large number of heterogeneous/homogeneous sensor nodes which communicates through wireless medium and works cooperatively to sense or monitor the environment. The number of sensor nodes in a network can vary from hundreds to thousands. The node senses data from environment and sends these data to the gateway node. Mostly WSNs are used for applications such as military surveillance and disaster monitoring. We propose a distributed localized faulty sensor detection algorithm where each sensor identifies its own status to be either ”good” or ”faulty” which is then supported by its neighbors as they also check the node behavior. Finally, the algorithm is tested under different number of faulty sensors in the same area. Our Simulation results demonstrate that the time consumed to find out the faulty nodes in our proposed algorithm is relatively less with a large number of faulty sensors existing in the networ

An Adaptive Fault-Tolerant Event Detection Scheme for Wireless Sensor Networks

In this paper, we present an adaptive fault-tolerant event detection scheme for wireless sensor networks. Each sensor node detects an event locally in a distributed manner by using the sensor readings of its neighboring nodes. Confidence levels of sensor nodes are used to dynamically adjust the threshold for decision making, resulting in consistent performance even with increasing number of faulty nodes. In addition, the scheme employs a moving average filter to tolerate most transient faults in sensor readings, reducing the effective fault probability. Only three bits of data are exchanged to reduce the communication overhead in detecting events. Simulation results show that event detection accuracy and false alarm rate are kept very high and low, respectively, even in the case where 50% of the sensor nodes are faulty

Fault Discrimination in Wireless Sensor Networks

In current times, one of the promising and interesting areas of research is Wireless Sensor Networks. A Wireless Sensor Network consists of spatially distributed sensors to monitor environmental and physical conditions such as temperature, sound, pressure etc. It is built of nodes where each node is connected to one or more sensors. They are used for Medical applications, Security monitoring, Structural monitoring and Traffic monitoring etc. The number of sensor nodes in a Wireless Sensor Network can vary in the range of hundreds to thousands. In this project work we propose a distributed algorithm for detection of faults in a Wireless Sensor Network and to classify the faulty nodes. In our algorithm the sensor nodes are classified as being Fault Free, Transiently Faulty or Intermittently Faulty considering the energy differences from its neighbors in different rounds of the algorithm run. We have shown the simulation results in the form of the output messages from the nodes depicting their health and also compared the results in form of graphs for different average node degrees and different number of rounds of our algorithm run

Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure

LEDFD: A Low Energy Consumption Distributed Fault Detection Algorithm for Wireless Sensor Networks

Detection of faulty nodes and network energy saving have become the hottest research topics. Furthermore, current fault detection algorithms always pursue high detection performance but neglect energy consumption. In order to obtain good fault detection performance and save the network power, this paper proposes a low energy consumption distributed fault detection algorithm (LEDFD), which takes full advantage of temporally correlated and spatially correlated characteristics of the sensor nodes. LEDFD utilizes the temporally correlated information to examine some faulty nodes and then utilizes the spatially correlated information to examine the nodes that have not been detected as faulty through exchanging information among neighbor nodes to determine those nodes' state. Because LEDFD takes the data produced by nodes themselves to detect certain types of faults, which means nodes need not exchange information with their neighbor nodes during the entire detection process, the energy consumption of networks is efficiently reduced. Experimental results show that the algorithm has good performance and low energy consumption compared with current algorithms. </jats:p

Malicious and Malfunctioning Node Detection via Observed Physical Layer Data

There are many mechanisms that can cause inadequate or unreliable information in sensor networks. A user of the network might be interested in detecting and classifying specific sensors nodes causing these problems. Several network layer based trust methods have been developed in previous research to assess these issues; in contrast this work develops a trust protocol based on observations of physical layer data collected by the sensors. Observations of physical layer data are used for decisions and calculations, and are based on just the measurements collected by the sensors. Although this information is packaged and distributed on the network layer, only the physical measurement is considered. This protocol is used to detect faulty nodes operating in the sensor network. The context of this research is Wireless Network Discovery (WND), which refers to modeling all layers of a non-cooperative wireless network. The focus in particular is the localization of transmitters, and detection of sensors affecting the localization. To accomplish this, a model for faulty sensors and two methods of detection are developed. Detection rates are analyzed with Receiver Operating Characteristic (ROC) curves, and the trade-off of detection versus localization error is discussed. Classification between faulty sensors is also considered to determine appropriate response to potential network attacks

Design and Analysis of Distributed Faulty Node Detection in Networks

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design. PRD assigns weights to individual links as well as end-to-end delay, so as to reflect the node status in the long run of the network. Large-scale simulation results demonstrate that PRD performs better than the widely used ETX metric as well as other two metrics devised recently in terms of energy consumption and end-to-end delay, while guaranteeing packet delivery ratio.