Arunanshu Mahapatro

Bio: Arunanshu Mahapatro is an academic researcher from Veer Surendra Sai University of Technology. The author has contributed to research in topics: Wireless sensor network & Fault detection and isolation. The author has an hindex of 7, co-authored 27 publications receiving 316 citations. Previous affiliations of Arunanshu Mahapatro include National Institute of Technology, Rourkela & National Institute of Standards and Technology.

Fault Diagnosis in Wireless Sensor Networks: A Survey

Abstract: The sensor nodes in wireless sensor networks may be deployed in unattended and possibly hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the network. In addition, node failures and environmental hazards cause frequent topology changes, communication failures, and network partitioning. This in turn adds a new dimension to the fragility of the network topology. Such perturbations are far more common than those found in conventional wireless networks thus, demand efficient techniques for discovering disruptive behavior in such networks. Traditional fault diagnosis techniques devised for multiprocessor systems are not directly applicable to wireless sensor networks due to their specific requirements and limitations. This survey integrates research efforts that have been produced in fault diagnosis specifically for wireless sensor networks. The survey aims at clarifying and uncovering the potential of this technology by providing the technique-based taxonomy. The fault diagnosis techniques are classified based on the nature of the tests, correlation between sensor readings and characteristics of sensor nodes and the network.

Online Distributed Fault Diagnosis in Wireless Sensor Networks

Abstract: This paper presents an online fault diagnosis algorithm for wireless sensor networks. This work explicitly takes into account the possibility of faults in different sections of sensor networks and communication channel. The diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. These local views are then disseminated using a spanning tree of cluster heads. Our algorithm is shown to be energy efficient as it works in conjunction with the normal network activities and requires minimum additional diagnostic messages to be exchanged. Simulation results show that the performance of our algorithm is less sensitive to the average node degree for a wide range of fault rates.

Detection and diagnosis of node failure in wireless sensor networks: A multiobjective optimization approach

Abstract: Detection of intermittent faults in sensor nodes is an important issue in sensor networks. This requires repeated application of test since an intermittent fault will not occur consistently. Optimization of inter test interval and maximum number of tests required is crucial. In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem. The two objectives, i.e., detection latency and energy overhead are taken into consideration. Tuning of detection parameters based on two-lbests based multiobjective particle swarm optimization (2LB-MOPSO) algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A comparative study of the performance of the three algorithms is carried out, which show that the 2LB-MOPSO is a better candidate for solving the multiobjective problem of intermittent fault detection. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.

Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

Abstract: In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.

Detection of Node Failure in Wireless Image Sensor Networks

Abstract: A sequenced process of fault detection followed by dissemination of decision made at each node characterizes the sustained operations of a fault-tolerant wireless image sensor network (WISN). This paper presents a distributed self-fault diagnosis model for WISN where fault diagnosis is achieved by disseminating decision made at each node. Architecture of fault-tolerant wireless image sensor nodes is presented. Simulation results show that sensor nodes with hard and soft faults are identified with high accuracy for a wide range of fault rate. Both time and message complexity of the proposed algorithm are 𝑂(𝑛) for an 𝑛-node WISN.

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Abstract: Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

Fault Diagnosis in Wireless Sensor Networks: A Survey

Abstract: The sensor nodes in wireless sensor networks may be deployed in unattended and possibly hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the network. In addition, node failures and environmental hazards cause frequent topology changes, communication failures, and network partitioning. This in turn adds a new dimension to the fragility of the network topology. Such perturbations are far more common than those found in conventional wireless networks thus, demand efficient techniques for discovering disruptive behavior in such networks. Traditional fault diagnosis techniques devised for multiprocessor systems are not directly applicable to wireless sensor networks due to their specific requirements and limitations. This survey integrates research efforts that have been produced in fault diagnosis specifically for wireless sensor networks. The survey aims at clarifying and uncovering the potential of this technology by providing the technique-based taxonomy. The fault diagnosis techniques are classified based on the nature of the tests, correlation between sensor readings and characteristics of sensor nodes and the network.

Wireless Body Area Network (WBAN): A Survey on Reliability, Fault Tolerance, and Technologies Coexistence

Abstract: Wireless Body Area Network (WBAN) has been a key element in e-health to monitor bodies. This technology enables new applications under the umbrella of different domains, including the medical field, the entertainment and ambient intelligence areas. This survey paper places substantial emphasis on the concept and key features of the WBAN technology. First, the WBAN concept is introduced and a review of key applications facilitated by this networking technology is provided. The study then explores a wide variety of communication standards and methods deployed in this technology. Due to the sensitivity and criticality of the data carried and handled by WBAN, fault tolerance is a critical issue and widely discussed in this paper. Hence, this survey investigates thoroughly the reliability and fault tolerance paradigms suggested for WBANs. Open research and challenging issues pertaining to fault tolerance, coexistence and interference management and power consumption are also discussed along with some suggested trends in these aspects.

Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks

Abstract: In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T–S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T–S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.