Rakesh Ranjan Swain

An effective graph-theoretic approach towards simultaneous detection of fault(s) and cut(s) in wireless sensor networks

Summary A failed sensor node partitions a wireless sensor network (WSN) into 2 or more disjoint components, which is called as a cut in the network. The cut detection has been considered as a very challenging problem in the WSN research. In this paper, we propose a graph-theoretic distributed protocol to detect simultaneously the faults and cuts in the WSN. The proposed approach could be accomplished mainly in 3 phases, such that initialization phase, fault detection phase, and a cut detection phase. The protocol is an iterative method where at every time iteration, the node updates its state to calculate the potential factor. We introduced 2 terminologies such as a safe zone or cut zone of the network. The proposed method has been evaluated regarding various performance evaluation measures by implementing the same in the network simulator NS–2.35. The obtained results show that the proposed graph-theoretic approach is simple yet very powerful for the intended tasks.

Controlling Wall Following Robot Navigation Based on Gravitational Search and Feed Forward Neural Network

Automated control of mobile robot navigation is a challenging area in the field of robotics research. In this work, an attempt is made to use a new neural network training algorithm based on gravitational search (GS) and feed forward neural network (FFNN) for automatic robot navigation of wall following mobile robots. The GS strategy is used for setting the optimal weight set of the FFNN so as to increase the performance of the neural network. The algorithm is tested with three large datasets obtained from UCI machine learning repository, containing a sequence of sensor readings where sensors are arranged around the waist of the SCITOS G5 robot. The proposed method shows promising results for all the datasets.

Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.

A fuzzy MLP approach for fault diagnosis in wireless sensor networks

This paper presents a fault diagnosis protocol for wireless sensor networks (WSNs) based on neural network approach. A particle swarm optimization based fuzzy multilayer perceptron is used in the fault detection and classif cation phase of the protocol. The proposed protocol considers the composite fault model such as hard permanent, soft permanent, intermittent, and transient fault. The performance of the proposed algorithm is evaluated by using generic parameters such as detection accuracy, false alarm rate, and false positive rate. The simulation is carried out by standard network simulator NS-2.35 and the performance is compared with the existing fault diagnosis protocols. The result shows that the proposed protocol performs superior than the existing protocols.

Heterogeneous fault diagnosis for wireless sensor networks

Abstract Fault diagnosis has been considered as a very challenging problem in wireless sensor network (WSN) research. Faulty nodes having different behavior such as hard, soft, intermittent, and transient fault are called as heterogeneous faults in wireless sensor networks. This paper presents a heterogeneous fault diagnosis protocol for wireless sensor networks. The proposed protocol consists of three phases, such as clustering phase, fault detection phase, and fault classification phase to diagnose the heterogeneous faulty nodes in the wireless sensor networks. The protocol strategy is based on time out mechanism to detect the hard faulty nodes, and analysis of variance method (ANOVA test) to detect the soft, intermittent, and transient faulty nodes in the network. The feed forward probabilistic neural network (PNN) technique is used to classify the different types of faulty nodes in the network. The performance of the proposed heterogeneous fault diagnosis protocol is evaluated using network simulator NS-2.35. The evaluation of the proposed model is also carried out by the testbed experiment in an indoor laboratory environment and outdoor environment.

Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes

In the broad research area of wireless sensor networks (WSN), detection of link failure is still in its infancy. In this paper, we propose to use a neural network model for detection of link failure in WSN. The neural network has been allowed to learn and adapt with the help of gradient descent based learning algorithm. We demonstrate the proposed model with regard to the preparation of training data and implementation of the model. This paper also provides a thorough theoretical and analytical investigation of link failures in WSN. The proposed neural network based model has been evaluated carefully with regard to testbed experiments. The simulation-based experiment has been conducted to justify the applicability of the proposed model for dense networks that could contain around 1000 links. We also analyze the theoretical performance of the proposed neural network based algorithm with regard to various performance evaluative measures such as failure detection accuracy, false alarm rate. The simulated experiments, as well as the testbed experiments in indoor and outdoor environments, suggest that the method is capable of link failure detection with higher detection rate and it is consistent. Furthermore, this article also reports a comprehensive case study as an extension of this present research towards automated detection of disjoint and disconnected nodes in a sensor network.

Investigation of RBF Kernelized ANFIS for Fault Diagnosis in Wireless Sensor Networks

Wireless sensor networks (WSN) are often deployed in human inaccessible environments. Some examples of such environments that are difficult for quick human reach include deep forests, various hazardous industries, hilltops, and sometimes underwater. The occurrence of failures in sensor networks is inevitable due to continuous or instant change in environmental parameters. A failure may lead to faulty readings which in turn may cause economic and physical damages to the environment. In this work, a thorough investigation has been conducted on the application of adaptive neuro-fuzzy inference system (ANFIS) for automated fault diagnosis in WSN. Further, a kernelized version of ANFIS has also been studied for the discussed problem. To avoid the model’s undesired biases toward a specific type of failure, oversampling has been done for multiple version of the ANFIS model. This study would serve as a guideline for the community toward the application of fuzzy inference approaches for fault diagnosis in sensor networks. However, the work focuses on the automated fault diagnosis in open air WSN and has no applicability in underwater sensor network systems.

A media access control protocol for healthcare sensor networks

The healthcare sensor network is one of the emerging areas of research for both computer science and health professionals. One of the key challenges in this network is that the traffic in emergency situation requires certain quality of service (QoS) parameters. The medium access control (MAC) layer plays a vital role for reliable and on-time data delivery in the healthcare sensor network. In this paper, we propose a new HealthCare MAC (HC-MAC) protocol for guaranteeing QoS to the emergency traffic without degrading the performance of the network for other traffics. In HC-MAC, the node that has the data request to reserve the time slot. The proposed protocol prioritizes the nodes according to the type of data they want to transmit. This always guarantees the successful and on-time delivery of the emergency traffic. Furthermore, the protocol also uses a multihop technique to increase the throughput and timeliness of other traffics.