Sasmita Acharya

Inter-actor Connectivity Restoration in Wireless Sensor Actor Networks: An Overview

Wireless Sensor and Actor Networks (WSANs) consist of powerful actors and resource constraint sensors linked by wireless medium. In WSANs, the sensors gather information about the physical environment while the actors take decisions and perform appropriate actions depending on the sensed data. In some applications, actors must communicate with each other to make appropriate decisions and perform the coordinated actions. Maintaining inter-actor connectivity is extremely important in critical WSAN applications, where the actors need to quickly plan for optimal coordinated response to events detected by the sensors. The failure of a critical actor partitions the inter-actor network into disjoint segments and hinders the network operation. Under such circumstances, the network no longer becomes capable of giving a timely response to a serious event. So, recovery from an inter-actor connectivity failure is of utmost importance. This paper reviews different approaches for restoring the inter-actor connectivity in WSANs.

An ANN Approach for Fault Tolerant Wireless Sensor Networks

Wireless Sensor Network (WSN) is an emerging technology that has revolutionized the whole world. This paper proposes an artificial neural network model for a reliable and fault-tolerant WSN based on an exponential Bi-directional Associative Memory (eBAM). An eBAM has higher capacity for pattern pair storage than the conventional BAMs. The proposed model strives to improve the fault tolerance and reliability of packet delivery in WSN by transmitting small-sized packets called vectors. The vectors are associated with the original large-sized packets after encoding the associations between the hetero-associative vectors for the given problem space. This encoding is done by the application of the evolution equations of an eBAM. The performance characteristics of the proposed model are compared with other BAM models through simulation.

A Fuzzy Knowledge Based Sensor Node Appraisal Technique for Fault Tolerant Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have a wide range of applications in the real world. They consist of a large number of small, inexpensive, limited energy and low-cost sensor nodes which are deployed in vast geographical areas for remote sensing and monitoring operations. The sensor nodes are mostly deployed in harsh environments and unattended setups. They are prone to failure due to battery depletion, low-cost design or malfunctioning of some components. The paper proposes a two-level fuzzy knowledge based sensor node appraisal technique (NAT) in which the cluster head (CH) assesses the health status of each non-cluster head (NCH) node by the application of fuzzy rules and challenge-response technique. The CH then aggregates data from only the healthy NCHs and forwards it to the base station. It is a pro-active approach which prevents faulty data from reaching the base station. The simulation is carried out with injected NCH faults at a specified rate. The simulation results show that the proposed NAT technique can significantly improve the throughput, network lifetime and quality of service (QoS) provided by WSNs.

An Artificial Neural Network Model for a Diesel Engine Fuelled with Mahua Biodiesel

In this paper, an Artificial Neural Network (ANN) model is used to predict the different parameters of a diesel engine fuelled with the mixture of diesel and mahua biodiesel in different proportion. The data has been obtained from an experiment carried out in a twin cylinder diesel engine in different loading condition and different blending ratios of diesel and biodiesel. Two input data, i.e., engine load and blending ratio and five output data, i.e., Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), Smoke level, Carbon monoxide (CO), and Nitrogen Oxides (NOx) emissions have been considered for ANN modeling. The network used is back propagation, feed forward with multilayer perceptron having ten numbers of neurons in hidden layer with trainlm training algorithm being proposed. It has been observed that the prediction ability of the model is high as there is minimum difference between the predicted and the experimentally measured values.

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.

A Fuzzy Knowledge Based Mechanism for Secure Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of a number of limited energy sensor nodes deployed randomly over an area. The paper proposes a fuzzy knowledge based secure tree-based data aggregation mechanism for WSNs called the Fuzzy knowledge based Data Aggregation Scheme (FDAS). In the proposed FDAS mechanism, a combination of fuzzy rules is applied to predict the node status of each node in the aggregation tree. The faulty nodes are then isolated from the process of data aggregation. The proposed FDAS mechanism also ensures the security of the network by the application of the privacy homomorphic cryptography technique. It has been found to give better performance characteristics than the other existing algorithms as validated through the results obtained from simulation.