C. R. Tripathy

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 improved load-balancing mechanism based on deadline failure recovery on GridSim

Abstract Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7xa0% better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets,xa0it gives 11xa0% better performance than EGDC.

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.

Modeling of Task Scheduling Algorithm Using Petri-Net in Cloud Computing

Task scheduling is an NP-hard problem. In earlier, different task scheduling algorithms are proposed for cloud computing environment. By adapting new and modified task scheduling algorithms, better resource utilization can be obtained. In real time, most of the tasks are deadline-based tasks. The deadline-based task has different parameters. The backfilling algorithm is used to schedule these types of tasks in Haizea. In this paper, we modeled the existing backfilling algorithm for scheduling deadline-based task using Petri-Net. The paper presents the design model of the existing backfilling algorithm. The model specifies real-time challenges of backfilling algorithm using Petri-Net. The work also comes forward with some design issues of backfilling algorithm using Petri-Net.

Task Scheduling Mechanism Using Multi-criteria Decision-making Technique, MACBETH in Cloud Computing

Cloud computing is the new era of Internet technology which provides various utilities and computing resources from the pool of resources on the basis of “pay per Use”. It is challenging one to allocate required on-demand resources for all the users’ request. Meanwhile, the service provider aims toward a better resource utilization. These user requests are called task, if task execution is bounded by time limit which is called deadline-based task. The deadline-based tasks have different parameters. To schedule these tasks, researchers proposed many works based upon these parameters. However, in this work, we considered the scheduling of deadline-based task that is a Multi-criteria Decision-making problem due to different task’s parameters associated with it. The work is proposed to implement Measuring Attractiveness through a Category-Based Evaluation Technique (MACBETH) to ranking the deadline-based task by which many tasks can meet their deadline. The results of the proposed work are quite good as compared to the existing mechanisms.

An Efficient Load Balancing Mechanism with Cost Estimation on GridSim

Due to the rapid technological advancements, the Grid computing has emerged as a new field, distinguished from conventional distributed computing. The load balancing is considered to be very important in Grid systems. In this paper, we propose a new dynamic and distributed load balancing method called Enhanced GridSim with Load Balancing based on Cost Estimation (EGCE) for computational Grid with heterogeneous resources. The proposed algorithm EGCE is an improved version of the existing EGDC in which we perform load balancing by estimating the expected finish time of a job on resources on each job arrival and then balance the load by migrating jobs to resources by taking into account the resource heterogeneity and network heterogeneity. We simulate the proposed algorithm on the GridSim platform. From the results, our algorithm is shown to be quite efficient in minimizing the average response time.

Truthful Resource Allocation Detection Mechanism for Cloud Computing

Resource allocation is a n-p hard problem. In spite of different proposed algorithms and methods, still it is a challenging one. VMs are basically considered as a resource in cloud computing. In his paper, we are considering the resource allocation in Haizea model which is an open-source VM-based lease management architecture and acts as a resource manager. Different scheduling mechanisms are used in Haizea like Advance Reservation (AR), Best effort (BE), Immediate (IM) and Deadline Sensitive (DS). Except these scheduling swapping and backfilling algorithm is proposed. But Backfilling is not applicable for all types of situation. In this work we tried to make a truthful scheduling by our proposed mechanism. The proposed mechanism also identifies where the lease can be scheduled or not. The mechanism for allocation detection is simple and robust.