Vijay Ram Ghorpade

Bio: Vijay Ram Ghorpade is an academic researcher from Bharati Vidyapeeth's College of Engineering. The author has contributed to research in topics: Grid & Mobile device. The author has an hindex of 1, co-authored 4 publications receiving 3 citations.

Application Checkpointing Technique for Self-Healing From Failures in Mobile Grid Computing

Abstract: A mobile grid (MG) consists of interconnected mobile devices which are used for high performance computing. Fault tolerance is an important property of mobile computational grid systems for achieving superior arrangement reliability and faster recovery from failures. Since the failure of the resources affects task execution fatally, fault tolerance service is essential to achieve QoS requirement in MG. The faults which occur in MG are link failure, node failure, task failure, limited bandwidth etc. Detecting these failures can help in better utilisation of the resources and timely notification to the user in a MG environment. These failures result in loss of computational results and data. Many algorithms or techniques were proposed for failure handling in traditional grids. The authors propose a checkpointing based failure handling technique which will improve arrangement reliability and failure recovery time for the MG network. Experimentation was conducted by creating a grid of ubiquitously available Android-based mobile phones.

Node Mobility Prediction in Mobile Grid Computing

Abstract: Mobile grid comprises of mobile nodes. A node can be a part of the network only if it continues to be connected to the grid. If we can manage to predict the next location of a node, we can determine if a task can be assigned to it for computing without its failure due to disconnection.The prediction of nodemobility is crucial for communication and enhancing the lifetime of the network. The Proposed technique that uses mobility prediction algorithm to investigate them ovement of thenodes.The proposed LocPsystem periodically collects location data with optimalenergy consumptionand predictsthefuture location.The system was deployed on Android mobile for data collection and prediction. Our experimental results provide a modus operandi for energy efficient location monitoring and prediction.

Efficient resource allocation scheme for on-the-fly computing based mobile grids

Abstract: Mobile grid (MG) is emergi ng as a new computing paradigm due to the ubiquitous availability of mobile devices. With the advancement in the capability of these devices, computationally intensive tasks can be executed using a peer-to-peer grid of such devices. MG can provide an edifice to execute parallel computationally intensive tasks. Key challenges that crop up while computing on a MG are resource constrained environment, inefficient resource allocation, high failure probability, etc. As a result, selection of appropriate nodes for task execution becomes critical for successful execution of the application. In this paper, we propose an efficient resource allocation model (ERAM) which provides resource allocation with failure handling. We created a MG comprising of Wi-Fi Direct connected Android smartphones. Different scenarios are considered for the purpose of experimentation. Our approach performs well with respect to application completion time, % battery consumption and recovery time from failure in comparison with existing techniques.

A trust management model based on NSGA-II in mobile grid system

Abstract: Mobile Grid network connects large number of mobile devices like smartphones, tablets, PDAs, wireless digital medical equipment’s etc for the purpose of sharing their resources and performing the task collaboratively and cooperatively. The mobile nodes participating in the mobile grid are autonomous and open in nature making them more vulnerable to data and control attacks made by malicious or selfish nodes. Preventing these malicious or selfish nodes and identifying the trusted nodes to participate in the network is an NP-hard problem. To recognize trusted nodes in the mobile grid system a novel trust management model is proposed in this paper by applying an elitist multi objective optimization algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed trust management model assesses the trust index of each mobile node in the network using various evaluation factors or attributes and then obtains the non-dominated set of trusted nodes in each front. Comparative analysis of the proposed trust model shows that the proposed model can be a potential candidate for implementing trust management in mobile grid network.

Application Checkpointing Technique for Self-Healing From Failures in Mobile Grid Computing

Abstract: A mobile grid (MG) consists of interconnected mobile devices which are used for high performance computing. Fault tolerance is an important property of mobile computational grid systems for achieving superior arrangement reliability and faster recovery from failures. Since the failure of the resources affects task execution fatally, fault tolerance service is essential to achieve QoS requirement in MG. The faults which occur in MG are link failure, node failure, task failure, limited bandwidth etc. Detecting these failures can help in better utilisation of the resources and timely notification to the user in a MG environment. These failures result in loss of computational results and data. Many algorithms or techniques were proposed for failure handling in traditional grids. The authors propose a checkpointing based failure handling technique which will improve arrangement reliability and failure recovery time for the MG network. Experimentation was conducted by creating a grid of ubiquitously available Android-based mobile phones.

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Abstract: In cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

A Thematic Analysis of the Articles on the Internet of Things in the Web of Science With HAC Approach

Abstract: This research was carried out using the bibliometric method to thematically analyze the articles on IoT in the Web of Science with Hierarchical Agglomerative Clustering approach. First, the descriptors of the related articles published from 2002 to 2016 were extracted from WoS, by conducting a keyword search using the “Internet of Things” keyword. Data analysis and clustering were carried out in SPSS, UCINET, and PreMap. The analysis results revealed that the scientific literature published on IoT during the period had grown exponentially, with an approximately 48% growth rate in the last two years of the study period (i.e. 2015 and 2016). After analyzing the themes of the documents, the resulting concepts were classified into twelve clusters. The twelve main clusters included: Privacy and Security, Authentication and Identification, Computing, Standards and Protocols, IoT as a component, Big Data, Architecture, Applied New Techniques in IoT, Application, Connection and Communication Tools, Wireless Network Protocols, and Wireless Sensor Networks.