ORCID Profile
0000-0002-3876-3703
Current Organisation
Federation University Australia
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Publisher: IEEE
Date: 09-2014
Publisher: Springer Science and Business Media LLC
Date: 03-10-2022
Publisher: IEEE
Date: 09-2014
Publisher: Research Square Platform LLC
Date: 26-08-2021
DOI: 10.21203/RS.3.RS-615784/V1
Abstract: Increase in mobile nodes has brought new challenges to IoT’s routing protocol-RPL. Mobile nodes (MN) bring new possibilities as well as challenges to the network. MN creates frequent route disruption, energy loss and increases end-to-end delay in the network. This could be solved by improving RPL to react faster to route failures through route prediction, while keeping energy expenditure for this process in reasonable limits. In this context a new Mobility Energy and Queue Aware-RPL (MEQA-RPL) is proposed that have the capability to sense route failure and to identify proactively the next possible route before the current route fails. While identifying the next route, MEQA-RPL employs constraint check on energy and queue availability to guarantee QoS for MN and better lifetime for the network. When compared to RPL with mobility support our model reduce average signaling cost by 31%, handover delay by 32% and improve packet delivery ratio by 17%. We run simulations with multiple mobile nodes which have also shown promising results on aforementioned parameters.
Publisher: Elsevier BV
Date: 2015
Publisher: Hindawi Limited
Date: 17-02-2023
DOI: 10.1155/2023/7754765
Abstract: On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.
Publisher: Wiley
Date: 03-10-2022
DOI: 10.1002/ETT.4652
Abstract: Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T‐CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T‐CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T‐CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets: (i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.64%, 32.95%, 12.05%, 32.65%, 26.54% high accuracy, and 27.4%, 26%, 23.7%, 34.7%, 36.5% lower energy consumption for validating NASA dataset, similarly 20.75%, 19.06%, 29.09%, 23.8%, 20.5% high accuracy, 20.84%, 18.03%, 28.64%, 30.72%, 33.74% lower energy consumption for validating Saskatchewan HTTP traces dataset than the existing approaches, like auto adaptive differential evolution algorithm BiPhase adaptive learning‐based neural network, error preventive score in time series forecasting models, time series forecasting methods for cloud data workload prediction, and self‐directed workload forecasting method.
Publisher: Bentham Science Publishers Ltd.
Date: 03-06-2020
DOI: 10.2174/2213275912666190807121149
Abstract: Internet of Things will be inevitable in all walks of our life, where it becomes necessary for all smart devices to have end-to-end data transfer capability. These low power and low-cost end devices need to be enabled with IPv6 address and corresponding routing mechanism for participating in Internet of Things environment. To enable fast and efficient routing in Internet of Things network and constrained with limited energy, Routing Protocol for Low-power Lossy Network (RPL) has been developed by ROLL-Work Group. As RPL is proactive and energyconserving, it has become the most promising routing protocol for Internet of Things. Nodes in Low-power Lossy Network (LLN) are designed to conserve energy by maintaining radio silence over 90% of its lifetime. It is possible to further improve the node’s lifetime and thereby considerably extending network’s longevity by performing sensible routing. Different routing structure in Internet of Things network can be attained by carefully crafting the objective function for the same set of nodes which satisfies different goals. This paper focuses on different objective functions in RPL which have been developed over time with the emphasis on energy conservation and maximizing the lifetime of the network. In this work, we have carefully studied different metric compositions used for creating an objective function. The study revealed that combining metrics provides better results in terms of energy conservation when compared to single metric defined as part of RPL standard. It was also noted that considering some metric as a constraint can increase the rate of route convergence without affecting the performance of the network.
Publisher: Emerald
Date: 20-09-2022
DOI: 10.1108/IJPCC-05-2022-0213
Abstract: Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay. This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator s ling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL. This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs. In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.
Publisher: Inderscience Publishers
Date: 2022
Location: Oman
No related grants have been discovered for Robin Cyriac.