Internet of Things (IoT) is network transfer data without human to human and human to machine communication. This project based on IoT (Internet of Things) for Home Automation. Home Automation is a collection of sensor Controller by microcontroller. Using Microcontroller, we can send data into the cloud server then we can share information on Cloud server and mobile app. we use cloud server as Ubidots. The monitoring value from the home sensor like Gas, Flame, Sound and Temperature to the cloud server (ubidots) and app. Main motivation of this project is to alert message and mail to Smartphone on any abnormal condition. This project is designed to reduce data transmit, reduce time, cost effective and easy to use.

Viswanathan P

School of Computer Science and Engineering

Chennai Campus

Kesavan G

Sanjeevi P

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A 24 hour IoT framework for monitoring and managing home automation

2016 International Conference on Inventive Computation Technologies (ICICT)

The Internet of things concept is based on data from sensor devices. The conservation of sensor data in the cloud has become a well-known challenge with respect to the sensor-cloud network. There is an enormous quantity of sensed data, including missed and unimportant data due to unavailability of the node, low link quality, node deficiency or latency in path communication. In order to address this issue, it is necessary to develop an accurate sensor selection approach, precisely for the purpose of reducing the quantity of unimportant data (missed and noisy data). In this paper, we formulate an innovative and efficient bi-level node energy assessment algorithm, named BNSA, which uses a belief propagation protocol for optimal energy-efficient sensor selection. On the first level, the equilibrium transmission energy function is applied to all nodes to evaluate the transmission rate and link quality, together with the residual energy of the node, to predict the energy consumption, providing an accurate prediction of the energy level of the nodes. On the second level, optimal nodes are selected based on the prediction of the energy level. Belief propagation draws inferences for revealing missed sensor data. This approach improves the lifespan of the network and reduces the energy utilization rate by more than 32.3% with a delay rate of 77%. This is reflected in an enhancement of the quality of service with 85% of the energy consumption over the network, compared with existing models. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

Equilibrium Transmission Bi-level Energy Efficient Node Selection Approach for Internet of Things

The major issue threatening the cloud computing is that it consumes an enormous amount of energy for providing valuable computational services. Many workload consolidation attempts were taken to reduce the energy consumption of data centre. Broad research on all workload consolidation issues in the cloud environment is extremely challenging since it entails developers to deliberate network setup and the cloud environment, which could be beyond the control. Moreover, the consolidation of workload cannot be predicted or restrained. Though, existing literature lacks the crucial agility for workload consolidation to reduce energy in cloud data centre, producing poor responsiveness and inadequate scalability. In this paper, we present the background and motivation of workload consolidation techniques in the cloud. Moreover, we acquaintance four recent workload consolidation algorithms considering metrics for reducing energy and the features of the best workload consolidation algorithm is highlighted. Finally, research areas and challenges are boasted based on the strengths and weakness of prevailing workload consolidation methods. Copyright © 2017 Inderscience Enterprises Ltd.

International Journal of Internet Protocol Technology

Workload consolidation techniques to optimise energy in cloud: review

The cloud data center is accommodated with many servers for cloud-based services which cause more consumption of energy and menace cost factors in computing tasks. Many existing scheduling techniques hinge on allocating task where scheduling algorithm is not based on assigning tasks through urgent and non-urgent task scheduling using dynamic voltage frequency scaling (DVFS) controller. In demand to reduce energy consumption and to maintain the quality of services, this paper proposes non-urgent and urgent task scheduling (NUTS) algorithm using DVFS, to restraint and scheduling of task in the more efficient way for minimizing the power consumption of the IT equipment. To increase the energy efficiency, we proposed scheduling queue and non-completed task queue for scheduling urgent, non-urgent and non-completed tasks to ally utilization of resources efficiently and to decrease the consumption of energy in the data center. In this paper, we compared proposed algorithm with two existing standard scheduling algorithms. The experimental results boast that NUTS algorithm performs better than the existing algorithms and can centrist energy efficiency in cloud data center. © 2017, Springer-Verlag Wien.

Journal	Data powered by Typeset2016 International Conference on Inventive Computation Technologies (ICICT)
Publisher	Data powered by TypesetIEEE
Open Access	No