Energy efficient model for the sensor cloud systems

Das, Kalyan; Das, Satyabrata; Darji, Rabi Kumar; Mishra, Ananya

doi:10.1109/rteict.2017.8256619

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…A low-power, energy-efficient communication protocol is suggested in the publication [27], The presented protocol optimizes the manner in which environmental data is obtained, packed, and transferred over vast distances with minimal energy use. The model suggested in [28] combines the prediction technique in the cloud system with a load balancing routing approach in a sensor network to reduce energy usage. In reference to flow scheduling between edge and cloud devices, they present in [29], a software defined networking (SDN)-based edge-cloud interaction.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Traffic in Cloud-Based IoT

Al-Kadhim,

Al-Raweshidy

2023

IEEE Sensors J.

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IoT is being increasingly used to enable continuous monitoring and sensing of physical things in the world. Energy efficiency is a critical aspect in its design and deployment, as IoT devices are usually battery-powered, and it is difficult, expensive, or even dangerous to replace the batteries in many real physical environments. In this paper, an energy-efficient cloud-based IoT network model has been created by optimizing sensor selection, selecting the least number of hops, and leveraging fading subchannel gain to reduce traffic power and cancel interference. Using the MILP, the optimization model and results are determined. The model assesses the outcomes of two possible scenarios: First, network optimization for energy efficiency based on the least number of hops, followed by a comparison with the second scenario. Second, energy-efficient network optimization by minimizing hops and selecting sub-channels. The results indicate that the first scenario consumes more network traffic power in IoT devices, whereas the second scenario reduces network traffic power by an average of 27 percent.

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Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Traffic in Cloud-Based IoT

Al-Kadhim,

Al-Raweshidy

2023

IEEE Sensors J.

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“…Wang et al [26] conducted research on data credibility through fog. Das et al [27] constructed a sensor cloud energy-saving system. Zhu et al [28] carried out research on data protection under the condition of big data.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Ren¹,

Qiu²,

Cheng³

2023

Computer Modeling in Engineering &Amp; Sciences

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Star sensors are an important means of autonomous navigation and access to space information for satellites. They have been widely deployed in the aerospace field. To satisfy the requirements for high resolution, timeliness, and confidentiality of star images, we propose an edge computing algorithm based on the star sensor cloud. Multiple sensors cooperate with each other to form a sensor cloud, which in turn extends the performance of a single sensor. The research on the data obtained by the star sensor has very important research and application values. First, a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition, which can reduce the amount of data computation. Then, a mapping model between content and space is constructed to achieve low-rank image representation and efficient computation. Finally, the data collected by the wireless sensor is delivered to the edge server, and a different method is used to achieve privacy protection. Only a small amount of core data is stored in edge servers and local servers, and other data is transmitted to the cloud. Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage, and has strong privacy.

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“…The results obtained were quite improved compared to the earlier works and was at par with the existing prediction methods. Das et al [11,12] proposed the data prediction based energyefficient sensor cloud models in which the prediction method is used in the cloud system to save energy consumption for the sensors. Hamouda and Msallam [13] proposed the selection of variable sampling intervals for the monitoring of the parameters used in agriculture activities, which is energy efficient using WSNs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

Das¹,

Das²,

Mohapatra³

2020

Karbala International Journal of Modern Science

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An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the combination of prediction and forecasting methods in the cloud system, which results in less communication and more battery life for the sensor. In our model, we used the ANN model initially, which had predicted the temperature for a given day with an accuracy of 92%. The results of ANN, together with the earlier real temperatures, are given as input to the ARIMA forecasting model, which provides an accuracy of 96% for one day in advance. Our simulation shows that the proposed method saves more energy compared to the traditional approach.

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Energy efficient model for the sensor cloud systems

Cited by 7 publications

References 12 publications

Energy-Efficient Traffic in Cloud-Based IoT

Energy-Efficient Traffic in Cloud-Based IoT

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

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