scholarly journals A Survey on Content Adaptation Systems towards Energy Consumption Awareness

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mohd Norasri Ismail ◽  
Rosziati Ibrahim ◽  
Mohd Farhan Md Fudzee
Keyword(s):  
Energy Consumption ◽  
Research Work ◽  
Content Adaptation ◽  
Wireless Multimedia ◽  
Mobile Tv ◽  
Energy Aware ◽  
Heterogeneous Devices ◽  
Digital Contents ◽  
Multimedia Content Adaptation ◽  
Rich Content

The availability of heterogeneous devices has rapidly changed the way people access the World Wide Web that includes rich content applications such as video streaming, 3D games, video conferencing, and mobile TV. However, most of these devices' (i.e., mobile phone, PDA, smartphone, and tablet) capabilities differ in terms of built-in software and library (what they can display), display size (how the content appears), and battery supply (how long the content can be displayed). In order for the digital contents to fit the target device, content adaptation is required. There have been many projects focused on energy-aware-based content adaptation that have been designed with different goals and approaches. This paper reviews some of the representative content adaptation solutions that have been proposed during the last few years, in relation to energy consumption focusing on wireless multimedia streaming in mobile devices. Also, this paper categorizes the research work according to different classifications of multimedia content adaptation requirements. In addition, we discuss some energy-related challenges content adaptation systems.

Energy Aware Routing Strategies for an Evolving Wireless Sensor Network

2017 ◽  
pp. 196-236 ◽  
Author(s):  
Solmaz Salehian ◽  
Shamala K. Subraminiam ◽  
Rozita Salehian
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wide Spectrum ◽  
Critical Issue ◽  
Wireless Sensor ◽  
Wireless Multimedia ◽  
Energy Aware ◽  
Design Factors

A critical issue in Wireless Sensor Networks is optimization of energy consumption. The development and deployment of various paradigms of algorithms addressing these optimization needs has formed the underlying design factors of solutions. Strong correlation exists between physical discoveries making sensors indeed cross the boundaries. This chapter presents a detail review and analysis of substantial algorithms which have structured the elevation of Wireless Sensor Networks and its realization. This encompasses the graceful migration from conventional WSN, Wireless Multimedia Sensor Network to the inventive WSN-Internet Protocol (WSN-IP). An enhanced taxonomy synthesizing these algorithms is presented to complement the identified trend transition in WSN. Each algorithm is reviewed and detail comparatives are deliberated. This chapter also presents a wide spectrum of open issues in the development of WSN algorithms for changing sensor architecture.

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Energetic and exergetic analysis of a convective drier: A case study of potato drying process

2020 ◽  
Vol 5 (1) ◽  
pp. 563-572
Author(s):  
Iman Golpour ◽  
Mohammad Kaveh ◽  
Reza Amiri Chayjan ◽  
Raquel P. F. Guiné
Keyword(s):  
Energy Consumption ◽  
Research Work ◽  
Specific Energy Consumption ◽  
Exergy Efficiency ◽  
Operating Conditions ◽  
Energy Utilization ◽  
Convective Drying ◽  
Drying Time ◽  
Energy And Exergy ◽  
Exergy Losses

AbstractThis research work focused on the evaluation of energy and exergy in the convective drying of potato slices. Experiments were conducted at four air temperatures (40, 50, 60 and 70°C) and three air velocities (0.5, 1.0 and 1.5 m/s) in a convective dryer, with circulating heated air. Freshly harvested potatoes with initial moisture content (MC) of 79.9% wet basis were used in this research. The influence of temperature and air velocity was investigated in terms of energy and exergy (energy utilization [EU], energy utilization ratio [EUR], exergy losses and exergy efficiency). The calculations for energy and exergy were based on the first and second laws of thermodynamics. Results indicated that EU, EUR and exergy losses decreased along drying time, while exergy efficiency increased. The specific energy consumption (SEC) varied from 1.94 × 105 to 3.14 × 105 kJ/kg. The exergy loss varied in the range of 0.006 to 0.036 kJ/s and the maximum exergy efficiency obtained was 85.85% at 70°C and 0.5 m/s, while minimum exergy efficiency was 57.07% at 40°C and 1.5 m/s. Moreover, the values of exergetic improvement potential (IP) rate changed between 0.0016 and 0.0046 kJ/s and the highest value occurred for drying at 70°C and 1.5 m/s, whereas the lowest value was for 70°C and 0.5 m/s. As a result, this knowledge will allow the optimization of convective dryers, when operating for the drying of this food product or others, as well as choosing the most appropriate operating conditions that cause the reduction of energy consumption, irreversibilities and losses in the industrial convective drying processes.

scholarly journals Energy-aware very fast decision tree

2021 ◽  
Author(s):  
Eva García-Martín ◽  
Niklas Lavesson ◽  
Håkan Grahn ◽  
Emiliano Casalicchio ◽  
Veselka Boeva
Keyword(s):  
Energy Consumption ◽  
Decision Tree ◽  
Concept Drift ◽  
Algorithm Design ◽  
Energy Aware ◽  
Battery Capacity ◽  
Very Fast Decision Tree ◽  
Public Datasets ◽  
Additional Constraints ◽  
Fast Decision

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

scholarly journals Energy management in sensor networks

2012 ◽  
Vol 370 (1958) ◽  
pp. 52-67 ◽  
Author(s):  
John A. Stankovic ◽  
Tian He
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Management ◽  
Large Scale ◽  
Energy Savings ◽  
Time Synchronization ◽  
Media Access Control ◽  
Media Access ◽  
Energy Aware ◽  
Holistic View

This paper presents a holistic view of energy management in sensor networks. We first discuss hardware designs that support the life cycle of energy, namely: (i) energy harvesting, (ii) energy storage and (iii) energy consumption and control. Then, we discuss individual software designs that manage energy consumption in sensor networks. These energy-aware designs include media access control, routing, localization and time-synchronization. At the end of this paper, we present a case study of the VigilNet system to explain how to integrate various types of energy management techniques to achieve collaborative energy savings in a large-scale deployed military surveillance system.

Performance study and critical review on energy aware routing protocols in mobile sink based WSNs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Aparna Ashok Kamble ◽  
Balaji Madhavrao Patil
Keyword(s):  
Wireless Networks ◽  
Energy Consumption ◽  
Routing Protocols ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Optimization Approach ◽  
Performance Study ◽  
Energy Aware ◽  
Energy Aware Routing

Abstract Wireless networks involve spatially extended independent sensor nodes, and it is associated with each other’s to preserve and identify physical and environmental conditions of the particular application. The sensor nodes batteries are equipped with restricted energy for working with an energy source. Consequently, efficient energy consumption is themain important challenge in wireless networks, and it is outfitted witharestricted power storage capacity battery. Therefore, routing protocol with energy efficiency is essential in wireless sensor network (WSN) to offer data transmission and connectivity with less energy consumption. As a result, the routing scheme is the main factor for decreasing energy consumption and the network's lifetime. The energy-aware routing model is mainly devised for WSN with high network performance when transmitting data to a sink node. Hence, in this paper, the effectiveness of energy-aware routing protocols in mobile sink-based WSNs is analyzed and justified. Some energy-aware routing systems in mobile sink-based WSN techniques, such as optimizing low-energy adaptive clustering hierarchy (LEACH) clustering approach, hybrid model using fuzzy logic, and mobile sink. The fuzzy TOPSIS-based cluster head selection (CHS) technique, mobile sink-based energy-efficient CHS model, and hybrid Harris Hawk-Salp Swarm (HH-SS) optimization approach are taken for the simulation process. Additionally, the analytical study is executed using various conditions, like simulation, cluster size, nodes, mobile sink speed, and rounds. Moreover, the performance of existing methods is evaluated using various parameters, namely alive node, residual energy, delay, and packet delivery ratio (PDR).

Numerical Study on Flow Characteristics in High Multi-Stage Pressure Reducing Valve

2017 ◽  
Author(s):  
Fu-qiang Chen ◽  
Zhi-xin Gao ◽  
Jin-yuan Qian ◽  
Zhi-jiang Jin
Keyword(s):  
Energy Consumption ◽  
Numerical Study ◽  
Research Work ◽  
Flow Characteristics ◽  
Temperature Fields ◽  
Turbulent Dissipation ◽  
Technological Support ◽  
Multi Stage ◽  
Valve Opening ◽  
Pressure Reducing Valve

In this paper, a new high multi-stage pressure reducing valve (HMSPRV) is proposed. The main advantages include reducing noise and vibration, reducing energy consumption and dealing with complex conditions. As a new high pressure reducing valve, its flow characteristics need to be investigated. For that the valve opening has a great effect on steam flow, pressure reduction and energy consumption, thus different valve openings are taken as the research points to investigate the flow characteristics. The analysis is conducted from four aspects: pressure, velocity, temperature fields and energy consumption. The results show that valve opening has a great effect on flow characteristics. No matter for pressure, velocity or temperature field, the changing gradient mainly reflects at those throttling components for all valve openings. For energy consumption, in the study of turbulent dissipation rate, it can be found that the larger of valve opening, the larger of energy consumption. It can be concluded that the new high multi-stage pressure reducing valve works well under complex conditions. This study can provide technological support for achieving pressure regulation, and benefit the further research work on energy saving and multi-stage design of pressure reducing devices.

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