Adaptive packets scheduling method for energy harvesting relay with receiver energy consumption

2015 ◽  
Author(s):  
Zhaojie Sun ◽  
Lilin Dan ◽  
Yue Xiao ◽  
Peibo Wen ◽  
Shaoqian Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Harvesting ◽  
Scheduling Method

scholarly journals Energy consumption index minimized resource allocation in hybrid energy multiuser OFDM system with distributed antennas

2018 ◽  
Vol 17 ◽  
pp. 03015
Author(s):  
Huanhuan MAO ◽  
Pengcheng Zhu ◽  
Jiamin Li
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Energy Harvesting ◽  
Power Allocation ◽  
Base Station ◽  
Mixed Integer ◽  
Subcarrier Allocation ◽  
Ofdm System ◽  
Consumption Index ◽  
Distributed Antennas

Energy harvesting is one of the promising option for realization of green communication and has been a growing concern recently. In this paper, we address the downlink resource allocation in OFDM system with distributed antennas with hybrid power supply base station, where energy harvesting and non-renewable power sources are used complementarily. A joint subcarrier and power allocation problem is formulated for minimizing the net Energy Consumption Index (ECI) with system Quality of Service (QoS) and bit error rates constraint. The problem is a 0-1 mixed integer nonlinear programming problem due to the binary subcarrier allocation variable. To solve the problem, we design an algorithm based on Lagrange relaxation method and fraction programming which optimizes the power allocation and subcarrier allocation iteratively in two nests. Simulation results show that the proposed algorithm converges in a small number of iterations and can improve net ECI of system greatly.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

scholarly journals Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yawen Zhang ◽  
Yifeng Miao ◽  
Shujia Pan ◽  
Siguang Chen
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Energy Harvesting ◽  
Convergence Rate ◽  
Energy Saving ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Computation Offloading ◽  
Total Energy Consumption ◽  
Task Processing

In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm.

scholarly journals On the Energy Efficiency in the Next Generation of Smart Buildings—Supporting Technologies and Techniques

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4399 ◽  
Author(s):  
César Benavente-Peces
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Harvesting ◽  
Smart Grids ◽  
Smart Cities ◽  
Clean Energy ◽  
Communication Technologies ◽  
Next Generation ◽  
Smart Buildings ◽  
The Impact

Energy efficiency is one of the most relevant issues that the scientific community, and society in general, must face in the next years. Furthermore, higher energy efficiencies will contribute to worldwide sustainability. Buildings are responsible for 40% of the overall consumed energy. Smart Grids and Smart Buildings are playing an essential role in the definition of the next generation of sustainable Smart Cities. The main goal is reducing the impact of energy consumption on the environment as much as possible. This paper focuses on information communication technologies (ICTs) and techniques, their key characteristics and contribution to obtain higher energy efficiencies in smart buildings. Given that electrical energy is the most used, the investigation mainly centres on this energy. This paper also pays attention to green energies and energy harvesting due to their contribution to energy efficiency by providing additional clean energy. The main contribution of this investigation is pointing out the most relevant existing and emerging ICT technologies and techniques which can be used to optimize the energy efficiency of Smart Buildings. The research puts special attention on available, novel and emerging sensors, communication technologies and standards, intelligence techniques and algorithms, green energies and energy harvesting. All of them enable high-performance intelligent systems to optimize energy consumption and occupants’ comfort. Furthermore, it remarks on the most suitable technologies and techniques, their main features and their applications in Smart Buildings.

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

2019 ◽  
Vol 118 (4) ◽  
pp. 160
Author(s):  
G. Madhumita ◽  
G. Rajini ◽  
B. Subisha
Keyword(s):  
Energy Consumption ◽  
Energy Harvesting ◽  
Real Time ◽  
Scheduling Algorithm ◽  
Earliest Deadline First ◽  
Real Time Systems ◽  
Tasks Scheduling ◽  
Time Systems ◽  
Earliest Deadline ◽  
Reducing Energy

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life.  Energy is a parameter by which the lifetime of system can be enhanced.  To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.  

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