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 Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

2022 ◽  
Author(s):  
Liping Qian
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Lagrangian Multiplier ◽  
Total Energy Consumption ◽  
Computing Services ◽  
Lagrangian Multiplier Method

<div>The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. With the unprecedented increase of computation-intensive yet latency sensitive marine mobile Internet services, mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been envisioned as promising approaches to providing with the low-latency as well as reliable computing services and ultra-dense connectivity. In this paper, we investigate the energy consumption minimization based energy-efficient MEC via cooperative NOMA for the UAV-assisted M-IoT networks. We consider that USVs offload their computation-workload to the UAV equipped with the edge-computing server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of Deep Reinforcement Learning (DRL), and the underlying algorithm is proposed to optimize the underlying multi-domain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.</div>

Unsupervised Deep Learning for Binary Offloading in Mobile Edge Computation Network

2021 ◽  
Author(s):  
Xue Chen ◽  
Hongbo Xu ◽  
Guoping Zhang ◽  
Yun Chen ◽  
Ruijie Li
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Energy Consumption ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Low Complexity ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Unsupervised Deep Learning ◽  
Execution Delay

Abstract Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on mobile device (MD). The resource allocation of MEC is an optimization problem, however, the existing large amount of computation may hinder its practical application. In this work, we propose a multiuser MEC framework based on unsupervised deep learning (DL) to reduce energy consumption and computation by offloading tasks to edge servers. The binary offloading decision and resource allocation are jointly optimized to minimize energy consumption of MDs under latency constraint and transmit power constraint. This joint optimization problem is a mixed integer nonconvex problem which result in the gradient vanishing problem in backpropagation. To address this, we propose a novel binary computation offloading scheme (BCOS), in which a deep neural network (DNN) with an auxiliary network is designed. By using the auxiliary network as a teacher network, the student network can obtain the lossless gradient information in joint training phase. As a result, the sub-optimal solution of the optimization problem can be acquired by the learning-based BCOS. Simulation results demonstrate that the BCOS is effective to solve the binary offloading problem by the trained network with low complexity.

scholarly journals Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

2022 ◽  
Author(s):  
Liping Qian
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Lagrangian Multiplier ◽  
Total Energy Consumption ◽  
Computing Services ◽  
Lagrangian Multiplier Method

<div>The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. With the unprecedented increase of computation-intensive yet latency sensitive marine mobile Internet services, mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been envisioned as promising approaches to providing with the low-latency as well as reliable computing services and ultra-dense connectivity. In this paper, we investigate the energy consumption minimization based energy-efficient MEC via cooperative NOMA for the UAV-assisted M-IoT networks. We consider that USVs offload their computation-workload to the UAV equipped with the edge-computing server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of Deep Reinforcement Learning (DRL), and the underlying algorithm is proposed to optimize the underlying multi-domain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.</div>

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

scholarly journals Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Wenxin Li ◽  
Qiyuan Peng ◽  
Chao Wen ◽  
Shengdong Li ◽  
Xu Yan ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Saving ◽  
Urban Rail Transit ◽  
Total Energy Consumption ◽  
Transit System ◽  
Running Time ◽  
Urban Rail

Optimizing to increase the utilization ratio of regenerative braking energy reduces energy consumption, and can be done without increasing the deviation of train running time in one circle. The latter entails that the train timetable is upheld, which guarantees that the demand for passenger transport services is met and the quality of services in the urban rail transit system is maintained. This study proposes a multi-objective optimization model for urban railways with timetable optimization to minimize the total energy consumption of trains while maximizing the quality of service. To this end, we apply the principles and ideas of calculus to reduce the power of the velocity in the train energy consumption model. This greatly simplifies the complexity of the optimization model. Then, considering the conflicting requirements of decision-makers, weight factors are added to the objective functions to reflect decision-makers’ preferences for energy-saving and the quality of service. We adopt the nondominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model. A practical case study of the Yizhuang urban railway line in Beijing is conducted to verify the effectiveness of the proposed model and evaluate the advantages of the optimal energy saving timetable (OEST) in comparison to the optimal quality of service timetable (OQOST). The results showed that the OEST reduced total energy consumption by 8.72% but increased the deviation of trains running time in one circle by 728 s. The total energy consumption was reduced by 6.09%, but there was no increase in the deviation of train running time in one circle with the OQOST.

Analysis of Electric Energy Consumption Patterns: A Case Study of a Real Life Office Building

2013 ◽  
Vol 330 ◽  
pp. 158-162 ◽  
Author(s):  
Jin Soo Han ◽  
Youn Kwae Jeong ◽  
Il Woo Lee
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Real Life ◽  
Electric Energy ◽  
Consumption Patterns ◽  
Office Building ◽  
Commercial Building ◽  
Total Energy Consumption ◽  
Electric Energy Consumption ◽  
Energy Consumption Patterns

Electric energy consumption shares a great portion of commercial building energy. Electric energy saving is essential to reduce total energy consumption in commercial buildings. To draw energy saving methods, it is necessary to monitor real energy consumption patterns and analyze the results. We monitor the lighting and non-lighting energy consumption of eleven zones in a real working office building every fifteen minutes during eleven months. We observe and analyze the monthly and daily energy consumption patterns of all zones and draw several feasible energy saving methods. Moreover, the lighting and occupancy are monitored simultaneously in detail to investigate the unnecessary energy consumption. It shows the possibility of a great amount of energy saving. Because we analyze the energy consumption patterns in all zones, the drawn energy saving methods are applicable to the current building with some added infrastructure and expandable to other similar office buildings. Our result is expected to contribute to reducing the energy consumption in buildings.

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.

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