research-article

Optimal Rate Control for Energy-Harvesting Systems with Random Data and Energy Arrivals

Authors:

Xinbing WangAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 1

Article No.: 13, Pages 1 - 30

https://doi.org/10.1145/3293535

Published: 10 February 2019 Publication History

Abstract

Due to the random and dynamic energy-harvesting process, it is challenging to conduct optimal rate control in Energy-Harvesting Communication Systems (EHCSs). Existing works mainly focus on two cases: (1) the traffic load is infinite (as long as there is energy, there is data to transmit), in which the objective is to optimize the rate control policy subject to the dynamic energy arrivals, thus maximizing the average system throughput; and (2) the traffic load is finite, in which the objective is to optimize the rate control policy, thus minimizing the time by which all packets are delivered. In this work, we focus on the optimal rate control of EHCSs from another important and practical perspective, where the data and energy arrivals are both random. Given any deadline of T, our goal is to maximize the total throughput in [0,T]. Specifically, two scenarios are considered: (1) energy is ready before the transmission; and (2) energy arrives randomly during the transmission. In both scenarios, we assume that the data arrive randomly during the transmission. For the first scenario, we develop a novel Stepwise Searching Algorithm (SSA) based on the cumulative curve methodology, which is shown to achieve the optimal solution and the complexity grows only linearly with the problem size. In addition, the SSA can provide a simple and appealing graphical visualization of approximating the optimal solution. For the second scenario, we provide a simplified case study that can be solved by the SSA with low computation overhead and demonstrate the difficulties in solving the general setting, which initiates a first step toward the full understanding of the scenario when energy arrives randomly during the transmission.

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Cited By

Feng YZhang XJia RLin FLu JZheng ZLi M(2023)Intelligent Trajectory Design for Mobile Energy Harvesting and Data TransmissionIEEE Internet of Things Journal10.1109/JIOT.2022.320225210:1(403-416)Online publication date: 1-Jan-2023
https://doi.org/10.1109/JIOT.2022.3202252
Ma DWang DHuang XHu YMa L(2023)KEFSAR: A Solar-Aware Routing Strategy For Rechargeable IoT Based On High-Accuracy PredictionThe Computer Journal10.1093/comjnl/bxad07467:4(1467-1482)Online publication date: 29-Jul-2023
https://doi.org/10.1093/comjnl/bxad074
Shresthamali SKondo MNakamura H(2022)Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement LearningJournal of Low Power Electronics and Applications10.3390/jlpea1204005312:4(53)Online publication date: 8-Oct-2022
https://doi.org/10.3390/jlpea12040053
Show More Cited By

Index Terms

Optimal Rate Control for Energy-Harvesting Systems with Random Data and Energy Arrivals
1. Networks
  1. Network performance evaluation
    1. Network performance modeling

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 1

February 2019

382 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3300201

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 February 2019

Accepted: 01 November 2018

Revised: 01 September 2018

Received: 01 December 2017

Published in TOSN Volume 15, Issue 1

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NSF of China
NSF of Zhejiang Province of China
Open Research Fund of Tianjin Key Laboratory of Advanced Networking (TANK)
CCF-Tencent RAGR
National Key R&D Program of China

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Cited By

Feng YZhang XJia RLin FLu JZheng ZLi M(2023)Intelligent Trajectory Design for Mobile Energy Harvesting and Data TransmissionIEEE Internet of Things Journal10.1109/JIOT.2022.320225210:1(403-416)Online publication date: 1-Jan-2023
https://doi.org/10.1109/JIOT.2022.3202252
Ma DWang DHuang XHu YMa L(2023)KEFSAR: A Solar-Aware Routing Strategy For Rechargeable IoT Based On High-Accuracy PredictionThe Computer Journal10.1093/comjnl/bxad07467:4(1467-1482)Online publication date: 29-Jul-2023
https://doi.org/10.1093/comjnl/bxad074
Shresthamali SKondo MNakamura H(2022)Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement LearningJournal of Low Power Electronics and Applications10.3390/jlpea1204005312:4(53)Online publication date: 8-Oct-2022
https://doi.org/10.3390/jlpea12040053
Han Y(2022)Data Transmission Control Method of Electrical Equipment of Automobile Based on the 5G Communication TechnologyMathematical Problems in Engineering10.1155/2022/21396292022(1-9)Online publication date: 24-Mar-2022
https://doi.org/10.1155/2022/2139629
Zhou XLi YHe DZhang CJi X(2021)Energy-Efficient Channel Allocation Based Data Aggregation for Intertidal Wireless Sensor NetworksIEEE Sensors Journal10.1109/JSEN.2021.308162521:15(17386-17394)Online publication date: 1-Aug-2021
https://doi.org/10.1109/JSEN.2021.3081625
Shresthamali SKondo MNakamura H(2019)Power Management of Wireless Sensor Nodes with Coordinated Distributed Reinforcement Learning2019 IEEE 37th International Conference on Computer Design (ICCD)10.1109/ICCD46524.2019.00092(638-647)Online publication date: Nov-2019
https://doi.org/10.1109/ICCD46524.2019.00092

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