In this paper, we consider Internet of Things (IoT) systems that can be applied to various applications with low-mobility or static IoT devices, such as wireless sensor networks and charging systems for low-power devices with communication. The IoT systems consist of IoT devices and a hybrid access point (H-AP) powered by both on-grid and renewable energy sources. The IoT devices have a capability to harvest energy from the H-AP's radio frequency signal, and they perform their tasks by using only their harvested energy. We consider the tasks do not have a real-time requirement which can be stored in the task queues of the IoT devices and performed later. We study resource and task scheduling for the IoT systems which aims at minimizing the on-grid energy consumption at the H-AP while guaranteeing the minimum average data rate and minimum task performing rates of IoT devices. To achieve the goal, we first propose a centralized resource and task scheduling algorithm. However, its computational complexity and signaling overhead are too large due to the task scheduling for each IoT device. Thus, to resolve these issues, we propose a hybrid resource and task scheduling algorithm in which each IoT device determines its own task scheduling in a distributed manner and the H-AP determines the resource scheduling. We then provide performance analyses showing that our proposed algorithms are asymptotically optimal and well satisfy the QoS requirements of IoT devices even with distributed task scheduling. Through the simulation results, we verify the analyses and show the performance of our algorithms.
Bibliographical noteFunding Information:
Manuscript received June 27, 2018; revised August 13, 2018 and September 13, 2018; accepted September 28, 2018. Date of publication October 3, 2018; date of current version May 8, 2019. This work was supported in part by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-IT1701-13 and in part by the Midcareer Researcher Program through an NRF grant funded by MSIT, South Korea, under Grant NRF-2017R1A2 B4006908. (Corresponding author: Jang-Won Lee.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org). Digital Object Identifier 10.1109/JIOT.2018.2873658
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications