Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

Min, Minghui; Wan, Xiaoyue; Xiao, Liang; Chen, Ye; Xia, Minghua; Wu, Di; Dai, Huaiyu

doi:10.1109/jiot.2018.2875926

Cited by 178 publications

(93 citation statements)

References 37 publications

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“…At edge servers, Xu et al [154] proposed a post-decision state (PDS) based learning algorithm to obtain the optimal workload offloading (to the centralized cloud) policy, which minimizes the long-term system cost. Instead, at the IoT devices, Min et al [155], [156] combined PDS based learning and deep convolutional neural network (CNN) to select the optimal edge device and offloading rate.…”

Section: A Reinforcement Learning Based Communication Optimization Imentioning

confidence: 99%

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Lan

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

238

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Sensing Optimizations Computing Optimizations Communications Optimizations  Commercial products and services  Standards for wireless protocol and transducer test method  Checkpointing optimization  Timekeeping across power failures  Packet-less communication  Reinforcement learning based communication optimization  Backscatter communication  Kinetic EH sensing  Thermal EH sensing  Solar EH sensing  RF EH sensingTABLE I: Vendors specializing in energy harvesting components and solutions suitable for IoTs. Company Source Energy Harvester EH Solution Application Foundation Year Piezo Systems Kinetic × piezoelectric energy harvesting 1988 MIDE Technology Kinetic × piezoelectric energy harvesting

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Section: A Reinforcement Learning Based Communication Optimization Imentioning

confidence: 99%

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Lan

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

238

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“…However, this paper focuses on the energy efficiency scheduling of MEC server, i.e., MEC server has to provide stable offloading service. Due to the advancement in energy harvesting techniques, research about IoT systems with energy harvesting attracts significant attention [10,[17][18][19]. In [17], reinforcement learning based offloading algorithm is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…In this algorithm, the energy harvesting is not primarily considered in the offloading scheduling; it is only mentioned that it can extend network life time. A healthcare IoT system was proposed in energy harvesting environment by the authors of [19]. This algorithm is also not suitable for the EH MEC environments since it aims to protect user privacy.…”

Section: Related Workmentioning

confidence: 99%

“…Each IoT devices schedules its computation frequency based on the energy harvesting status. All of the works in [10,[17][18][19] assumed that IoT devices were equipped with energy harvesting modules and tried to solve the offloading scheduling problem under battery constraints. However, these algorithms are not suitable for the proposed EH MEC environment where the MEC server is equipped with energy harvesting module, i.e., our proposed scheme aims at the stable operation of MEC server in energy harvesting environment, but previous studies aimed at survivability of IoT devices.…”

Section: Related Workmentioning

confidence: 99%

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Two-Stage Computation Offloading Scheduling Algorithm for Energy-Harvesting Mobile Edge Computing

Park

Lee

et al. 2019

Energies

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Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.

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“…A comprehensive survey on the major characteristics, research issues, and challenges in WBANs for patient monitoring from a practical design and implementation perspective was provided in the works of [4,5]. Additionally, several previous works in the literature were proposed to investigate the energy-saving technologies from the aspects of the media access control (MAC) protocol design, power control, and cross-layer resource allocation strategies to make efforts to prolong the lifetime of WBANs [6][7][8][9][10][11][12]. In the work of [7], the authors presented a time division multiple access (TDMA)-based technique to improve WBANs' reliability and energy efficiency by adaptively synchronizing nodes while tackling channel and buffer status.…”

mentioning

confidence: 99%

Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Xie

Zhang

et al. 2019

Sensors

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Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

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Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

Cited by 178 publications

References 37 publications

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

Two-Stage Computation Offloading Scheduling Algorithm for Energy-Harvesting Mobile Edge Computing

Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

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