Joint Time Allocation for Wireless Energy Harvesting Decode-and-Forward Relay-Based IoT Networks With Rechargeable and Nonrechargeable Batteries

Shim, Yeonggyu; Park, Hyuncheol; Shin, Wonjae

doi:10.1109/jiot.2020.3020960

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…During the experiment, when the task starts to execute, we calculate the energy consumption required to process each task according to Equation (12). After the task is completed, we use Equation ( 13) to obtain the energy consumed by all tasks to com-higher energy consumption.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…On the other hand, in intelligent production lines where batteries power many fog nodes, different task scheduling strategies lead to different energy consumption, which inevitably brings many problems. For example, a study found that when a device cannot be charged in time, frequent data exchange, transmission, and processing can cause the battery's life to be significantly reduced due to instantaneous discharge, thereby causing a data leakage security risk [12]. It is a tremendous challenge for intelligent production lines to ensure low delay to complete tasks and effectively reduce the power consumption of fog nodes.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

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Section: Energy Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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“…Meanwhile, the energy consumption of user devices is also a key factor. In many scenarios, mobile devices cannot be charged in time, and frequent linking, switching, and data transmission with base stations (BS) lead to excessive instantaneous discharge, which causes a significant decrease in the battery life of mobile devices [6]. Therefore, energy harvesting (EH) technology is seen as a possible solution for harvesting clean energy, including solar, wind, and kinetic energy, to provide computing energy for user devices [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Here, d represents the offloading deadline which is equal to the length of the time slot. -17) indicates the computing time and transmission time cannot exceed a time slot (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). indicates the offloading energy consumption cannot exceed the maximum instantaneous discharge threshold.…”

mentioning

confidence: 99%

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Bai

Han

et al. 2021

IEEE Access

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Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios.

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“…There are several relaying schemes that can be employed to improve PLS, but two of the most popular are Cooperative Schemes DF and AF in wireless communication techniques [2,3]. The nodes in communication networks are powered by putting separate batteries inside them; however, in some circumstances, replacing or recharging those batteries is not recommended [4]. The Energy-Harvesting (EH) approach [5][6][7] can be used to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Secrecy by Power Splitting and Jamming in Cooperative Multiple Relay Based on Energy Harvesting in Full-Duplex Network

et al. 2021

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In this article, we investigated the secrecy performance of a three-hop relay network system with Power Splitting (PS) and Energy Harvesting (EH). In the presence of one eavesdropper, a signal is transferred from source to destination with the help of a relay. The source signal transmits in full-duplex (FD) mood, jamming the relay transfer signals to the destination. The relay and source employ Time Switching (TS) and Energy Harvesting (EH) techniques to obtain the power from the power beacon. In this study, we compared the Secrecy Rate of two Cooperative Schemes, Amplify and Forward (AF) and Decode and Forward (DF), for both designed systems with the established EH and PS system. The Secrecy Rate was improved by 50.5% in the AF scheme and by 44.2% in the DF scheme between the relay and eavesdropper at 40 m apart for the proposed system in EH and PS. This simulation was performed using the Monto Carlo method in MATLAB.

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Joint Time Allocation for Wireless Energy Harvesting Decode-and-Forward Relay-Based IoT Networks With Rechargeable and Nonrechargeable Batteries

Cited by 12 publications

References 28 publications

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Physical Layer Secrecy by Power Splitting and Jamming in Cooperative Multiple Relay Based on Energy Harvesting in Full-Duplex Network

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