Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Chen, Fangni; Wang, Anding; Ni, Zhengwei; Hua, Jingyu

doi:10.3390/s21144798

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…They have not considered the cluster member nodes which are neither participated in transmission nor reception. Psomas and Krikidis [20] evaluated the impact of successive interference cancellation (SIC) on the SWIPT-PS performance in bipolar ad-hoc network. The main aim of SIC is that some signals related to interference may be strong enough to decode the information instead of removing from aggregated received signal.…”

Section: Swipt-psmentioning

confidence: 99%

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

Pavani

Devi

Subbareddy

2022

Intell. and Converged Netw.

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Simultaneous wireless information and power transfer (SWIPT) is recently emerging as one of the vital solutions to prolong the lifetime of energy constrained wireless sensor nodes. However, current works on SWIPT considered only the immediate past-hop node's RF signal as a source of energy harvesting in multi-hop Internet of things (IoT) networks. In case of weak radio frequency (RF) signal, the amount of harvested energy does not support for continuous communication. Hence, in this paper a new energy harvesting mechanism is proposed which considers multiple sources (MS) such as (1) sink broadcasting energy, (2) co-channel interference, (3) neighbor nodes' RF signal, and (4) immediate past-hop node's RF signal for energy harvesting. Towards such prospect, a new SWIPT architecture is proposed called hybrid SWIPT (H-SWIPT) by integrating time switching (TS) and power splitting (PS) architectures.Furthermore, an efficient route selection mechanism is introduced to minimize the total energy consumption of the path based on an energy cost metric. To validate the proposed mechanism, simulation experiments are conducted and obtained the superiority of H-SWIPT compared with existing methods in terms of average harvested energy. Further, the effectiveness of proposed method performance is investigated through energy cost at different node density and barrier rates.

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Section: Swipt-psmentioning

confidence: 99%

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

Pavani

Devi

Subbareddy

2022

Intell. and Converged Netw.

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“…IoT with WSN support has brought about a number of applications and improved the convenience of everyday living. The distributed network across different WSN applications for the Internet of Things may be static or dynamic depending on its specific requirements [7,8]. Smart healthcare monitoring systems enable dynamic networks, unlike static networks, which are supported by crop monitoring and environmental monitoring, for instance.…”

Section: Introductionmentioning

confidence: 99%

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications

Gupta,

Wadhwa,

Rani

et al. 2023

Sensors

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Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

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“…Even given that, realizing 5G or B5G IoT networks is still challenging due to the limited energies for the IoT devices equipped with batteries. To alleviate this problem, simultaneous wireless information and power transfer (SWIPT) are proposed to effectively and conveniently extend the lifetime of IoT devices, and employed in many related works [ 3 , 4 , 5 , 6 , 7 ]. In fact, SWIPT is a key technique in 5G and B5G because power allocation and interference management are still the crucial issues to be addressed in the communication networks [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory

Liu

Lin

et al. 2022

Sensors

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Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

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Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Cited by 10 publications

References 38 publications

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory

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