Optimal Power Allocation for Maximizing Energy Efficiency in DAS-Based IoT Network

Masood, Zaki; Park, Hosung; Jang, Han Seung; Yoo, Sunyong; Kim, Eun Jung

doi:10.1109/jsyst.2020.3013693

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…The control plane maps VNF and virtual links to the bottom layer based on the service function resource requirements and underlying resource information and implements the deployment of logical service chains. The data plane mainly includes general hardware devices (such as standardized forwarding devices and x86 hardware resource devices), whose main function is to receive the rules issued by the control plane and carry the actual service requests [9].…”

Section: Network Model and Problem Descriptionmentioning

confidence: 99%

Optimization Distribution Method of Computer Sensor Network Communication Based on the Linear Planning Model

et al. 2022

Journal of Sensors

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In order to explore the optimal allocation method of computer network communication, a linear programming model is proposed. Based on the overall structure of service chain deployment, the integer linear programming model is introduced to make mathematical modeling, so that network resources and load balancing can be effectively utilized under resource and delay constraints. Simulation results showed that in the experiment, 10 to 100 SFC request intensities were generated, respectively, calculating the request acceptance rates of the three deployment algorithms. Under the same conditions, the average request acceptance rate based on the G-kSP algorithm is about 95.4% and about 92.3%, and the greedy deployment algorithm is about 81.7%. We show that the optimized distribution method of computer sensor network communication based on a linear programming model reduces the load balance and time complexity while reducing the request acceptance rate.

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Section: Network Model and Problem Descriptionmentioning

confidence: 99%

Optimization Distribution Method of Computer Sensor Network Communication Based on the Linear Planning Model

et al. 2022

Journal of Sensors

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“…The authors of [22] investigate the problem of maximizing the throughput of IoT devices and propose a Lagrangian-based algorithm for this gradient, which jointly allocates spectrum and transmit power to improve the total system throughput. The authors of [23] address the energy efficiency optimization problem of optimized networks and propose an iterative algorithm based on the Karush-Kuhn-Tucker (KKT) condition to combine the transmit power and power coefficient, and its algorithm improves the energy efficiency performance of the system. In [24], a multi-objective energy-carrying network optimization problem was investigated, and the multi-objective problem was converted into a single-objective problem by using the defined equivalent sum-rate method for solving the problem, and the scheme optimized both system throughput and system energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [14] and others demonstrate IoT networks with energy cooperation, low power consumption and good performance. In [20][21][22][23][24][25][26] studied SWIPT networks optimized the on-net operation of terminals without introducing energy cooperation to save the power consumption of the H-AP. The above literature shows that SWIPT and energy cooperation technologies can effectively reduce system power consumption, however, the energy efficiency performance of SWIP-enabled IoT with energy cooperation is still unknown, and as people attach importance to the green network, energy efficiency becomes more and more important.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

Cao

Zhong

Peng

et al. 2022

Sensors

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As an advanced technology, simultaneous wireless information and power transfer (SWIPT), combined with the internet of things (IoT) devices, can effectively extend the online cycle of the terminal. To cope with the fluctuation of energy harvesting by the hybrid access points (H-AP), the energy cooperation base station is introduced to realize the sharing of renewable energy. In this paper, we study the SWIPT-enabled IoT networks with cooperation. Our goal is to maximize the energy efficiency of the system, and at the same time, we need to meet the energy harvesting constraints, user quality of service (QoS) constraints and transmission power constraints. We jointly solve the power allocation, time switching and energy cooperation problems. Because this problem is a nonlinear programming problem, it is difficult to solve directly, so we use the alternating variable method, the iterative algorithm is used to solve the power allocation and time switching problem, and the matching algorithm is used to solve the energy cooperation problem. Simulation results show that the proposed algorithm has obvious advantages in energy efficiency performance compared with the comparison algorithm. At the same time, it is also proved that the introduction of energy cooperation technology can effectively reduce system energy consumption and improve system energy efficiency.

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“…This motivates us to propose a routing protocol that addresses the QoS node energy consumption, Smart Mobile Data Collectors to Enhance quality of service in Wireless Sensor Networ throughput, latency-time, and stability. In WSNs and internet of things (IoT) [1,2], clustering emerged as a major technique for data-mining to address problems such as lifetime of the network, stability, aggregation of data, reduction of energy consumption, and reliability [3]. For many benefits, aggregation is recommended in the exploitation of WSNs data.…”

Section: Introductionmentioning

confidence: 99%

Smart Mobile Data Collectors to Enhance quality of service in Wireless Sensor Networks

Gantassi

Masood

Choi

2022

Preprint

Self Cite

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This paper proposes a new intelligent mobile data collector (MDC) using the traveling salesman problem (TSP) in order to find the optimal path traversed by the MDC for efficiency of energy and quality of service (QoS). Specifically, our proposed MDC-TSP protocol uses K-Means and grid clustering algorithm to reduce the consumption of energy during the cluster head (CH) election step. Furthermore, MDC is used as an interface between CH and sink to improve Wireless Sensor Network (WSN) QoS to enhance the MDC-K protocol transmission phase. Experimental results show that MDC-TSP improves on various improvements of the low energy adaptive clustering (LEACH) protocol like MDC-K.

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Optimal Power Allocation for Maximizing Energy Efficiency in DAS-Based IoT Network

Cited by 15 publications

References 35 publications

Optimization Distribution Method of Computer Sensor Network Communication Based on the Linear Planning Model

Optimization Distribution Method of Computer Sensor Network Communication Based on the Linear Planning Model

Energy Efficiency Optimization for SWIPT-Enabled IoT Network with Energy Cooperation

Smart Mobile Data Collectors to Enhance quality of service in Wireless Sensor Networks

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