Fog computing-based deep learning model for optimization of microgrid-connected WSN with load balancing

Karthik, S.; Kavithamani, A.

doi:10.1007/s11276-021-02613-2

Cited by 21 publications

(12 citation statements)

References 22 publications

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“…Figure 4 shows the convergence performance of the algorithms proposed in this paper. It can be seen from the figure that as number of iterations increases, the objective function value Equation (11) of the algorithms gradually increase. After about six iterations, all algorithms tend to convergence.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…A three-layer framework is proposed based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency [10]. In [11], fog computing was utilized to optimize the WSN connected to the microgrid. In the grid-connected community (GCC), an energy model was modeled to evaluate the energy consumption of the WSN and its performance in microgrids.…”

Section: Literature Reviewmentioning

confidence: 99%

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Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Zhong

Zhang

et al. 2021

Energies

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Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.

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Section: Simulation Resultsmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Zhong

Zhang

et al. 2021

Energies

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“…As a good solution to these problems, machine earning techniques were able to successfully handle these dynamic situations [28]. For optimization of microgrid-connected WSNs with oad balancing and intrusion detection, some authors [29,30] use the data-driven deep earning approach to solve the above-mentioned data transmission tasks with dynamic conditions. We find that the proposed method [29,30] can outperform the state-of-the-art solutions in terms of recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…For optimization of microgrid-connected WSNs with oad balancing and intrusion detection, some authors [29,30] use the data-driven deep earning approach to solve the above-mentioned data transmission tasks with dynamic conditions. We find that the proposed method [29,30] can outperform the state-of-the-art solutions in terms of recognition accuracy. Zhou et al [31] also trained deep neural network in WSNs for image target recognition when data, energy and computation resources are imited.…”

Section: Related Workmentioning

confidence: 99%

Optimal Data Transmission for WSNs with Data-Location Integration

Min

2021

Symmetry

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Wireless sensor networks (WSNs) have good performance for data transmission, and the data transmission of sensor nodes has the function of symmetry. However, the wireless sensor nodes are facing great pressure in data transmission due to the increasing amount and types of data that easily cause premature energy consumption of some nodes and, thus, affects data transmission. Clustering algorithm is a common method to balance energy consumption, but the existing algorithms fail to balance the network oad effectively for big data transmission. Therefore, an optimal data transmission with data-location integration (ODTD-LI) is proposed for WSNs in this paper. For optimal data transmission, we update the network topology once for one round. In the proposed algorithm, we perform calculations of the optimal cluster heads, clustering and data transmission routing through three steps. We first deploy N homogeneous and symmetry nodes in a square area randomly and calculate the optimal number of cluster heads according to the node ocations. then, the optimal number of cluster heads, energy consumption, the distances and degrees of the nodes are taken into consideration during the clustering phase. Direct communication is carried out within a cluster, and the member nodes of the cluster pass the information directly to the cluster head. Lastly, an optimal hybrid routing from each cluster node to Sink is constructed for data transmission after clustering. The simulations verify the good performance of the proposed algorithm in view of the ifetime, average delay, coverage rate (CR) and oad balance of the network compared with the existing algorithms. Through the research conducted in this paper, we find that our work has good performance for selecting the hybrid routing in the network with the nodes randomly arranged.

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“…The objectives of load balancing are throughput improvement, response time reduction, and traffic optimization. The goal of the load balancing approach is to optimize how server-side resources are used, as well as to reduce the time it takes to process requests and improve scalability in a distributed setting [5]. To ISSN: 2302-9285  Hybrid load-balancing algorithm for distributed fog computing in internet of things … (Abrar Saad Kadhim) 3463 mitigate the effects of node load and disruption on the fog computing environments, each node should employ a dispersed structure.…”

Section: Introductionmentioning

confidence: 99%

Hybrid load-balancing algorithm for distributed fog computing in internet of things environment

Kadhim

Manaa

2022

Bulletin EEI

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Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.

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Fog computing-based deep learning model for optimization of microgrid-connected WSN with load balancing

Cited by 21 publications

References 22 publications

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Optimal Data Transmission for WSNs with Data-Location Integration

Hybrid load-balancing algorithm for distributed fog computing in internet of things environment

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