A Novel Hybrid Grey Wolf Optimization Methodology for Resource Constrained Networks Kaynak Kisitli Aǧlar için Yeni Bir Hibrit Gri Kurt Eniyileme Metodolojisi

Date

2022-01-01

Author

Sert, Seyyit Alper

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

115
views

0
downloads

Resource Constrained Networks (RCNs) are networks that mostly have constraints (limitation) on one or more of the basic element properties that make up the network. These networks, especially in the Internet of Things (IoT) or Wireless Sensor Network (WSN) examples, consist of nodes with poor performance in various aspects, and resource constraints become crucial for the nodes that have wireless connection to the network. A resource constraint could be in the form of bandwidth, which is one of the main features of the network, or it can also appear in the form of energy, which is one of the characteristics of the nodes, and this latter situation drastically affects the lifespan of the network and application. Today, extensive research on the efficient use of energy, especially clustering studies, focus on the selection of Cluster Heads (CHs) for creating a solution to the problem. In this study, a Hybrid Gray Wolf Optimization (HGWO) methodology is proposed to overcome the early convergence of the basic Gray Wolf Optimization (GWO) algorithm and is tested for optimizing the CH selection process in WSNs with the aim of prolonging the lifespan of RCNs.

Subject Keywords

approximation, energy efficiency, hybrid gray wolf optimization, resource constrained networks, Kaynak kısıtlı ağlar, Hibrit gri kurt eniyi- leme, Enerji verimliliği, Yakınsama

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138739716&origin=inward
https://hdl.handle.net/11511/101613

DOI

https://doi.org/10.1109/siu55565.2022.9864673

Conference Name

30th Signal Processing and Communications Applications Conference, SIU 2022

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A comparative study of evolutionary network design Kalkan, Sinan; Şehitoğlu, Onur Tolga; Üçoluk, Göktürk; Department of Computer Engineering (2003) In network design, a communication network is optimized for a given set of parameters like cost, reliability and delay. This study analyzes network design problem using Genetic Algorithms in detail and makes comparison of different approaches and representations. Encoding of a problem is one of the most crucial design choices in Genetic Algorithms. For network design problem, this study compares adjacency matrix representation with list of edges representation. Also, another problem is defining a fair fitne...
A Link delay computation method for the quality of service support in software defined networks Balo, Efe; Güran Schmidt, Ece.; Department of Electrical and Electronics Engineering (2019) Packet switched networks cannot provide tight delay bounds that are required by certain types of applications despite facilitating high throughput. Therefore, delay measurementtechniquesforpacket-switchednetworkshavealwaysgrabbedtheattention of the community to both utilize advantages of packet-switched networks and provide a realistic end to end delay prediction of packets. Software Deﬁned Networking (SDN) isa new paradigmofpacket-switchednetworkingwhichgathersmanagement functionality of network in a logic...
A novel deep reinforcement learning algorithm for online antenna tuning Balevi, Eren; Andrews, Jeffrey G. (2019-12-01) © 2019 IEEE.The interactions between the cells, most notably due to their coupled interference and the large number of users, render the optimization of antenna parameters prohibitively complex. To cope with this problem, we propose a novel practical deep learning (DL) based reinforcement learning (RL) algorithm to jointly optimize antenna tilt angle and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). In the proposed algorithm, DL is used to extr...
A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers Koltuk, Furkan; Schmidt, Şenan Ece (2020-07-01) © 2020 IEEE.Cloud data center workloads have time- dependencies and are hence non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data centers that have similar time characteristics and cumulative distributions to those of the actual traces. We evaluate our method using the actual resource request traces of Azure collected in 2019 and the well-known Google cloud trace. Our method enables generating synthetic...
A Theoretical Performance Bound for Joint Beamformer Design of Wireless Fronthaul and Access Links in Downlink C-RAN Kadan, Fehmi Emre; Yılmaz, Ali Özgür (2021-01-01) It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution to this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink C-RAN with a wireless fronthaul and aim to m...

Citation Formats

S. A. Sert, “A Novel Hybrid Grey Wolf Optimization Methodology for Resource Constrained Networks Kaynak Kisitli Aǧlar için Yeni Bir Hibrit Gri Kurt Eniyileme Metodolojisi,” presented at the 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138739716&origin=inward.