Routing of Mobile Cognitive Radio Base Station for Disaster Recovery Networks

Hartinah, Siti; Prakoso, Hario; Anwar, Khoirul

doi:10.1109/iceltics.2018.8548865

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…The MCRBS should have connectivity between MCRBSs as the backbone link to rebuild network communication. It can be established through point-to-point link via radio communication based on a predefined routing algorithm [4] [5]. The MCRBS requires an antenna as critical system capable of establishing point-to-point link of the radio communication, where the system should be capable of dynamically directing the beam into specific sector by determining MCRBSs at The previous studies in [6] and [7], the antenna of MCRBS was designed to serve mobile devices of the second telecommunication generation -the fifth telecommunication generation networks using omnidirectional biconical and directional horn antenna.…”

Section: Mobile Cognitive Radio Base Station (Mcrbs) Proposed Inmentioning

confidence: 99%

Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks

Prihatiningtyas¹,

Anwar²,

Pramudita³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Mobile Cognitive Radio Base Station (MCRBS) is a technology to recover network communication temporarily in disaster areas. The communications can be built by constructing the backbone network, where it is formed by connecting multiple MCRBSs with point-to-point radio communication link considering the best routing algorithm. The MCRBS antenna system should has an ability on producing the beam into specific d irection to establish point-to-point radio communication link. However, there is no capable system to support the MCRBS antenna system for directing the beam. Therefore, it requires a system that can assist in focusing the beam of antenna system for the MCRBSs connectivity. In this paper, we propose Rotman Lens that has beamforming capability to support radio backbone communication for multiple MCRBSs connectivity in post-disaster networks. The Rotman Lens is chosen to generate amplitude and phase shift in performing beamforming technique with simultaneous signal transmissions from MCRBS to others without moving the antenna system. In this research, the Rotman Lens was evaluated through a series of computer simulations and laboratory experiments to investigate the lens performance. The final p roposed lens p rovides the e xperiment results for a ll beam ports with a n average value both return l oss and mutual coupling of less than −15 dB and −20 dB, where the magnitude deviation in average value and the maximum error of phase difference are 1.172 dB and 6.014 • , respectively. The proposed Rotman Lens is also confirmed t o be capable of controlling beam with scanning capability of −27 • , −14 • , 0 • , 14 • , 27 • to support wireless communication backbone link among MCRBSs in disaster recovery networks.

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Section: Mobile Cognitive Radio Base Station (Mcrbs) Proposed Inmentioning

confidence: 99%

Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks

Prihatiningtyas¹,

Anwar²,

Pramudita³

2022

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

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“…In this subsection, the average network capacity is estimated, where the results are illustrated in Figures 4 and 5. Two different case studies are handled in the study: Case 1 – fixed BS‐PU and mobile BS‐CR [31] and Case 2 – mobile BS‐PU and fixed BS‐CR. Case 1 condition is illustrated in Figure 4 and Case 2 condition is illustrated in Figure 5.…”

Section: Performance Evaluationmentioning

confidence: 99%

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Natarajan¹,

Srihari

Chandragandhi³

et al. 2021

IET Communications

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With new telecommunications engineering applications, the cognitive radio (CR) networkbased internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR-IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT. INTRODUCTIONWireless networks reconfigurable (RWN) is mainly an adaptive network firmware developed to satisfy the demands of modern applications, changing network topologies and changing network conditions. In particular, the RWM can be reconfiguredThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…3 and 4, is estimated in this section. The study deals with two different case studies: Box 1-BS-PU, BS-CR [39] mobile fixed and Box 2-BS-PU for mobile and BS-CR mobile. Figure 1 illustrates the condition in Fig.…”

Section: Average Network Capacitymentioning

confidence: 99%

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Dhiman¹,

Sharma

2021

Complex Intell. Syst.

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In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

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Routing of Mobile Cognitive Radio Base Station for Disaster Recovery Networks

Cited by 8 publications

References 11 publications

Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks

Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

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