Hybrid NN-based green cognitive radio sensor networks for next-generation IoT

Mukherjee, Amrit; Manman, Li; Goswami, Pratik; Yang, Lixia; Garg, Sahil; Piran, Md. Jalil

doi:10.1007/s00521-021-05700-9

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The operation of an effective routing protocol involves establishing the possible maximum routing paths, maximum energy conservation and minimum delay and signaling mechanism utilization for the progress of secondary communication nodes. In [35], the authors illustrated the description of the routing protocol which has the main characteristics of routing metrics for the ideal route selection in transmission process. In [36], the authors proposed a capability aware scheme with assigning mechanism in the routing protocols of the ad hoc CRN.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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“…Since data gathered from all other members of a cluster can not be collected if a CH node died during a round, and hence, data acquired cannot be delivered to the sink. As a result, the lifespan of the cluster members, or the WSN as a whole, is rendered worthless 11 …”

Section: Introductionmentioning

confidence: 99%

“…As a result, the lifespan of the cluster members, or the WSN as a whole, is rendered worthless. 11 In lieu of the above discussion, the researchers have predominantly focused on utilizing the meta-heuristic methods for the selection of an optimized CH. 10,12 The combination of different parameters namely, energy, distance, node proximity, etc.…”

Section: Introductionmentioning

confidence: 99%

ROTEE: Remora Optimization and Tunicate swarm algorithm‐based Energy‐Efficient cluster‐based routing for EH‐enabled heterogeneous WSNs

Gupta

Rout

2022

Int J Communication

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Since the development of Wireless Sensor Networks (WSNs), the limited battery of the sensor nodes has been an unavoidable concern. Hence, to keep the WSNs operational for a longer possible duration, the recharging of node's battery through harvesting the ambient energy from surroundings (for an example, solar energy) has been proposed. In this work, we focus not only on utilizing the energy harvesting (EH)-enabled sensor nodes for routing purposes but also introduce a novel hybrid optimization ROATSA that uses Remora Optimization Algorithm (ROA) and Tunicate Swarm Algorithm (TSA) for energy-efficient cluster-based routing. The proposed work is termed as ROA and TSA-based Energy-Efficient Cluster-based Routing for EH-enabled WSN (ROTEE). Hybrid ROATSA is chosen due to enhanced convergence and exploitation capabilities. To reduce the financial burden on the network, we use only four EH-enabled nodes and locate them at each periphery of the network, equidistant to each other and the other nodes are 3-level energy heterogeneous sensor nodes. The selection of cluster head (CH) is optimized through ROATSA by considering profile index of each node by evaluating them at energy, distance, load balancing, node density, the delay involved, and network's average energy. The proposed work ROTEE shows supreme performance against the recently proposed clustering techniques.

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Sustainable Green Cognitive Radio Networks: Optimized Deep Transfer Learning Model for Energy Consumption

Elghamrawy,

Hassnien

2024

Studies in Systems, Decision and Control

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Hybrid NN-based green cognitive radio sensor networks for next-generation IoT

Cited by 6 publications

References 31 publications

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

ROTEE: Remora Optimization and Tunicate swarm algorithm‐based Energy‐Efficient cluster‐based routing for EH‐enabled heterogeneous WSNs

Sustainable Green Cognitive Radio Networks: Optimized Deep Transfer Learning Model for Energy Consumption

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