Bi-CRS: Bio-Inspired Cluster-Based Routing Scheme for D2D Communication in IoT

Yashoda, M. B.; Shivashetty, Vrinda

doi:10.1007/978-981-16-7118-0_17

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…To address the constrained problem of resource and power allocation, which aims to enhance overall network throughput, we propose using bio-inspired methods. These methods have shown promising results in previous works [37,[45][46][47][48]. In the following section, we will detail the bio-inspired algorithms and their application in optimizing channel and power allocation for D2D communication within cellular networks.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Benbraika,

Kraa,

Himeur

et al. 2024

Computers

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Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Benbraika,

Kraa,

Himeur

et al. 2024

Computers

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“…Metaheuristic and heuristic algorithms are commonly utilized in optimal CH selection as efficient techniques to obtain workable solutions with variable degrees of success. The ABC algorithm is an intelligent swarm technique based on metaheuristics stimulated by bees' natural honeygathering behavior [8,9]. In contrast to other forms of swarm intelligence, the ABC algorithm may obtain good optimization results by balancing local exploration and global development.…”

Section: International Journal Of Electrical and Computer Engineering...mentioning

confidence: 99%

“…The network's quality is calculated using a fitness function. The fitness function is derived using equation (9).…”

Section: Ch Selection Using the Abc Algorithmmentioning

confidence: 99%

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

B¹,

Champla

Pushpavalli³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system's lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively.

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“…The source node can communicate with the destination using the path through optimal adjacent CH through OPSO based routing algorithm. The simulation results of the proposed scheme is compared with existing swarm intelligence (SI) based algorithms like GAPSO-H [16], ARES [17], bio-inspired cluster-based routing scheme (Bi-CRS) [30], ESMO [27] based on performance metrics like packet delivery ratio (PDR), delay, residual energy, and network lifetime. Under this section, the above metrics are investigated by altering number of nodes in each simulation.…”

Section: Simulation Set Up and Performance Analysismentioning

confidence: 99%

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

M. Balakrishna,

Shivashetty

2023

IJECE

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<span lang="EN-US">Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.</span>

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Bi-CRS: Bio-Inspired Cluster-Based Routing Scheme for D2D Communication in IoT

Cited by 5 publications

References 18 publications

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

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