Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Aftab, Farooq; Khan, Asif; Zhang, Zhongshan

doi:10.1109/access.2019.2913912

Cited by 45 publications

(30 citation statements)

References 25 publications

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“…FGSA employs an efficient fitness function considering different factors such as distance, delay, link lifetime, and energy. Aftab et al 30 develop a hybrid self‐organized clustering scheme (HSCS) that uses glow‐worm swarm optimization for clustering and CH selection and adopts the dragonfly algorithm for cluster management. Gupta and Jha 31 propose an improved cuckoo‐search–based clustering protocol which uses a novel objective function for uniform distribution of CHs in the network.…”

Section: Related Workmentioning

confidence: 90%

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Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Sadeghi

Avokh

2020

Int J Communication

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This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster-based scheme named "Cuckoo-search Clustering with Two-hop Routing Tree (CC-TRT)" to develop a two-hop load-balanced data aggregation routing tree in the network. CC-TRT uses a modified energyaware cuckoo-search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo-search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC-TRT scheme to present two methods for constructing multi-hop data aggregation routing trees, named "Cuckoo-search Clustering with Multi-Hop Routing Tree (CC-MRT)" and "Cuckoo-search Clustering with Weighted Multi-hop Routing Tree (CC-WMRT)." Both CC-MRT and CC-WMRT rely on a two-level structure; they not only use an energy-aware cuckoo-search algorithm to fairly select the best CHs but also adopt a load-balanced high-level routing tree to route the aggregated data of CHs to the sink node. However, CC-WMRT slightly has a better performance thanks to its low-level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC-TRT, CC-MRT, and CC-WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.

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Section: Related Workmentioning

confidence: 90%

“…In previous studies, 29–32 the authors use meta‐heuristic algorithms to improve the performance of the network. Dhumane and Prasad 29 propose a fractional gravitational search algorithm (FGSA) to find the optimal CHs in the IoT‐based networks.…”

Section: Related Workmentioning

confidence: 99%

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Sadeghi

Avokh

2020

Int J Communication

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“…Identifying a valid clustering technique is an important research subject for VANETs not only to organize and manage VANETs but also to efficiently integrate a short-range vehicle network with a wide-range network. Vehicular clustering offers various benefits such as dynamic topology stabilization, improvement of routing efficiency and minimization of control overhead, and specifically provides scalability by limiting the number of accesses to a PLMN [14], [16].…”

Section: Related Work a Vanet Clustering Techniquesmentioning

confidence: 99%

“…Clustering improves system stability by localizing the control targets and reducing signals for avoiding global propagation. Additionally, as VANET clustering reduces the impact of dynamic topology changes, the structure of network system becomes more manageable and stable [16]. An overall taxonomy of clustering techniques and protocols in various VANET applications in each category are well summarized in [17].…”

Section: Related Work a Vanet Clustering Techniquesmentioning

confidence: 99%

“…The contributions of this paper are summarized as follows: -Vehicle clusters are autonomously formed through NMDP-APC process in consideration of real-time motion dynamics; -The cluster granularity is designed to be derived from a ML algorithm with minimum criteria; -Traffic density and cellular congestion states are specifically considered for granularity determination; -A fully distributed system is designed with its entire procedure and sequence in line with 5G C-V2X; and -Numerical evaluation and substantial simulations are performed by reflecting theoretical analysis. The proposed scheme is applicable not only to vehicular communication but also to a wide range of other moving MTC objects including a large number of mobile robots and emerging drone-like objects aiming for future applications [16]. This study therefore must have a large potential to be usefully applied to a wide variety of mobile MTC applications.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

et al. 2020

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Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5 th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters. INDEX TERMS 5G mobile communication, vehicular ad hoc networks, machine-to-machine communications, machine learning, mobile computing, clustering methods, heterogeneous networks.

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A bio‐inspired backbone formation protocol in cognitive radio networks

Kumar

Murmu

Singh

2022

Int J Communication

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Summary The cognitive radio network (CRN) is an emerging technology where communication among secondary users (SUs) takes place in an opportunistic manner. Since, a backbone eases the routing, reduces communication overhead, and efficiently handles the dynamic changes in the network. Therefore, we intend to propose a backbone formation algorithm in CRN. However, finding optimal backbone is equivalent to find minimum connected dominating set (CDS) for the given network, which is NP‐complete. Hence, the backbone construction problem can be better handled by optimization algorithms than the deterministic ones. The bio‐inspired is an interesting category of optimization algorithms. Therefore, it has been popularly used to handle various optimization problems in wireless networks. However, it has never been used for backbone construction in CRN. The ant colony optimization (ACO) is a promising bio‐inspired method because it has been effectively used to handle many problems in wireless networks, e.g. cluster head selection, leader election, routing etc. Therefore, we use ant colony system (ACS) for backbone formation in CRN. Also, the approach helps us to find optimized path between any pair of nodes to ensure efficient communication. To the best of our knowledge, it is the first bio‐inspired backbone construction algorithm in CRN.

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Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Cited by 45 publications

References 25 publications

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

A bio‐inspired backbone formation protocol in cognitive radio networks

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