Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

Khan, Mohd Abdul Rahim; Shavkatovich, Shavkatov Navruzbek; Nagpal, Bharti; Kumar, Anil; Haq, Mohd Anul; Tharini, V. Jeevika; Karupusamy, Sathishkumar; Alazzam, Malik Bader

doi:10.1016/j.tcs.2022.05.031

Cited by 29 publications

(10 citation statements)

References 31 publications

(35 reference statements)

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“…The works in [31][32][33][34][35] proposed different methods based on metaheuristic techniques for cluster head selection in IoT-WSN. In [31], an energyefficient cluster head selection using two metaheuristic algorithms, namely, Competitive Swarm Optimization and Harmony Search Algorithm was proposed.…”

Section: Metaheuristic Techniquesmentioning

confidence: 99%

“…The developed approach provided a global search with a fast convergence rate for energy-efficient cluster-based WSNs, using different energy-saving strategies such as clustering methods and optimal route selection for WSN devices. Moreover, the authors in [32], developed a hybrid metaheuristic approach, namely, Whale Optimization Algorithm with Simulated Annealing algorithm, to minimize the device's energy consumption in IoT-based WSNs and to divide the IoT network into clusters and select the best cluster head for each group. The authors in [33] proposed a selection process based on computational intelligence techniques, namely, neuro-fuzzy inference system-based routing for intercluster transmission in IoT-WSN.…”

Section: Metaheuristic Techniquesmentioning

confidence: 99%

“…At every iteration, the new position for each particle is computed by adding the particle's current velocity to its position. Then, each particle's velocity is modified towards its Lbest and Gbest employing Equation (32), and its new position is found utilizing Equation (33). The ith swarm particle changes its velocity v i ðtÞ and position p i ðtÞ at the time step t based to the following equations:…”

Section: Clustering Approach Using Particle Swarmmentioning

confidence: 99%

“…The velocity and position are updated for each particle using Equation (32) and Equation ( 33) respectively. end end Algorithm 1: Clustering with Particle Swarm Optimization (PSO).…”

Section: Efficient Uav Placement Using Genetic Algorithm (Ga)mentioning

confidence: 99%

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Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

et al. 2023

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Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

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Section: Metaheuristic Techniquesmentioning

confidence: 99%

Section: Metaheuristic Techniquesmentioning

confidence: 99%

Section: Clustering Approach Using Particle Swarmmentioning

confidence: 99%

“…The velocity and position are updated for each particle using Equation (32) and Equation ( 33) respectively. end end Algorithm 1: Clustering with Particle Swarm Optimization (PSO).…”

Section: Efficient Uav Placement Using Genetic Algorithm (Ga)mentioning

confidence: 99%

See 2 more Smart Citations

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

et al. 2023

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“…The impact of PR(.) on NTp is the focus of the following section and acquiring of W PR is also considered [77][78][79][80][81].…”

Section: Network Pruningmentioning

confidence: 99%

Emerging Paradigms of Network and Pruning Strategies: A comprehensive Survey

2022

Preprint

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Deep neural networks have been utilized in a variety of applications and have shown to have exceptional skills in the area of computer vision. Complex network designs delivers a considerable computational resource and energy cost issue for real-time deployment. These difficulties can be solved using improvements like network compression. Many times, network compression may be achieved with minimum loss of accuracy. Accuracy may even enhance in rare circumstances. This study presents a pruning survey on network compression. Pruning can be classified as dynamic or static, depending on whether it is done offline or in real time. This article analyses pruning methods and explains the criterion for removing duplicate calculations. Also covered trade-offs in element-by-element, channel-by-channel, shape-by-shape, filter-by-filter, layer-by-layer, and even network-by-network pruning. In this article, the pros and limitations of a variety of existing methodologies are contrasted and analyzed, as well as compressed network accuracy findings for a variety of frameworks and practical advice for compressing networks.

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RETRACTED ARTICLE: Image-Fused: improved Alzheimer’s disease diagnosis using efficient deep neural network

Mishra

Dutta

2023

Soft Comput

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Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

Cited by 29 publications

References 31 publications

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

Emerging Paradigms of Network and Pruning Strategies: A comprehensive Survey

RETRACTED ARTICLE: Image-Fused: improved Alzheimer’s disease diagnosis using efficient deep neural network

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