Hierarchical resource scheduling method using improved cuckoo search algorithm for internet of things

Zhang, Chunguang; Zeng, Guangping; Wang, Hongbo; Tu, Xuyan

doi:10.1007/s12083-019-00801-8

Cited by 14 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, it is assumed that the system's microstates are independent of one another for entropy's extensibility. Tsallis proposes a nonextensive entropy (22) to describe these systems. It is denoted as:…”

Section: Tsallis Entropy Methodmentioning

confidence: 99%

A novel multithresholding algorithm for segmentation of the MRI images

Pancholi

Modi

Chitaliya

2023

Salud, Ciencia y Tecnología

View full text Add to dashboard Cite

La segmentación es una etapa crucial en las técnicas de evaluación de imágenes. La segmentación precisa de imágenes de resonancia magnética cerebral se ha estudiado ampliamente porque el uso de este tipo de métodos permite detectar y reconocer una amplia gama de trastornos. El umbralaje es un método sencillo y eficaz para segmentar imágenes. Pero dependiendo de cuántos umbrales se empleen para la segmentación, las técnicas basadas en el umbral tienden a ser más costosas de calcular. En consecuencia, los algoritmos metaheurísticos son una herramienta crucial para el umbralado multinivel que ayudan a determinar los mejores valores. Utilizando un algoritmo de búsqueda cucú (NCS), hemos sugerido un método más eficiente para segmentar imágenes de resonancia magnética. Se utilizaron tres funciones objetivo diferentes (el método de Otsu, la entropía de Kapur y la función de entropía de Tsallis) comparando el resultado de la estrategia proyectada con el algoritmo de búsqueda del cuco (CS).

show abstract

Section: Tsallis Entropy Methodmentioning

confidence: 99%

A novel multithresholding algorithm for segmentation of the MRI images

Pancholi

Modi

Chitaliya

2023

Salud, Ciencia y Tecnología

View full text Add to dashboard Cite

show abstract

“…Therefore, this guideline for optimization strategies helps to find the best way to carry out the task with reliable and beneficial results. To create a system-performance-sensitive planner [17], considering the framework of the IoT, one examines and analyzes K-means, principal component analysis, and fuzzy C-means clustering (FCM) methods. Sefati et al [18] proposed a dynamic scheduling technique for cloud computing that prioritized requests from the IoT while considering homogenous and heterogeneous servers.…”

Section: Background Studymentioning

confidence: 99%

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

et al. 2023

View full text Add to dashboard Cite

One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.

show abstract

“…Hierarchical resource scheduling for the IoTs that was based on an Improved Heuristic algorithm was studied and further explored extensively by Zhang et al, [15]. The Cuckoo Search (CS) algorithm that is based on an Adaptive Cauchy Mutation had been proposed.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Multi Hop Data Aggregation in Medical Iot Using Trust Based Secure Routing

Sunithanandhini¹,

Natarajan

Ramprasath³

2022

Preprint

View full text Add to dashboard Cite

The concept of the Internet of Things (IoT) consists of various sensing devices that have been subject to the threats that stem from an increased dependence on the technologies of communication. The IoT is the concept of healthcare is the main ingredient in the provision of medical facilities to patients and also facilitates both hospitals and doctors. The system proposed contained different medical devices like the sensors and the applications that are mobile-based or web-based. These communicate by means of the devices connected through the network that helps in monitoring and recording medical information and health data of the patients. The IoT was characterized by distributed topology and limited energy. For this scenario, there is a routing algorithm that is energy-saving and secure playing a major role to ensure IoT performance. The aggregation of security data has a crucial role in reducing the transmission of data and further in prolonging the lifetime of Wireless Sensor Network (WSN). When there was a threat to node security, the nodes had generated different data errors resulting in trouble to the measures of security. Owing to the difference in standards and their communication stacks, the traditional mechanisms to security were dependent on the trust management for functioning. For the purpose of this work, there was a multi-hop routing model used in medical IoT that was proposed. For balancing of consumption of energy and trust, routing from the source to the destination has to be taken into consideration for the appraisal of trust as opposed to the destination peers. At the same time, the consumption of energy has to be one major criterion. There was an Imperialist Competitive Algorithm (ICA) with Harmony Search (HS) that can balance the consumption of energy for the reduction of trust cost. This was based on a socio-politically inspired strategy of optimization. The HS was based on the process of improvisation of musicians. The results of the experiment proved this strategy was better in terms of performance than the other methods.

show abstract

Hierarchical resource scheduling method using improved cuckoo search algorithm for internet of things

Cited by 14 publications

References 22 publications

A novel multithresholding algorithm for segmentation of the MRI images

A novel multithresholding algorithm for segmentation of the MRI images

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

Optimization of Multi Hop Data Aggregation in Medical Iot Using Trust Based Secure Routing

Contact Info

Product

Resources

About