Efficient Resource Utilization in Cloud-Fog Environment Integrated with Smart Grids

Akram, Junaid; Najam, Zeeshan; Rafi, Arslan

doi:10.1109/fit.2018.00040

Cited by 13 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Faster RCNN is purely used CNN for feature extraction rather than handcrafted features ( Figure 4 ). With VGG16 [ 44 ] model, faster RCNN gives 5 fps on GPU and achieves object detection accuracy on PASCAL VOC [ 45 , 46 ] dataset using three hundred proposals per image. With the rapid development of faster RCNN, Lenc et al [ 47 ] studied the role of generation of region proposals through selective search and generation of region proposals through CNN and claimed that CNN based RPN contains less geometric information for object detection in the CONV Layers rather than FC layers.…”

Section: Methodsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

show abstract

Section: Methodsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The resource management problem is also addressed considering diverse practical reallife applications such as vehicular network [22,4], smart grid [23,24], smart Buildings [25,26], smart manufacturing [16,27], smart city [28,29]. Authors in [22] presented an adaptive resource management algorithm for vehicular networks with the goal to minimize the transmission rate, delay-jitter and the upper-bound of delay.…”

Section: Real-world Application Specificmentioning

confidence: 99%

HeRAFC: Heuristic Resource Allocation and Optimization in MultiFog-Cloud Environment

Dehury¹,

Veeravalli²,

Srirama³

2023

Preprint

View full text Add to dashboard Cite

By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computing environments, resulting in better quality of service and lower latency on the network.From the service providers' point of view, this addresses the network latency and congestion issues. This is achieved by deploying the services in cloud and fog computing environments.The responsibility of service providers is to manage the heterogeneous resources available in both computing environments. In recent years, resource management strategies have made it possible to efficiently allocate resources from nearby fog and clouds to users' applications.Unfortunately, these existing resource management strategies fail to give the desired result when the service providers have the opportunity to allocate the resources to the users' application from fog nodes that are at a multi-hop distance from the nearby fog node. The complexity of this resource management problem drastically increases in a MultiFog-Cloud environment. This problem motivates us to revisit and present a novel Heuristic Resource Allocation and Optimization algorithm in a MultiFog-Cloud (HeRAFC) environment. Taking users' application priority, execution time, and communication latency into account, HeR-AFC optimizes resource utilization and minimizes cloud load. The proposed algorithm is evaluated and compared with related algorithms. The simulation results show the efficiency of the proposed HeRAFC over other algorithms.

show abstract

“…It is proposed in the paper [38,39] that wireless sensor networks (WSNs) can benefit from computational intelligence techniques such as multi-objective particle swarm optimization (MOPSO), with the overall goal of concurrently minimising localization time, energy consumption during localization, and maximising the number of nodes that are fully localised. Using Dijkstra's algorithm, refs [40][41][42] construct an improved version 15 July 2021 submitted to Mathematics 3 of 19 versions of the low-energy adaptive clustering hierarchy (LEACH) protocol in a cloud environment. This protocol optimises the power consumption or energy usage based on shortest route selection, and it is referred to as LEACH-DA.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

show abstract

Efficient Resource Utilization in Cloud-Fog Environment Integrated with Smart Grids

Cited by 13 publications

References 15 publications

Automatic Target Detection from Satellite Imagery Using Machine Learning

Automatic Target Detection from Satellite Imagery Using Machine Learning

HeRAFC: Heuristic Resource Allocation and Optimization in MultiFog-Cloud Environment

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Contact Info

Product

Resources

About