A Cloud-Fog Based Environment Using Beam Search Algorithm in Smart Grid

Tehreem, Komal; Javaid, Nadeem; Bano, Hamida; Ansar, Kainat; Waheed, Moomina; Butt, Hanan

doi:10.1007/978-3-319-98530-5_57

Cited by 4 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies are closely related to our work to execute workflow applications on heterogeneous nodes in cloud computing. In [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], the authors suggested dynamic approaches to deal with runtime anomalies and security risks in IoT networks. However, these methods consumed too many resources and had much delay during encryption and decryption in resource allocation.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

View full text Add to dashboard Cite

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

show abstract

Section: Related Workmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…However, the static brings many resource matching and deadline missing challenges performances within the proposed work. These studies [20,29,30,38] suggested dynamic scheduling and handles deadline of applications. However, they considered the mobile and fog nodes for coarse-grained and ine-grained applications with lightweight and delay-sensitive tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The study devised the global searching Algorithm 4 like Breadth-irst searching (BFS) [35], Hill Climbing searching (HCS) [30], and Beam searching (BS) [29]. The main agenda of searching in this study to search optimal objective function within available diferent candidate solutions such as Z * .…”

Section: Task Schedulingmentioning

confidence: 99%

Underwater Sensor Multi-Parameter Scheduling for Heterogenous Computing Nodes

Elhoseny

Lakhan

Rashid

et al. 2022

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

r gb]1.00,0.00,0.00Sensor Aware distributed workflow applications are becoming increasingly popular underwater. The apps are marine operations that generate data and process it based on its characteristics. Mobile-fog-cloud paradigms, as well as computing such as sensor nodes, have emerged. As previously stated, the nodes can be combined into a single system to achieve several goals. Many factors are considered, including network contents, workload fluctuation, variable execution durations, deadlines, and bandwidth. As a result, scheduling mobile workflow systems with multiple parameters might be challenging. The study suggests a novel content-efficient decision-aware task scheduling (CATSA) method for defining and adapting to complicated environmental changes. The CATSA consists of several components that work together to perform various benchmarks in the system, including a decision planner, sequencing, and scheduling. As evidenced by test findings during evaluation, the suggested architecture outperforms current studies regarding workflow execution quality of services and improved the makespan 30% and deadline meeting 40% in the study.

show abstract

“…They have used MQTT protocol as the communication protocol and Ubidots [95] IoT platform for experimentation. Tehreem et al [97] have integrated the fog computing environment with the smart grid to manage energy consumption efficiently. The region-based division has been carried out, having multigrid's assigned to each region.…”

Section: Edge Computing and Fog Computing-based Techniques In Smart Hmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

View full text Add to dashboard Cite

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

show abstract

A Cloud-Fog Based Environment Using Beam Search Algorithm in Smart Grid

Cited by 4 publications

References 9 publications

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Underwater Sensor Multi-Parameter Scheduling for Heterogenous Computing Nodes

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Contact Info

Product

Resources

About