Hybrid Whale Optimization‐Based Energy‐Efficient Lightweight Internet of Things Framework DOI

Avishek Sinha,

Samayveer Singh, Harsh Kumar Verma

и другие.

International Journal of Communication Systems, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

ABSTRACT The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, faces challenges addressing time‐sensitive tasks due the physical distance between servers from users. Edge been introduced for internet of things (IoT) as an effective complement enhance capacity handling latency‐critical tasks. However, limited resources IoT and edge nodes can lead suboptimal task management. In response these challenges, we propose a lightweight approach that leverages hybrid technique combining whale optimization algorithm (WOA) with adaptive inertia weight genetic component. This method aims efficiency offloading cloud‐edge environment. Experimental results demonstrate proposed strategy not only addresses limitations traditional methods but also achieves significant improvements, 34% increase makespan minimization, 11% reduction rejection ratio, 17% decrease execution cost, 15% improvement energy utilization compared WOAs. simulation highlight effectiveness enhancing quality service (QoS) metrics latency‐sensitive applications.

Язык: Английский

ESFMTO: A reliable task offloading strategy based on edge server failure model in IIoT DOI
Yu-Bin Yang, Yan Chen, Ningjiang Chen

и другие.

Ad Hoc Networks, Год журнала: 2025, Номер unknown, С. 103887 - 103887

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

The application of hybrid spider monkey optimization and fuzzy self-defense algorithms for multi-objective scientific workflow scheduling in cloud computing DOI
Mustafa Ibrahim Khaleel

Internet of Things, Год журнала: 2025, Номер unknown, С. 101517 - 101517

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

A Survey on Task Scheduling in Edge-Cloud DOI
Subham Sahoo, Sambit Kumar Mishra

SN Computer Science, Год журнала: 2025, Номер 6(3)

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0

Towards sustainable smart cities: Workflow scheduling in cloud of health things (CoHT) using deep reinforcement learning and moth flame optimization for edge-cloud systems DOI
Mustafa Ibrahim Khaleel

Future Generation Computer Systems, Год журнала: 2025, Номер unknown, С. 107821 - 107821

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

An efficient computation offloading and resource allocation technique using deep reinforcement learning in edge-cloud collaborated environment DOI
Mukesh Kumar Jha, Mohit Kumar

The Journal of Supercomputing, Год журнала: 2025, Номер 81(8)

Опубликована: Май 29, 2025

Язык: Английский

Процитировано

0

Hybrid Whale Optimization‐Based Energy‐Efficient Lightweight Internet of Things Framework DOI

Avishek Sinha,

Samayveer Singh, Harsh Kumar Verma

и другие.

International Journal of Communication Systems, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

ABSTRACT The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, faces challenges addressing time‐sensitive tasks due the physical distance between servers from users. Edge been introduced for internet of things (IoT) as an effective complement enhance capacity handling latency‐critical tasks. However, limited resources IoT and edge nodes can lead suboptimal task management. In response these challenges, we propose a lightweight approach that leverages hybrid technique combining whale optimization algorithm (WOA) with adaptive inertia weight genetic component. This method aims efficiency offloading cloud‐edge environment. Experimental results demonstrate proposed strategy not only addresses limitations traditional methods but also achieves significant improvements, 34% increase makespan minimization, 11% reduction rejection ratio, 17% decrease execution cost, 15% improvement energy utilization compared WOAs. simulation highlight effectiveness enhancing quality service (QoS) metrics latency‐sensitive applications.

Язык: Английский

Процитировано

0