Trustworthy and efficient project scheduling in IIoT based on smart contracts and edge computing

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 11, 2025

To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources complete specific tasks. Additionally, when span across factories, ensuring faithful execution contracts becomes another challenge. In this paper, we propose modified combinatorial auction method combined blockchain and edge computing technologies organize project scheduling. Firstly, transform efficient resource into resource-constrained multi-project problem (RCPSP). Subsequently, the solution integrates random sampling (CA-RS) contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that suggested CA-RS approach significantly enhances efficiency arrangement within industrial Internet Things compared baseline algorithms.

Language: Английский

---

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2128 - e2128

Published: June 17, 2024

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending capabilities cloud network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring timely execution tasks within fog environments. This article presents comprehensive review advancements task methodologies for systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid nature-inspired heuristic approaches. Through systematic analysis relevant literature, we highlight strengths limitations each approach identify key challenges facing scheduling, including dynamic environments, heterogeneity, scalability, constraints, security concerns, algorithm transparency. Furthermore, propose future research directions these challenges, integration machine learning techniques real-time adaptation, leveraging federated collaborative developing resource-aware energy-efficient algorithms, incorporating security-aware techniques, advancing explainable AI methodologies. By addressing pursuing directions, aim facilitate development more robust, adaptable, efficient task-scheduling solutions ultimately fostering trust, security, sustainability systems facilitating their widespread adoption across diverse applications domains.

Language: Английский

---

AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 10265 - 10298

Published: May 8, 2024

Language: Английский

---

Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 687 - 687

Published: Jan. 23, 2025

This systematic literature review analyzes machine learning (ML)-based techniques for resource management in fog computing. Utilizing the Preferred Reporting Items Systematic Reviews and Meta-Analyses (PRISMA) protocol, this paper focuses on ML deep (DL) solutions. Resource computing domain was thoroughly analyzed by identifying key factors constraints. A total of 68 research papers extended versions were finally selected included study. The findings highlight a strong preference DL addressing challenges within paradigm, i.e., 66% reviewed articles leveraged techniques, while 34% utilized ML. Key such as latency, energy consumption, task scheduling, QoS are interconnected critical optimization. analysis reveals that prime addressed ML-based management. Latency is most frequently parameter, investigated 77% articles, followed consumption scheduling at 44% 33%, respectively. Furthermore, according to our evaluation, an extensive range challenges, computational scalability management, data availability quality, model complexity interpretability, employing 73, 53, 45, 46 ML/DL

Language: Английский

---

Towards an efficient scheduling strategy based on multi-objective optimization in fog environments

Computing, Journal Year: 2025, Volume and Issue: 107(3)

Published: March 1, 2025

Language: Английский

---

Energy and priority-aware scheduling algorithm for handling delay-sensitive tasks in fog-enabled vehicular networks

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(10), P. 14346 - 14368

Published: March 19, 2024

Language: Английский

---

A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 4281 - 4320

Published: May 11, 2024

Language: Английский

---

Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 8, 2024

Efficient scheduling of scientific workflows in cloud computing environments is essential for optimizing resource utilization and minimizing completion time. In this study, we comprehensively evaluate different algorithms, focusing on the Modified Firefly Optimization Algorithm (ModFOA) comparison with existing methods like Ant Colony (ACO), Genetic (GA), Particle Swarm (PSO). Our investigation considers key performance metrics such as makespan, utilization, energy consumption across diverse configurations scenarios. Scientific often involve intricate tasks dependencies, posing challenges efficient scheduling. While algorithms have shown promise, they may not fully address unique requirements environments, leading to suboptimal outcomes. Therefore, propose evaluating ModFOA’s effectiveness cloud. Through comparative analysis, ModFOA demonstrates superior terms achieving lower times various configurations. Additionally, exhibits competitive moderate consumption, positioning it a promising solution workflow environments. This study underscores significance selecting highlights potential improving management Further research could focus refining parameters validating its practicality real-world

Language: Английский

---

Hybrid Metaheuristic Algorithm based energy efficient Authentication method for IoT enable edge computing

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 18, 2024

The authors have requested that this preprint be removed from Research Square.

Language: Английский

---

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 21, 2024

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) comparing it with established methods such as Ant Colony (ACO), Genetic (GA), Particle Swarm (PSO). We investigate key performance metrics, including makespan, utilization, energy consumption, across both cloud edge configurations. Scientific often involve complex tasks dependencies, which can challenge traditional algorithms. While existing show promise, they may not fully address unique demands environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates computing resources, offering an effective solution these environments. Through comparative analysis, demonstrates improved reducing makespan times, while maintaining competitive efficiency. This study highlights importance incorporating integration algorithms showcases ModFOA's potential enhance workflow efficiency management Future research should focus on refining parameters validating its effectiveness practical scenarios.

Language: Английский

---