SPO: Security-Aware Multi-Objective IoT Task Scheduling in Distributed Cloud Environment

Intelligent DBA-Fuzzy Task Scheduling Framework for IoT Tasks in Distributed Cloud Environment

The rapid growth of Internet of Things (IoT) applications has introduced significant challenges in efficient task scheduling within distributed cloud environments, particularly in meeting Quality of Service (QoS) requirements while minimizing operational costs and SLA violations. To address this issue, this paper proposes an AI-Based Hybrid Detective Behavior Optimization (DBA) technique integrated with fuzzy systems for intelligent task scheduling of IoT workloads. The proposed approach leverages the exploration–exploitation capabilities of DBA along with fuzzy logic-based decision-making to dynamically prioritize and allocate tasks under uncertain and heterogeneous cloud conditions. The model is evaluated using the DigitalOcean cloud workload in the WorkflowSim simulation environment and compared against traditional methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Deep Reinforcement Learning (DRL). Experimental results demonstrate that the proposed DBA-Fuzzy approach significantly outperforms baseline methods by reducing SLA violations by 24.6%, improving QoS by 21.3%, minimizing execution cost by 18.9%, and enhancing throughput by 26.7%. These improvements highlight the robustness and adaptability of the proposed model in handling dynamic IoT workloads. The findings suggest that integrating metaheuristic optimization with fuzzy reasoning provides an effective solution for multi-objective task scheduling, making it highly suitable for next-generation distributed cloud environments supporting large-scale IoT applications.

QUBO: Quantum-Inspired Metaheuristic-based IoT Task Scheduling in Multi-Cloud Environment

Task scheduling in multi-cloud-based IoT is a very relevant issue as the demand on energy-efficient and environmentally conscious computing is growing. The proposed study suggests that a Quantum-Inspired Metaheuristic-based scheduling method based on a Quadratic Unconstrained Binary Optimization (QUBO) model could be used to optimize the task allocation among IoT devices over distributed cloud resources. The main goal is to reduce key performance parameters, such as temperature, cost, energy consumption, and carbon emissions, which are essential in sustainable cloud operations. The suggested approach is assessed on the CEA-Curie workload data set in the CloudSim simulation platform and compared to such traditional algorithms as Genetic Algorithm (GA), Deep Reinforcement Learning (DRL), and Asynchronous Advantage Actor-Critic (A3C). The experimental findings show that the QUBO-based algorithm is much better than the current method with an average temperature, operational cost, energy consumption, and carbon reduction of 21.8%, 19.5%, 24.3%, and 22.7% respectively relative to baseline algorithms. These enhancements indicate that quantum-inspired optimization is effective in solving more complicated multi-objective scheduling issues. The results indicate that the suggested model offers a scalable and sustainable solution to next-generation IoT-cloud ecosystems, which helps increase the impact of reduced environmental impact and improved resource efficiency in multi-cloud systems.

UWL REPOSITORY

QoS-aware and energy-efficient metaheuristic optimization based service placement strategy for fog-based IoT applications

This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.

AI-Assisted IoT Network Security using Hybrid Metaheuristic Optimization Algorithm (AI-HMOA)

The intensive growth of Internet of Things (IoT) devices has caused significant security concerns because of dynamic networks structure, resource scarcity, and differences in devices, as well as rising vulnerability to advanced cyberattacks. The traditional security control systems are not always able to offer such flexible and energy efficient security in large scale IoT systems. To overcome these issues, this paper presents an AI-based IoT network security architecture based on hybrid metaheuristic optimization algorithms (AI-HMOA) that intelligently optimizes the resistance to intrusion, secure routing and key management operations. The presented framework will combine the use of machine learning-based attack detection and a hybrid metaheuristic optimization framework that incorporates global search algorithm exploration and local search strategy exploitation. The hybrid optimizer operates dynamically to choose the best security parameters such as the use of secure routes, cryptographic keys, and trust levels of the nodes to use, depending on the current network conditions. Also, adaptive learning regarding traffic patterns through AI-driven decision-making can be used to identify deviant behavior and avert threats like packet dropping, spoofing, and denial-of-service attacks. Simulation experiments have shown that the suggested solution qualifies greatly in terms of improving the ratio of packet delivery, network life and detection accuracy and minimizing energy consumption, routing overhead and the effects of attacks as compared to traditional and single-algorithm optimization based security systems. The results validate the fact that a combination of artificial intelligence and hybrid metaheuristic optimization provides a powerful, scalable, and energy-efficient security solution of the next-generation IoT network.

CARE-FSP: A Cost-Aware and Resource-Efficient Hybrid RSO–SFO Framework for Fog-Based IoT Service Placement

The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.