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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

https://doi.org/10.1145/3544836
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.5220openalex-snapshot
Profit‐aware coalition formation in fog computing providers: A game‐theoretic approach
inferredcites →https://doi.org/10.1002/ett.3824openalex-snapshot
Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing
inferredcites →https://doi.org/10.1002/spe.2509openalex-snapshot
iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
inferredcites →https://doi.org/10.1002/spe.3025openalex-snapshot
A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions
inferredcites →https://doi.org/10.1002/spe.995openalex-snapshot
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
inferredcites →https://doi.org/10.1007/978-3-030-22971-9_32openalex-snapshot
Mobility-Aware Task Parallel Offloading for Vehicle Fog Computing
inferredcites →https://doi.org/10.1007/s00607-021-00930-0openalex-snapshot
Energy-makespan optimization of workflow scheduling in fog–cloud computing
inferredcites →https://doi.org/10.1007/s10723-018-9437-3openalex-snapshot
A Lightweight Service Placement Approach for Community Network Micro-Clouds
inferredcites →https://doi.org/10.1007/s10723-019-09491-1openalex-snapshot
Resource Management Approaches in Fog Computing: a Comprehensive Review
inferredcites →https://doi.org/10.1007/s11036-018-1177-xopenalex-snapshot
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
inferredcites →https://doi.org/10.1007/s11042-018-7051-9openalex-snapshot
A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments
inferredcites →https://doi.org/10.1007/s11277-021-08714-7openalex-snapshot
Task Offloading in Fog Computing for Using Smart Ant Colony Optimization
inferredcites →https://doi.org/10.1007/s12083-019-00721-7openalex-snapshot
Task offloading in mobile fog computing by classification and regression tree
inferredcites →https://doi.org/10.1016/b978-0-12-805395-9.00001-0openalex-snapshot
Internet of Things: an overview
inferredcites →https://doi.org/10.1016/b978-0-12-805395-9.00004-6openalex-snapshot
Fog Computing: principles, architectures, and applications
inferredcites →https://doi.org/10.1016/j.comcom.2020.10.007openalex-snapshot
Blockchain-based Mobility-aware Offloading mechanism for Fog computing services
inferredcites →https://doi.org/10.1016/j.comnet.2020.107496openalex-snapshot
A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective
inferredcites →https://doi.org/10.1016/j.comnet.2021.108146openalex-snapshot
An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing
inferredcites →https://doi.org/10.1016/j.comnet.2021.108361openalex-snapshot
Failure-resilient DAG task scheduling in edge computing
inferredcites →https://doi.org/10.1016/j.dcan.2018.10.003openalex-snapshot
Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing
inferredcites →https://doi.org/10.1016/j.future.2013.01.010openalex-snapshot
Internet of Things (IoT): A vision, architectural elements, and future directions
inferredcites →https://doi.org/10.1016/j.future.2018.02.014openalex-snapshot
User mobility aware task assignment for Mobile Edge Computing
inferredcites →https://doi.org/10.1016/j.future.2018.04.057openalex-snapshot
Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities
inferredcites →https://doi.org/10.1016/j.future.2018.12.055openalex-snapshot
A computation offloading method over big data for IoT-enabled cloud-edge computing
inferredcites →https://doi.org/10.1016/j.future.2019.01.059openalex-snapshot
Task migration for mobile edge computing using deep reinforcement learning
inferredcites →https://doi.org/10.1016/j.future.2019.07.019openalex-snapshot
Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning
inferredcites →https://doi.org/10.1016/j.future.2019.09.060openalex-snapshot
Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
inferredcites →https://doi.org/10.1016/j.future.2021.05.026openalex-snapshot
Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments
inferredcites →https://doi.org/10.1016/j.imu.2021.100619openalex-snapshot
Rank based mobility-aware scheduling in Fog computing
inferredcites →https://doi.org/10.1016/j.iot.2018.09.009openalex-snapshot
Internet of Things as a Service (iTaaS): Challenges and solutions for management of sensor data on the cloud and the fog
inferredcites →https://doi.org/10.1016/j.iot.2019.100091openalex-snapshot
An effective approach of latency-aware fog smart gateways deployment for IoT services
inferredcites →https://doi.org/10.1016/j.jnca.2019.102407openalex-snapshot
A fog-driven dynamic resource allocation technique in ultra dense femtocell networks
inferredcites →https://doi.org/10.1016/j.jnca.2020.102781openalex-snapshot
A survey on computation offloading modeling for edge computing
inferredcites →https://doi.org/10.1016/j.jnca.2020.102972openalex-snapshot
Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment
inferredcites →https://doi.org/10.1016/j.jnca.2021.102994openalex-snapshot
A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices
inferredcites →https://doi.org/10.1016/j.jnca.2021.103008openalex-snapshot
Context-aware scheduling in Fog computing: A survey, taxonomy, challenges and future directions
inferredcites →https://doi.org/10.1016/j.jpdc.2017.09.009openalex-snapshot
A dynamic tradeoff data processing framework for delay-sensitive applications in Cloud of Things systems
inferredcites →https://doi.org/10.1016/j.jpdc.2018.03.004openalex-snapshot
Quality of Experience (QoE)-aware placement of applications in Fog computing environments
inferredcites →https://doi.org/10.1016/j.jpdc.2021.06.005openalex-snapshot
Fog computing: A taxonomy, systematic review, current trends and research challenges
inferredcites →https://doi.org/10.1016/j.jss.2019.04.050openalex-snapshot
FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
inferredcites →https://doi.org/10.1016/j.jss.2022.111351openalex-snapshot
iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments
inferredcites →https://doi.org/10.1016/j.sysarc.2019.02.009openalex-snapshot
All one needs to know about fog computing and related edge computing paradigms: A complete survey
inferredcites →https://doi.org/10.1016/j.sysarc.2020.101970openalex-snapshot
Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment
inferredcites →https://doi.org/10.1049/pbpc027e_ch2openalex-snapshot
Resource management in edge and fog computing using FogBus2 framework
inferredcites →https://doi.org/10.1109/access.2017.2692960openalex-snapshot
From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework
inferredcites →https://doi.org/10.1109/access.2017.2702013openalex-snapshot
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study
inferredcites →https://doi.org/10.1109/access.2017.2704100openalex-snapshot
Fog Computing in Healthcare–A Review and Discussion
inferredcites →https://doi.org/10.1109/access.2019.2900530openalex-snapshot
Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks
inferredcites →https://doi.org/10.1109/access.2019.2908263openalex-snapshot
Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks
inferredcites →https://doi.org/10.1109/access.2020.2975741openalex-snapshot
Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization
inferredcites →https://doi.org/10.1109/access.2020.2983253openalex-snapshot
Fog Computing: A Comprehensive Architectural Survey
inferredcites →https://doi.org/10.1109/cloud53861.2021.00085openalex-snapshot
Pogonip: Scheduling Asynchronous Applications on the Edge
inferredcites →https://doi.org/10.1109/comst.2017.2771153openalex-snapshot
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges
inferredcites →https://doi.org/10.1109/comst.2020.2970550openalex-snapshot
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/comst.2020.3011208openalex-snapshot
A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities
inferredcites →https://doi.org/10.1109/comst.2021.3101460openalex-snapshot
Survey on Placement Methods in the Edge and Beyond
inferredcites →https://doi.org/10.1109/iccsn52437.2021.9463602openalex-snapshot
An Request Offloading and Scheduling Approach Base on Particle Swarm Optimization Algorithm in IoT-Fog Networks
inferredcites →https://doi.org/10.1109/icfec50348.2020.00010openalex-snapshot
Comparison of Alternative Architectures in Fog Computing
inferredcites →https://doi.org/10.1109/infocomwkshps51825.2021.9484436openalex-snapshot
PGA: A Priority-aware Genetic Algorithm for Task Scheduling in Heterogeneous Fog-Cloud Computing
inferredcites →https://doi.org/10.1109/isorc.2019.00033openalex-snapshot
URMILA: A Performance and Mobility-Aware Fog/Edge Resource Management Middleware
inferredcites →https://doi.org/10.1109/jiot.2018.2876279openalex-snapshot
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/jiot.2020.2981557openalex-snapshot
Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things
inferredcites →https://doi.org/10.1109/jiot.2020.3022699openalex-snapshot
Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey
inferredcites →https://doi.org/10.1109/jiot.2021.3067732openalex-snapshot
Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing
inferredcites →https://doi.org/10.1109/jiot.2021.3091508openalex-snapshot
Multiagent DDPG-Based Joint Task Partitioning and Power Control in Fog Computing Networks
inferredcites →https://doi.org/10.1109/jiot.2021.3104324openalex-snapshot
A Collaborative Computational Offloading Strategy for Latency-Sensitive Applications in Fog Networks
inferredcites →https://doi.org/10.1109/jsac.2017.2760478openalex-snapshot
The Extended Cloud: Review and Analysis of Mobile Edge Computing and Fog From a Security and Resilience Perspective
inferredcites →https://doi.org/10.1109/jsac.2018.2869954openalex-snapshot
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing
inferredcites →https://doi.org/10.1109/jsac.2019.2906790openalex-snapshot
Application Component Placement in NFV-Based Hybrid Cloud/Fog Systems With Mobile Fog Nodes
inferredcites →https://doi.org/10.1109/jsac.2020.2986615openalex-snapshot
Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing
inferredcites →https://doi.org/10.1109/pimrc.2018.8580724openalex-snapshot
Computational Cost and Energy Efficient Task Offloading in Hierarchical Edge-Clouds
inferredcites →https://doi.org/10.1109/sahcn.2018.8397129openalex-snapshot
Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing
inferredcites →https://doi.org/10.1109/scc53864.2021.00065openalex-snapshot
Multi-objective Sparrow Search Optimization for Task Scheduling in Fog-Cloud-Blockchain Systems
inferredcites →https://doi.org/10.1109/tc.2021.3131040openalex-snapshot
Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tcc.2021.3132175openalex-snapshot
A Multi-Objective Clustering Evolutionary Algorithm for Multi-Workflow Computation Offloading in Mobile Edge Computing
inferredcites →https://doi.org/10.1109/tii.2019.2952412openalex-snapshot
Context-Aware Placement of Industry 4.0 Applications in Fog Computing Environments
inferredcites →https://doi.org/10.1109/tii.2021.3130255openalex-snapshot
CeCO: Cost-Efficient Computation Offloading of IoT Applications in Green Industrial Fog Networks
inferredcites →https://doi.org/10.1109/tmc.2018.2815015openalex-snapshot
ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing
inferredcites →https://doi.org/10.1109/tmc.2019.2928811openalex-snapshot
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
inferredcites →https://doi.org/10.1109/tmc.2019.2945956openalex-snapshot
Collaborative Service Placement for Edge Computing in Dense Small Cell Networks
inferredcites →https://doi.org/10.1109/tmc.2019.2957804openalex-snapshot
Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach
inferredcites →https://doi.org/10.1109/tmc.2019.2959772openalex-snapshot
Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks
inferredcites →https://doi.org/10.1109/tmc.2020.2967041openalex-snapshot
An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments
inferredcites →https://doi.org/10.1109/tmc.2020.2970698openalex-snapshot
Optimal Application Deployment in Resource Constrained Distributed Edges
inferredcites →https://doi.org/10.1109/tmc.2020.2984134openalex-snapshot
RAMOS: A Resource-Aware Multi-Objective System for Edge Computing
inferredcites →https://doi.org/10.1109/tmc.2020.2990630openalex-snapshot
Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach
inferredcites →https://doi.org/10.1109/tmc.2020.3012509openalex-snapshot
Imitation Learning Enabled Task Scheduling for Online Vehicular Edge Computing
inferredcites →https://doi.org/10.1109/tmc.2020.3017079openalex-snapshot
Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/tmc.2020.3036390openalex-snapshot
Online Task Offloading for 5G Small Cell Networks
inferredcites →https://doi.org/10.1109/tmc.2020.3036871openalex-snapshot
Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems
inferredcites →https://doi.org/10.1109/tmc.2020.3043736openalex-snapshot
Joint Server Selection, Cooperative Offloading and Handover in Multi-access Edge Computing Wireless Network: A Deep Reinforcement Learning Approach
inferredcites →https://doi.org/10.1109/tmc.2021.3085527openalex-snapshot
Mobility-Aware Computation Offloading in Edge Computing Using Machine Learning
inferredcites →https://doi.org/10.1109/tmc.2021.3089338openalex-snapshot
VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems
inferredcites →https://doi.org/10.1109/tmc.2021.3106746openalex-snapshot
Adaptive User-managed Service Placement for Mobile Edge Computing via Contextual Multi-armed Bandit Learning
inferredcites →https://doi.org/10.1109/tmc.2021.3119200openalex-snapshot
Efficient Dependent Task Offloading for Multiple Applications in MEC-Cloud System
inferredcites →https://doi.org/10.1109/tmc.2021.3123165openalex-snapshot
A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
inferredcites →https://doi.org/10.1109/tnet.2018.2873002openalex-snapshot
An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge Computing
inferredcites →https://doi.org/10.1109/tnet.2019.2916577openalex-snapshot
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
inferredcites →https://doi.org/10.1109/tnet.2019.2953806openalex-snapshot
OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds
inferredcites →https://doi.org/10.1109/tnet.2020.2973800openalex-snapshot
Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services
inferredcites →https://doi.org/10.1109/tnet.2020.3012410openalex-snapshot
Joint Server Assignment and Resource Management for Edge-Based MAR System
inferredcites →https://doi.org/10.1109/tnet.2020.3025985openalex-snapshot
Service Placement for Collaborative Edge Applications
inferredcites →https://doi.org/10.1109/tnet.2020.3048613openalex-snapshot
Service Placement and Request Scheduling for Data-Intensive Applications in Edge Clouds
inferredcites →https://doi.org/10.1109/tnet.2021.3093452openalex-snapshot
Two-Dimensional Task Offloading for Mobile Networks: An Imitation Learning Framework
inferredcites →https://doi.org/10.1109/tnsm.2018.2888481openalex-snapshot
QoS-Aware Fog Resource Provisioning and Mobile Device Power Control in IoT Networks
inferredcites →https://doi.org/10.1109/tpds.2019.2891695openalex-snapshot
An Efficient Application Partitioning Algorithm in Mobile Environments
inferredcites →https://doi.org/10.1109/tpds.2019.2893925openalex-snapshot
Learning Driven Computation Offloading for Asymmetrically Informed Edge Computing
inferredcites →https://doi.org/10.1109/tpds.2019.2926979openalex-snapshot
Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments
inferredcites →https://doi.org/10.1109/tpds.2019.2950937openalex-snapshot
Energy-Aware Application Placement in Mobile Edge Computing: A Stochastic Optimization Approach
inferredcites →https://doi.org/10.1109/tpds.2019.2961905openalex-snapshot
Online Deadline-Aware Task Dispatching and Scheduling in Edge Computing
inferredcites →https://doi.org/10.1109/tpds.2020.3014896openalex-snapshot
Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning
inferredcites →https://doi.org/10.1109/tpds.2020.3042599openalex-snapshot
Distributed and Collective Deep Reinforcement Learning for Computation Offloading: A Practical Perspective
inferredcites →https://doi.org/10.1109/tpds.2021.3076687openalex-snapshot
Offloading Tasks With Dependency and Service Caching in Mobile Edge Computing
inferredcites →https://doi.org/10.1109/tpds.2021.3099731openalex-snapshot
Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems
inferredcites →https://doi.org/10.1109/tpds.2021.3105325openalex-snapshot
Towards Revenue-Driven Multi-User Online Task Offloading in Edge Computing
inferredcites →https://doi.org/10.1109/tpds.2021.3119948openalex-snapshot
LOCUS: User-Perceived Delay-Aware Service Placement and User Allocation in MEC Environment
inferredcites →https://doi.org/10.1109/tpds.2021.3128037openalex-snapshot
Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum
inferredcites →https://doi.org/10.1109/tsc.2018.2826544openalex-snapshot
Multi-User Multi-Task Computation Offloading in Green Mobile Edge Cloud Computing
inferredcites →https://doi.org/10.1109/tsc.2018.2890603openalex-snapshot
Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds
inferredcites →https://doi.org/10.1109/tsc.2020.3028575openalex-snapshot
An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment
inferredcites →https://doi.org/10.1109/tsc.2021.3075988openalex-snapshot
Demand-Driven Deep Reinforcement Learning for Scalable Fog and Service Placement
inferredcites →https://doi.org/10.1109/tsc.2021.3079110openalex-snapshot
Hierarchical Scheduling Mechanisms in Multi-Level Fog Computing
inferredcites →https://doi.org/10.1109/tsc.2021.3094322openalex-snapshot
Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum
inferredcites →https://doi.org/10.1109/tsc.2021.3116280openalex-snapshot
An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tsc.2021.3128603openalex-snapshot
User Preference-Based Hierarchical Offloading for Collaborative Cloud-Edge Computing
inferredcites →https://doi.org/10.1109/tvt.2018.2890685openalex-snapshot
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
inferredcites →https://doi.org/10.1109/tvt.2019.2894437openalex-snapshot
Knowledge-Driven Service Offloading Decision for Vehicular Edge Computing: A Deep Reinforcement Learning Approach
inferredcites →https://doi.org/10.1109/wcnc.2019.8885747openalex-snapshot
Mobility-aware Task Offloading and Migration Schemes in SCNs with Mobile Edge Computing
inferredcites →https://doi.org/10.1145/3057266openalex-snapshot
Fog Computing for Sustainable Smart Cities
inferredcites →https://doi.org/10.1145/3186592openalex-snapshot
Latency-Aware Application Module Management for Fog Computing Environments
inferredcites →https://doi.org/10.1145/3301443openalex-snapshot
Fog Computing for the Internet of Things
inferredcites →https://doi.org/10.1145/3326066openalex-snapshot
Resource Management in Fog/Edge Computing
inferredcites →https://doi.org/10.1145/3344341.3368800openalex-snapshot
Microservices-based IoT Application Placement within Heterogeneous and Resource Constrained Fog Computing Environments
inferredcites →https://doi.org/10.1145/3391196openalex-snapshot
An Overview of Service Placement Problem in Fog and Edge Computing
inferredcites →https://doi.org/10.1145/3403955openalex-snapshot
Application Management in Fog Computing Environments
inferredcites →https://doi.org/10.1145/3460866.3461768openalex-snapshot
FogBus2
inferredcites →https://doi.org/10.15439/2021f005openalex-snapshot
A Distributed Application Placement and Migration Management Techniques for Edge and Fog Computing Environments
inferredcites →https://doi.org/10.3390/info10060191openalex-snapshot
Computation Offloading Strategy in Mobile Edge Computing
inferredcites →https://doi.org/10.3390/s19061446openalex-snapshot
Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks
inferredcites →https://doi.org/10.5281/zenodo.3951624openalex-snapshot
COVID-X: Novel Health-Fog Framework Based on Neutrosophic Classifier for Confrontation Covid-19

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A forecasting-based approach for optimal deployment of edge servers in 5G networks
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Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review
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A novel state-space ODE framework with tensor decomposition for resource allocation in fog-DSDN networks
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A comparative analysis of deep reinforcement learning based task scheduling and offloading in edge and fog computing: a comprehensive survey
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AI and Blockchain Assisted Framework for Offloading and Resource Allocation in Fog Computing
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Service Placement in the Computing Continuum for Delay-Sensitive IoT/IIoT Applications
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SOS-FCI: a secure offloading scheme in fog–cloud-based IoT
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FABLSTM: an optimal resource allocation in SDVN networks with contextual variables integration
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Gabriel Graph and Firefly Optimization for IoT fog management
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State-of-the-Art Load Balancing Algorithms for Mist-Fog-Cloud Assisted Paradigm: A Review and Future Directions
inferred← citeshttps://doi.org/10.1007/s11831-023-10021-2openalex-snapshot
Next Generation Task Offloading Techniques in Evolving Computing Paradigms: Comparative Analysis, Current Challenges, and Future Research Perspectives
inferred← citeshttps://doi.org/10.1007/s12083-025-02183-6openalex-snapshot
Toward a unified computing paradigm: a survey and roadmap for data management in integrated IoT, fog, and cloud services
inferred← citeshttps://doi.org/10.1007/s41060-025-00961-2openalex-snapshot
Application of optimized differential privacy protection algorithm in data mining privacy protection
inferred← citeshttps://doi.org/10.1007/s42452-024-05726-3openalex-snapshot
Internet of Artificial Intelligence (IoAI): the emergence of an autonomous, generative, and fully human-disconnected community
inferred← citeshttps://doi.org/10.1007/s42979-025-03712-zopenalex-snapshot
Enhancing the Kubernetes Platform with a Load-Aware Orchestration Strategy
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Towards smarter IoT through taxonomy and prospective directions for microservices placement in fog computing paradigms
inferred← citeshttps://doi.org/10.1007/s44227-024-00045-wopenalex-snapshot
GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments
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Enhancing quality of service through federated learning in edge-cloud architecture
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From Gaia to LEDGE: A systematic review of seamless application mobility in middleware technologies
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FaaSBid: An auction-based model for Function as a Service in edge-fog environments using unallocated resources
inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112150openalex-snapshot
Integrating EODMA clustering and AOMDV routing through a reciprocal trust and energy-aware model for fog computing
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An adaptive bandwidth scheduling algorithm for IoT devices using machine learning techniques
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Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments
inferred← citeshttps://doi.org/10.1016/j.iot.2023.100866openalex-snapshot
A comprehensive review on Internet of Things application placement in Fog computing environment
inferred← citeshttps://doi.org/10.1016/j.iot.2023.100939openalex-snapshot
A novel hierarchical edge-based architecture for service oriented IoT
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Industry 4.0 and industrial workflow scheduling: A survey
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A look into smart factory for Industrial IoT driven by SDN technology: A comprehensive survey of taxonomy, architectures, issues and future research orientations
inferred← citeshttps://doi.org/10.1016/j.jnca.2024.103891openalex-snapshot
Resource allocation in Fog–Cloud Environments: State of the art
inferred← citeshttps://doi.org/10.1016/j.jnca.2025.104250openalex-snapshot
ReinFog: A Deep Reinforcement Learning empowered framework for resource management in edge and cloud computing environments
inferred← citeshttps://doi.org/10.1016/j.jnca.2026.104439openalex-snapshot
Pulse: Multi-objective scheduling of service-based applications in multi-cluster cloud–edge–IoT infrastructures
inferred← citeshttps://doi.org/10.1016/j.jss.2023.111910openalex-snapshot
MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116233openalex-snapshot
Scalable task scheduling in internet of things networks: Integrating coati optimization with distributed reinforcement learning
inferred← citeshttps://doi.org/10.1038/s41598-026-41740-8openalex-snapshot
Dynamic fog node placement optimization using adaptive dynamic pufferfish optimization for real-time IoT networks
inferred← citeshttps://doi.org/10.1038/s41598-026-52991-wopenalex-snapshot
A hybrid evolutionary framework for efficient IoT task scheduling in fog computing
inferred← citeshttps://doi.org/10.1049/wss2.12051openalex-snapshot
Steam computing paradigm: Cross‐layer solutions over cloud, fog, and edge computing
inferred← citeshttps://doi.org/10.1051/itmconf/20268701019openalex-snapshot
A Review of Reliable and Efficient Task Offloading Strategies on Fog Computing Model for IOT Based Applications
inferred← citeshttps://doi.org/10.1088/1742-6596/2570/1/012029openalex-snapshot
Novel Particle Bee Optimisation based Framework for Fault Tolerance in Fog Environment
inferred← citeshttps://doi.org/10.1109/access.2024.3395664openalex-snapshot
Latency-Aware Multi-Objective Fog Scheduling: Addressing Real-Time Constraints in Distributed Environments
inferred← citeshttps://doi.org/10.1109/access.2024.3418995openalex-snapshot
Internet of Things (IoT): Origins, Embedded Technologies, Smart Applications, and Its Growth in the Last Decade
inferred← citeshttps://doi.org/10.1109/access.2024.3520701openalex-snapshot
Dynamic Service Placement in Edge Computing: A Comparative Evaluation of Nature-Inspired Algorithms
inferred← citeshttps://doi.org/10.1109/access.2025.3599190openalex-snapshot
Deep Learning-Centric Task Offloading in IoT-Fog-Cloud Continuum: A State-of-the-Art Review, Open Research Issues, and Future Directions
inferred← citeshttps://doi.org/10.1109/cac59555.2023.10451632openalex-snapshot
FedCPSO: Federated Learning with Combined Particle Swarm Optimization
inferred← citeshttps://doi.org/10.1109/cascon66301.2025.00053openalex-snapshot
A-MARL: Agile Multi-Agent Reinforcement Learning for Soft Real-Time Task Scheduling in Edge Computing
inferred← citeshttps://doi.org/10.1109/ccgridw69005.2026.00037openalex-snapshot
Predictscale: Proactive Autoscaling Via Workload Forecasting for Edge Microservices Application
inferred← citeshttps://doi.org/10.1109/cloud60044.2023.00037openalex-snapshot
Application and Infrastructure-Aware Orchestration in the Cloud-to-Edge Continuum
inferred← citeshttps://doi.org/10.1109/cloud62652.2024.00020openalex-snapshot
Telemetry-Driven Microservices Orchestration in Cloud-Edge Environments
inferred← citeshttps://doi.org/10.1109/comsnets63942.2025.10885657openalex-snapshot
DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8
inferred← citeshttps://doi.org/10.1109/comst.2023.3316615openalex-snapshot
Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions
inferred← citeshttps://doi.org/10.1109/e-tems64751.2025.11239312openalex-snapshot
Application of Deep Learning Techniques for Automatic Detection of Network Security Threats in Internet of Things Environments
inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10437401openalex-snapshot
A Novel Sustainable Bandwidth Allocation Strategy for Multiple Service Migration in 5G/6G Edge Computing
inferred← citeshttps://doi.org/10.1109/hpcc67675.2025.00097openalex-snapshot
Energy-Efficient and Latency-Aware Microservice Deployment for Satellite Edge System
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11292584openalex-snapshot
Intelligent Job Scheduling for Latency-Critical Edge Applications Using Context-Aware Deep Reinforcement Learning Models
inferred← citeshttps://doi.org/10.1109/icdcsw63686.2024.00021openalex-snapshot
A Framework for Dynamic Dependency-based Service Placement in the Cloud-Edge Continuum
inferred← citeshttps://doi.org/10.1109/icws62655.2024.10878506openalex-snapshot
Collaborative Resource Management and Workloads Scheduling in Cloud-Assisted Mobile Edge Computing across Timescales
inferred← citeshttps://doi.org/10.1109/ipdps65963.2026.00095openalex-snapshot
EdgeWeaver: Accelerating IoT Application Development Across Edge-Cloud Continuum
inferred← citeshttps://doi.org/10.1109/ipdpsw71298.2026.00019openalex-snapshot
Time and Communication Balanced Transaction Scheduling on Trees under the Dual-flow Model
inferred← citeshttps://doi.org/10.1109/isorc70347.2026.11605583openalex-snapshot
Accelerated Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Environments
inferred← citeshttps://doi.org/10.1109/jiot.2023.3235107openalex-snapshot
An Autonomic Workload Prediction and Resource Allocation Framework for Fog-Enabled Industrial IoT
inferred← citeshttps://doi.org/10.1109/jiot.2023.3289228openalex-snapshot
Graph-Reinforcement-Learning-Based Dependency-Aware Microservice Deployment in Edge Computing
inferred← citeshttps://doi.org/10.1109/jiot.2023.3306238openalex-snapshot
Orchestration and Management of Adaptive IoT-Centric Distributed Applications
inferred← citeshttps://doi.org/10.1109/jiot.2025.3529723openalex-snapshot
TRBFT: An Efficient Blockchain Consensus for Edge-Computing-Enabled IoT Systems
inferred← citeshttps://doi.org/10.1109/jiot.2026.3719938openalex-snapshot
A Bounded-Control Gateway-Side QoS Controller for Urgency-Sensitive IoMT Traffic in Smart Healthcare
inferred← citeshttps://doi.org/10.1109/mlsd65526.2025.11220683openalex-snapshot
Resource Allocation Model for IoT Data Processing Systems
inferred← citeshttps://doi.org/10.1109/sose58276.2023.00011openalex-snapshot
PULCEO - A Novel Architecture for Universal and Lightweight Cloud-Edge Orchestration
inferred← citeshttps://doi.org/10.1109/ssci52147.2023.10371825openalex-snapshot
A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tmc.2023.3286847openalex-snapshot
Predictive Service Provisioning With Online Learning in Wireless Edge Networks
inferred← citeshttps://doi.org/10.1109/tmc.2024.3419016openalex-snapshot
Clouds on the Road: A Software-Defined Fog Computing Framework for Intelligent Resource Management in Vehicular Ad-Hoc Networks
inferred← citeshttps://doi.org/10.1109/tnse.2025.3565736openalex-snapshot
Optimizing Resource Allocation for Dynamic IoT Requests Using Network Function Virtualization
inferred← citeshttps://doi.org/10.1109/tsc.2025.3528346openalex-snapshot
TF-DDRL: A Transformer-Enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments
inferred← citeshttps://doi.org/10.1145/3592598openalex-snapshot
Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
inferred← citeshttps://doi.org/10.1145/3631309.3632834openalex-snapshot
Orchestrating serverless applications in the Cloud-to-Edge continuum
inferred← citeshttps://doi.org/10.1145/3652892.3700751openalex-snapshot
FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems
inferred← citeshttps://doi.org/10.1145/3656338openalex-snapshot
Intelligent Edge-powered Data Reduction: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1145/3701997openalex-snapshot
MemoriaNova: Optimizing Memory-Aware Model Inference for Edge Computing
inferred← citeshttps://doi.org/10.1145/3729214openalex-snapshot
A Survey on Services Placement Algorithms in Integrated Cloud-Fog / Edge Computing
inferred← citeshttps://doi.org/10.1371/journal.pone.0304009openalex-snapshot
Optimizing storage on fog computing edge servers: A recent algorithm design with minimal interference
inferred← citeshttps://doi.org/10.23919/date64628.2025.10993012openalex-snapshot
Multi-Partner Project: Orchestrating Deployment and Real-Time Monitoring - NEPHELE Multi-Cloud Ecosystem
inferred← citeshttps://doi.org/10.32604/cmc.2023.035414openalex-snapshot
Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities
inferred← citeshttps://doi.org/10.32604/cmes.2025.059786openalex-snapshot
An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing
inferred← citeshttps://doi.org/10.33140/jsndc.04.01.08openalex-snapshot
Review of Intelligent Healthcare for the Internet of Things: Challenges, Techniques and Future Directions
inferred← citeshttps://doi.org/10.3389/friot.2026.1883673openalex-snapshot
Artificial intelligence-driven resource management in edge-cloud computing continuum for internet of things applications: new trends and future directions
inferred← citeshttps://doi.org/10.3390/app14167104openalex-snapshot
Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities
inferred← citeshttps://doi.org/10.3390/computers14030099openalex-snapshot
Fog Service Placement Optimization: A Survey of State-of-the-Art Strategies and Techniques
inferred← citeshttps://doi.org/10.3390/electronics12122599openalex-snapshot
A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective
inferred← citeshttps://doi.org/10.3390/fi16010016openalex-snapshot
An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
inferred← citeshttps://doi.org/10.3390/fi17010037openalex-snapshot
Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
inferred← citeshttps://doi.org/10.3390/iot4040021openalex-snapshot
Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead
inferred← citeshttps://doi.org/10.3390/s22197326openalex-snapshot
Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments
inferred← citeshttps://doi.org/10.3390/s24061968openalex-snapshot
Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends
inferred← citeshttps://doi.org/10.3390/s24092779openalex-snapshot
A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting
inferred← citeshttps://doi.org/10.3390/s24165141openalex-snapshot
Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing Network to Minimize Average Task Delay with Deadline Constraints
inferred← citeshttps://doi.org/10.3390/s24186064openalex-snapshot
A Survey on Reduction of Energy Consumption in Fog Networks—Communications and Computations
inferred← citeshttps://doi.org/10.3390/su15065104openalex-snapshot
An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications
inferred← citeshttps://doi.org/10.3390/su16041599openalex-snapshot
Edge-Distributed IoT Services Assist the Economic Sustainability of LEO Satellite Constellation Construction
inferred← citeshttps://doi.org/10.36227/techrxiv.21788450openalex-snapshot
Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions
inferred← citeshttps://doi.org/10.36227/techrxiv.21788450.v1openalex-snapshot
Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions
inferred← citeshttps://doi.org/10.36287/setsci.6.1.005openalex-snapshot
Integration of Edge AI and Metaheuristic Algorithms for Advanced Optimization and Analytical Solutions in Future Smart Systems
inferred← citeshttps://doi.org/10.3897/jucs.90429openalex-snapshot
Cost-Effective Scheduling in Fog Computing: An Environment Based on Modified PROMETHEE Technique
inferred← citeshttps://doi.org/10.4018/979-8-3373-3321-2.ch010openalex-snapshot
Survey of Fog Computing Applications in Vehicular Communication for Smart Transportation Systems
inferred← citeshttps://doi.org/10.4018/979-8-3693-1694-8.ch005openalex-snapshot
From Theory to Practice
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Hybrid evolutionary optimization for efficient placement of IoT applications in fog computing environments
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.