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Real-Time Scheduling in IoT Applications: A Systematic Review

https://doi.org/10.3390/s23010232
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.5581openalex-snapshot
A job scheduling algorithm for delay and performance optimization in fog computing
inferredcites →https://doi.org/10.1002/cpe.6163openalex-snapshot
Optimizing resource scheduling based on extended particle swarm optimization in fog computing environments
inferredcites →https://doi.org/10.1002/ett.3539openalex-snapshot
Task scheduling in cloud‐based survivability applications using swarm optimization in IoT
inferredcites →https://doi.org/10.1007/978-981-13-9364-8_8openalex-snapshot
Real-Time Scheduling Approach for IoT-Based Home Automation System
inferredcites →https://doi.org/10.1007/s00500-019-04220-yopenalex-snapshot
IoT transaction processing through cooperative concurrency control on fog–cloud computing environment
inferredcites →https://doi.org/10.1007/s10586-021-03327-yopenalex-snapshot
Orchestrating real-time IoT workflows in a fog computing environment utilizing partial computations with end-to-end error propagation
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/s11227-021-04170-zopenalex-snapshot
Mathematical model for the scheduling of real-time applications in IoT using Dew computing
inferredcites →https://doi.org/10.1007/s11227-022-04311-yopenalex-snapshot
Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment
inferredcites →https://doi.org/10.1007/s11277-021-09148-xopenalex-snapshot
Real Time Scheduling in TSCH for IoT Using Neurogenetic Algorithm
inferredcites →https://doi.org/10.1007/s12083-020-01051-9openalex-snapshot
Task scheduling in cloud-fog computing systems
inferredcites →https://doi.org/10.1007/s12652-020-02425-wopenalex-snapshot
A fire detection model based on power-aware scheduling for IoT-sensors in smart cities with partial coverage
inferredcites →https://doi.org/10.1007/s42452-019-1925-yopenalex-snapshot
Challenges and opportunities in IoT healthcare systems: a systematic review
inferredcites →https://doi.org/10.1016/j.apenergy.2019.114208openalex-snapshot
Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114892openalex-snapshot
iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings
inferredcites →https://doi.org/10.1016/j.compeleceng.2020.106740openalex-snapshot
Real-time scheduling under heterogeneous routing for industrial Internet of Things
inferredcites →https://doi.org/10.1016/j.compind.2019.06.002openalex-snapshot
Quantum-based predictive fog scheduler for IoT applications
inferredcites →https://doi.org/10.1016/j.dsx.2020.04.041openalex-snapshot
Internet of things (IoT) applications to fight against COVID-19 pandemic
inferredcites →https://doi.org/10.1016/j.future.2018.12.063openalex-snapshot
Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT
inferredcites →https://doi.org/10.1016/j.future.2019.09.039openalex-snapshot
Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing
inferredcites →https://doi.org/10.1016/j.jclepro.2018.02.061openalex-snapshot
The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines
inferredcites →https://doi.org/10.1016/j.jclepro.2021.126093openalex-snapshot
Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop
inferredcites →https://doi.org/10.1016/j.jmsy.2021.09.004openalex-snapshot
Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services
inferredcites →https://doi.org/10.1016/j.jnca.2018.07.007openalex-snapshot
A survey study on Internet of Things resource management
inferredcites →https://doi.org/10.1016/j.jnca.2022.103333openalex-snapshot
Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach
inferredcites →https://doi.org/10.1016/j.mex.2019.100777openalex-snapshot
Method for conducting systematic literature review and meta-analysis for environmental science research
inferredcites →https://doi.org/10.1108/ijicc-03-2020-0021openalex-snapshot
Adaptive task scheduling in IoT using reinforcement learning
inferredcites →https://doi.org/10.1109/access.2018.2866491openalex-snapshot
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions
inferredcites →https://doi.org/10.1109/access.2019.2961952openalex-snapshot
Green Fog Planning for Optimal Internet-of-Thing Task Scheduling
inferredcites →https://doi.org/10.1109/access.2020.2973758openalex-snapshot
Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing
inferredcites →https://doi.org/10.1109/access.2020.2999322openalex-snapshot
Performance Optimization of Control Applications on Fog Computing Platforms Using Scheduling and Isolation
inferredcites →https://doi.org/10.1109/access.2021.3068817openalex-snapshot
RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
inferredcites →https://doi.org/10.1109/jiot.2021.3064055openalex-snapshot
Age of Processing: Age-Driven Status Sampling and Processing Offloading for Edge-Computing-Enabled Real-Time IoT Applications
inferredcites →https://doi.org/10.1109/jstsp.2022.3144874openalex-snapshot
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems
inferredcites →https://doi.org/10.1109/meco.2019.8760040openalex-snapshot
A New Hardware-Accelerated Scheduler for Soft Real-Time Tasks
inferredcites →https://doi.org/10.1109/tetc.2020.3033672openalex-snapshot
Automata-Based Dynamic Fault Tolerant Task Scheduling Approach in Fog Computing
inferredcites →https://doi.org/10.1109/tgcn.2021.3056479openalex-snapshot
Energy-Aware Scheduling of Streaming Applications on Edge-Devices in IoT-Based Healthcare
inferredcites →https://doi.org/10.1109/tii.2020.3001067openalex-snapshot
Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications
inferredcites →https://doi.org/10.1109/tits.2018.2852646openalex-snapshot
Traffic Light Scheduling for Pedestrian-Vehicle Mixed-Flow Networks
inferredcites →https://doi.org/10.1109/tsc.2022.3155783openalex-snapshot
Real-Time Scheduling on Hierarchical Heterogeneous Fog Networks
inferredcites →https://doi.org/10.1145/3352700.3352713openalex-snapshot
Multi-objective Optimization of Real-Time Task Scheduling Problem for Distributed Environments
inferredcites →https://doi.org/10.1155/2021/6663199openalex-snapshot
A Smart Semipartitioned Real‐Time Scheduling Strategy for Mixed‐Criticality Systems in 6G‐Based Edge Computing
inferredcites →https://doi.org/10.3390/fi10060052openalex-snapshot
A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
inferredcites →https://doi.org/10.3390/math10071100openalex-snapshot
An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing
inferredcites →https://doi.org/10.3390/s21051666openalex-snapshot
Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing
inferredcites →https://doi.org/10.3390/s21165430openalex-snapshot
A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring
inferredcites →https://doi.org/10.3390/s22010108openalex-snapshot
Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing
inferredcites →https://doi.org/10.3390/su11010204openalex-snapshot
Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications
inferredcites →https://doi.org/10.3390/su11082192openalex-snapshot
An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

What points at this work

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

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Adaptive Statistical Round Robin (ASRR): Statistical Measures and Conditional Logic to Optimize System Responsiveness
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inferred← citeshttps://doi.org/10.1016/j.iot.2025.101775openalex-snapshot
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PFSA: A Predictive Fair Slot Allocation Mechanism for Dynamic IoT Environments
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