The Internet of Medical Things (IoMT) leverages 5 G-connected User Equipment (UE) to enable real-time health monitoring, transmitting patient data to hospital clouds for processing. However, this approach faces challenges in latency, resource efficiency, and energy use, critical for meeting 5 G's ultra-reliable low-latency communication (URLLC) demands in healthcare. This paper proposes an optimized task allocation framework for SDN-enabled 5 G IoMT networks, integrating fog computing to process tasks closer to the edge. Patient-generated tasks defined by data size, CPU cycles, and deadlines are intercepted by a dedicated fog broker, which applies a multi-objective optimization model to minimize latency (transmission and processing), load imbalance, and energy consumption across fog nodes. A heuristic solves this NP-hard problem, after which the SDN controller installs optimal routing paths between UEs and selected fog nodes. We evaluate the framework using NS3, Mininet, and Ryu simulations, comparing it against Round Robin and Nearest Fog Server baselines. Results show reductions in latency, deadline violations, and energy consumption, alongside improved load distribution. By harnessing SDN's centralized control and fog computing's proximity, our approach enhances QoS, scalability, and sustainability in IoMT, offering a robust solution for next generation healthcare systems.

Delay-sensitive IoT applications require an immediate response, while computing tasks in a fog-enabled network must be executed efficiently. Fog computing nodes are resource-limited and cannot handle more requests within the deadline. The process of task offloading continues to be difficult because of the user's mobility and the processing capabilities of the fog node. A new multi-level fog layer is proposed in the system architecture of the fog computing. In addition to this, this model utilizes the IoT device as a fog computing node based on certain criteria like computational capacity, mobility, etc. Random mobility prediction model is used to predict the future location of the user and the IoT device. A task offloading scheme that optimize the location aware module before engaging the device for task computation. Comprehensive MobFogSim simulations demonstrate that the proposed model and algorithm converge effectively, reducing latency and improving the user experience of handling sensitive applications by 20% compared to existing methods.

This paper proposes an innovative, hybrid approach to task scheduling and virtual machine (VM) placement in fog computing environments that aims to optimize energy efficiency and task completion for Internet of Things (IoT) applications. The proposed method combines Gorilla Troops Optimizer (GTO), a bio-inspired metaheuristic, with a resource-aware virtual machine placement strategy that allows for simultaneous optimization of task scheduling and virtual machine allocation. This integrated approach addresses key challenges in the allocation of IoT tasks by considering multiple objectives, such as latency, power consumption, and load balancing. A dynamic exploration-exploitation strategy and innovative fitness functionality have been employed to efficiently map tasks to fog nodes while minimizing task failure and suspension periods. Extensive simulations performed with iFogSim2 demonstrate the effectiveness of the proposed method and have achieved significant improvements over existing algorithms. The proposed approach is 18% better than ant colony optimization (ACO), 15% better than improved multi-objective differential evolution (IMODE), and 13% better than genetic and simulated annealing (GASA). These results underscore the effectiveness of the hybrid method in optimizing task scheduling and VM placement for dynamic and latency-sensitive applications in IoT. This work provides a scalable solution for fog computing systems that significantly improves service quality by optimizing resource consumption and reducing energy consumption, providing a promising approach to real-world IoT environments.

Background The rural locations of telemedicine and urgent care are slowed down by communication latency as a result of the poor network infrastructure and overreliance on centralized cloud computing that adds to time lag in responding. Even with the development of networking technology, reliable low-latency systems to support rural areas are yet to be developed. Methods The current paper suggests a Fog-5G Latency Optimization (F5GLO) application framework, which integrates 5G connectivity and fog computing to permit local data processing. To reduce the transmission delay and latency, healthcare data is stored at local fog nodes to allow predictive mobility of the fog node activation and low latency routing algorithms to utilize resources effectively and guarantee efficiency in transfer. Results The model is capable of cutting end to end latency by up to 87 percent in comparison to the conventional cloud-based models thereby enhancing critical healthcare applications such as remote patient monitoring and emergency medical services. It is also strong in various network traffic conditions. Conclusions Fog computing plus 5G networks introduce agility to the healthcare service delivery in the remote environment where quick processing of clinical data locally and transmission can improve the reliability of the given services by transmitting information to decision makers faster. This is a realistic incremental solution to the issue of healthcare provision to the populations that are not within reach of the giant facilities.

Cloud computing provides flexible and scalable services but suffers from several security challenges such as unauthorized access, data breaches, and insider attacks. This paper proposes a high-level security system using fog computing to enhance data protection. The system integrates user behavior profiling and decoy information technology to detect abnormal activities and mislead attackers. Encryption techniques such as AES and RSA are also used to ensure secure communication. The proposed system improves cloud security by preventing unauthorized access and protecting sensitive data. Key words: Fog Computing, Cloud Security, User Behavior Profiling, Decoy Technology, AES, RSA

Vehicular Fog Computing (VFC) is significantly enhancing the efficiency, safety, and computational capabilities of Intelligent Transportation Systems (ITS), and the integration of Unmanned Aerial Vehicles (UAVs) further elevates these advantages by incorporating flexible and auxiliary services. This evolving UAV-integrated VFC paradigm opens new doors while presenting unique complexities within the cooperative computation framework. Foremost among the challenges, modeling the intricate dynamics of aerial-ground interactive computing networks is a significant endeavor, and the absence of a comprehensive and flexible simulation platform may impede the exploration of this field. Inspired by the pressing need for a versatile tool, this paper provides a lightweight and modular aerial-ground collaborative simulation platform, termed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AirFogSim</monospace>. We present the design and implementation of AirFogSim, and demonstrate its versatility with five key missions in the domain of UAV-integrated VFC. A multifaceted use case is carried out to validate AirFogSim's effectiveness, encompassing several integral aspects of the proposed AirFogSim, including UAV trajectory, task offloading, resource allocation, and blockchain. In general, AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains. Our code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhiweiWei-NAMI/AirFogSim</uri>.

High self-adaptive task offloading framework in vehicular fog networks: A hybrid approach leveraging case-based reasoning and integer linear programming

ABSTRACT The exponential growth of the Internet of Things (IoT) and Internet of Medical Things (IoMT) has imposed significant demands on cloud‐centric architectures, particularly in terms of latency, energy efficiency, and scalability. Fog computing emerges as a promising paradigm by decentralizing computation and storage closer to data sources. This paper presents and validates a unified framework that integrates multi‐objective metaheuristic optimization for computation offloading and task scheduling with graph‐based database architectures for real‐time IoMT data management. The key novelty lies in the system‐level integration of these components, creating a feedback‐aware architecture where scheduling decisions explicitly account for database performance, rather than proposing new optimization algorithms in isolation. The study leverages enhanced metaheuristic algorithms such as Multi‐objective Arithmetic Optimization Algorithm (MoAOA) and Henon‐Evoked Rhinopithecus Swarm Optimization (HERSOA) to optimize energy consumption, latency, and throughput while ensuring reliable task prioritization and resource allocation. A formal data‐layer latency model is introduced and validated, with the optimization framework explicitly incorporating database performance metrics. Through comprehensive simulation, ablation studies, and statistical validation, we demonstrate that the combination of advanced optimization techniques and graph‐based fog architectures significantly improves QoS parameters, system stability, and scalability in dynamic IoT environments. This work provides a foundational guideline for future research and practical deployments in smart healthcare, smart cities, and industrial IoT.

As a result of the increase in industrial Internet of Things (IoT) applications, fog computing (FC) has become a major area of research. A decentralized computing system called fog computing extends cloud computing to the network’s edge. The cloud allows for real-time insights and analysis by processing and storing enormous volumes of data produced by IoT devices. Consequently, the task scheduling technique in cloud computing is crucial. A number of metrics, such as makespan, resource utilization, and energy consumption, must be optimized for FC to function efficiently. This paper proposes a novel metaheuristic optimization technique called the Gaussian Black-winged Kite Algorithm (GBKA) to address task scheduling optimization of industrial IoT applications in a fog computing environment. The proposed algorithm employs Gaussian mutation, and the migration patterns and attack style of the black-winged kite serve as the inspiration for the proposed GBKA. The algorithm is designed to balance exploration of the search space and exploitation of the best solutions, avoiding local optima and improving energy efficiency. The Google Cloud Jobs dataset (GoCJ) with varying task sizes is used to validate the proposed algorithm. An analysis has been conducted to compare the performance of the proposed algorithm with the standard Black-winged Kite Algorithm (BKA) and metaheuristic algorithms like Dragonfly Algorithm (DA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Experimental results show that GBKA reduces energy and makespan by an average of 7.26 and 9.32%, respectively. Additionally, it attains optimal resource utilization with an average overall improvement of 8.54%.

The Internet of Things (IoT) has emerged as a transformative force in residential automation, enabling smart home systems that interconnect sensors, actuators, and computing infrastructure to deliver improved convenience, energy efficiency, and security. This paper presents a structured review of IoT-based smart home automation systems, synthesizing findings from five open-access research works covering system architectures, wireless communication protocols, hardware platforms, software frameworks, and cybersecurity. Key findings indicate that Wi-Fi and ZigBee dominate current deployments due to their complementary power and bandwidth profiles; fog computing is reshaping gateway architectures toward lower latency and greater resilience; ESP8266/ESP8285 microcontrollers and Raspberry Pi boards constitute the most widely adopted hardware platforms for cost-sensitive deployments; and cybersecurity particularly authentication, encryption, and device vulnerability management, remains the most critical unresolved challenge. Future directions including AI-driven automation, standardized interoperability, and privacy-preserving architectures are discussed. Keywords— Internet of Things; smart home automation; Wi-Fi; wireless sensor networks.

A Comprehensive Survey on Fog Computing: Architectures, Techniques, Challenges, and Future Directions

Software-Defined Networking (SDN) has emerged as a revolutionary paradigm. The integration of SDN within fog networks represents a synergistic convergence of two cutting-edge technologies. With the complexity of SDN serving fog networks, the optimization of communication cost becomes paramount. Addressing the intricate challenges of communication cost optimization necessitates the application of sophisticated methodologies. Multi-Objective Optimization (MOO) algorithms present a robust solution, allowing for the simultaneous optimization of multiple conflicting objectives. By employing MOO, this research proposes a bi-objective optimization model for the intra- and inter-domain communication cost of controller deployment in an SDN-based computing network. The evaluation performed has captured two aspects of the performance of using Binary Angle quantization Multi-objective Particle swarm optimization (BAMP) and Binary crowding Distance Angle quantization Multi-objective Particle swarm optimization (BDAMP) for SDN controllers’ deployment. The first aspect is multi-objective-based evaluation, and the second aspect is the SDN network performance. Our developed BAMP and BDAMP have shown superiority over the benchmarks in terms of both aspects. Most importantly, the best performance is achieved by BDAMP in terms of both intra- and inter- communication cost.

Fog Computing: A Comprehensive Review on Security Analysis, Attacks, and Future Prospects

The Coffee Berry Borer is the most destructive pest affecting global production of Coffea arabica. Early detection of pest-induced fruit damage remains challenging due to the small size of infestation symptoms and the dense clustering of coffee berries under complex field conditions. This study evaluates optimized object detection architectures designed to improve the balance between detection accuracy and computational efficiency. Three baselines were established: YOLOv8n (M0), YOLOv11n (M1), and YOLOv26n (M2). Seven architectural variants (M3-M9) were then developed by integrating FasterNet, SimSPPF, and EMA. Experimental results showed that M0 achieved the highest detection accuracy (mAP@0.5 = 0.9534 and 6.09 GFLOPs), whereas model M6, combining FasterNet and SimSPPF, provided the best accuracy-efficiency trade off with mAP@0.5 = 0.9446 and 5.12 GFLOPs. Pareto analysis confirmed M6 as the optimal configuration. Finally, in situ validation across 25 points achieved a mean F1-score of 0.7255 (SD = 0.0504) for infected berries despite cast shadows, proving its readiness for real-time agricultural deployment.

Service Placement in Fog Computing: A Systematic Review of Issues, Techniques and Multi-Objective Optimization Approaches

A trust-based incentive mechanism and resource allocation in fog networks

A Collaborative Audit Scheme of IoT Data Integrity for Fog Computing

Knowledge-based optimization and reasoning for intelligent task offloading in dynamic vehicular fog networks

This paper addresses a scientific and applied problem of ensuring the energy resilience of critical medical infrastructure operating under conditions of extreme resource scarcity and instability of external power supply caused by deliberate destruction of the energy system. Unlike conventional Building Energy Management Systems (BEMS), which are primarily focused on economic efficiency in stable environments, the proposed approach shifts the focus toward maximizing the autonomous operation time of life-support equipment.The methodological foundation of the study is the development of a hybrid decentralized model for adaptive energy resource prioritization implemented within an Edge–Fog Computing architecture. This architectural design ensures full system autonomy: TinyML models deployed at the Edge level provide real-time anomaly detection, while strategic decision-making is performed by a Deep Reinforcement Learning (DRL) agent at the Fog node level, without reliance on cloud services. This eliminates a critical single point of failure and significantly enhances data privacy.The scientific novelty of the work lies in the introduction of an ethically oriented reward function for a Deep Q-Network (DQN) agent, which mathematically formalizes the prioritization of the bioethical principles of Beneficence and Non-Maleficence through the integration of risk categories defined by the NFPA 99 standard. The model employs a hard prioritization mechanism in which the unconditional supply of critical loads prevails over patient comfort and operational costs, while also accounting for physical system constraints, such as minimum battery charge levels, by incorporating elements of Constrained Reinforcement Learning.The proposed model was validated in the simulation environment HospitalEnergyEnv using the “Blackout-48” scenario. Comparative simulation results demonstrated that the proposed DQN agent maintained 100% operational availability of critical medical equipment throughout 48 hours of autonomous operation by proactively switching to a deep energy-saving mode. In contrast, traditional Greedy and Rule-Based algorithms depleted available resources at the 32nd and 41st hours, respectively, resulting in emergency power loss in the intensive care unit.The practical significance of the obtained results lies in the development of a deployment-ready architecture for autonomous hospital energy systems capable of adaptively allocating limited resources during prolonged blackouts while ensuring patient safety in accordance with international standards.

Towards 5G-enabled vehicular communication systems, message dissemination in a mobile, low-latency environment, like public safety, is critical to the timely and reliable delivery of emergency messages. It is well-known that classical cryptographic primitives such as RSA and ECC are no longer secure (feasible) in the future when QC is practical. Proposition 1 In the spirit of such observations, this paper provides a lightweight post-quantum authentication scheme for emergency message propagation in vehicular fog networks. The proposed technique relies on CRYSTALSDilithium, a NIST-standard post-quantum lattice-based digital signature scheme, to achieve quantum-safe authentication at low cost. The protocol has four phases: system initialization, vehicle emergency token generation, fog-level validation, and distributed revocation. It adopts a semi-trusted architecture consisting of certified On-Board Units (OBUs), roadside fog nodes, and a central authority. Empirical results show a 2.65 ms signing time, 1.43 ms verification time, and 3.2 KB token size, all leading to prototyping in 5G network real-time applications. Experimental results reveal &gt;30% reduction of computational delay with lower communication overhead compared with other existing schemes and without sacrificing security. The results demonstrate that the proposed scheme achieves post-quantum security, scalable revocation, and the preservation of privacy in fog computing environments that are characterised by limited computing capability. This paper presents a practical, future-proof authentication model well-suited for emergency response in next-generation vehicular networks.