Fog Nodes Research Articles

TCV-D: An Approach for Path Selection in Vehicular Task Offloading

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

LFTDA: A lightweight and fault-tolerant data aggregation scheme with privacy-enhanced property in fog-assisted smart grid

In recent years, the data aggregation scheme was widely adopted in smart grids (SG) to protect data privacy while ensuring data availability. Meanwhile, asymmetric homomorphic encryption is a popular technique that guarantees data aggregation and accurate computing results. However, it often brings heavy computation overheads and costs, which increases burdens to SG. In order to lighten SG’s burden, in this paper, a lightweight and fault-tolerant data aggregation scheme (LFTDA) with privacy-enhanced property is proposed. Unlike existing schemes, our proposed scheme uses a lightweight symmetric encryption algorithm and a skillful mathematical structure to achieve data aggregation. In addition, in the proposed scheme, smart meters are allowed to dynamically join and exit from the group while guaranteeing the correctness, security, and efficiency of aggregation. More importantly, our LFTDA is fault-tolerant, which means that the data collection from other devices will not be affected even if some of the smart meters or fog nodes do not work. From the view of security, LFTDA can defend against eavesdropping attack and collusion attack. Finally, we evaluate its performance along with other related schemes in terms of aggregation, decryption, and communication costs, which shows that LFTDA is very competitive and hence is suitable for many practical SG applications.

Deep hybrid model for attack detection in IoT-fog architecture with improved feature set and optimal training

IoT-Fog computing provides a wide range of services for end-based IoT systems. End IoT devices interface with cloud nodes and fog nodes to manage client tasks. Critical attacks like DDoS and other security risks are more likely to compromise IoT end devices while they are collecting data between the fog and the cloud layer. It’s important to find these network vulnerabilities early. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. However, deep learning cannot be carried out on Internet of Things devices because to their constrained calculation and storage capabilities. In this research, we suggest a three-stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Improved Z-score normalization-based data preparation will be carried out in the initial step. On the basis of preprocessed data, features like IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as input to hybrid classifiers dubbed optimized Deep Maxout and Deep Belief Network (DBN) in the third step of the process to classify the assaults based on the input dataset. A hybrid optimization model called the BMUJFO (Blue Monkey Updated Jellyfish Optimization) technique is presented for the best Deep Maxout training. Additionally, the suggested model produced higher accuracy, precision, sensitivity, and specificity results, with values of 95.26 percent, 94.84%, 96.28%, and 97.84%, respectively.

Secure Computing for Fog-Enabled Industrial IoT.

Smart cities are powered by several new technologies to enhance connectivity between devices and develop a network of connected objects which can lead to many smart industrial applications. This network known as the Industrial Internet of Things (IIoT) consists of sensor nodes that have limited computing capacity and are sometimes not able to execute intricate industrial tasks within their stipulated time frame. For faster execution, these tasks are offloaded to nearby fog nodes. Internet access and the diverse nature of network types make IIoT nodes vulnerable and are under serious malicious attacks. Malicious attacks can cause anomalies in the IIoT network by overloading complex tasks, which can compromise the fog processing capabilities. This results in an increased delay of task computation for trustworthy nodes. To improve the task execution capability of the fog computing node, it is important to avoid complex offloaded tasks due to malicious attacks. However, even after avoiding the malicious tasks, if the offloaded tasks are too complex for the fog node to execute, then the fog nodes may struggle to process all legitimate tasks within their stipulated time frame. To address these challenges, the Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT) is proposed for fog nodes. EEOIT proposes a mechanism to detect malicious nodes as well as manage the allocation of computing resources so that IIoT tasks can be completed in the specified time frame. Simulation results demonstrate that EEOIT outperforms other techniques in the literature in an IIoT setting with different task densities. Another significant feature of the proposed EEOIT technique is that it enhances the computation of trustable tasks in the network. The results show that EEOIT entertains more legitimate nodes in executing their offloaded tasks with more executed data, with reduced time and with increased mean trust values as compared to other schemes.

An energy-aware module placement strategy in fog-based healthcare monitoring systems

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

DSSPS: A dynamic secure service provision scheme in fog-based VANETs

In fog-based VANETs, fog nodes are close to the network edge that analyze data uploaded by vehicles, and then provide value-added services. It is suitable to adopt data sharing schemes to provide value-added services. However, applying existing data sharing schemes directly for service provision in fog-based VANETs has limitations in terms of security and efficiency. Therefore, we propose a dynamic secure service provision scheme in fog-based VANETs called DSSPS. Firstly, we construct an efficient subscription permission authentication mechanism that allows vehicles to request service from different fog nodes after a single subscription at TA. Secondly, a subgroup selecting algorithm is proposed, which effectively reduces the decryption overhead of vehicles. Formal security proof demonstrates DSSPS is secure. At the same time, DSSPS achieves better performance in terms of computation and communication costs.

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

SummaryIn this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS.

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.

Enhancing Energy Efficiency in Telehealth Internet of Things Systems Through Fog and Cloud Computing Integration: Simulation Study.

The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency. This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems. The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency. Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings. The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.

Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach

Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart farming. Nonetheless, the performance of these IoT applications is affected by their limited effectiveness in processing offloaded IoT requests originating from multiple IoT devices. Specifically, the requested IoT services are composed of multiple dependent microservice instances collectively referred to as a service plan (SP). Each SP comprises a series of tasks designed to be executed in a predefined order, with the objective of meeting heterogeneous Quality of Service (QoS) requirements (e.g., low service delays). Different from the cloud, selecting the appropriate service plan for each IoT request can be a challenging task in dynamic fog environments due to the dependency and decentralization of microservice instances, along with the instability of network conditions and service requests (i.e., change quickly over time). To deal with this challenge, we study the microservice instances selection problem for IoT applications deployed on fog platforms and propose a learning-based approach that employs Deep Reinforcement Learning (DRL) to compute the optimal service plans. The latter optimizes the delay of application requests while effectively balancing the load among microservice instances. In our selection process, we carefully address the plan-dependency to efficiently select valid service plans for every request by introducing two distinct approaches; an action masking approach and an adaptive action mapping approach. Additionally, we propose an improved experience replay to address delayed action effects and enhance our model training efficiency. A series of experiments were conducted to assess the performance of our Microservice Instances Selection Policy (MISP) approach. The results demonstrate that our model reduces the average failure rate by up to 65% and improves load balance by up to 45% on average when compared to the baseline algorithms.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this paper, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Energy-Efficient Fog Computing: A Review and Future Directions

Fog computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and bandwidth-intensive applications in the era of the Internet of Things (IoT) and edge computing. However, the proliferation of fog nodes introduces significant energy consumption challenges, particularly in resource-constrained environments. This paper presents a comprehensive review of energy-efficient techniques in fog computing, focusing on optimization strategies to minimize energy consumption while meeting application requirements. We categorize existing research efforts based on their approaches, including task scheduling, resource allocation, communication protocols, and energy harvesting techniques. Additionally, we identify key open challenges and propose future research directions to further enhance energy efficiency in fog computing environments.

An SDN-enabled fog computing framework for wban applications in the healthcare sector

For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing.

A secure and privacy preserved data aggregation scheme in IoMT

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment.

Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

Fog Computing-Based System for Decentralized Smart Parking System by Using Firebase

The growth of vehicle number is unavoidable whilst the availability of parking is not directly proportional with this condition. Nowadays, many shopping centers do not have sufficient parking spot, causing customers to have difficulty in finding available parking spots. Research has been conducted to tackle the issue of finding available parking spots. Much of this research proposed the narrowband-Internet of things (NB-IoT) as a fog node. For communication purposes, this NB-IoT-based fog node has some shortcomings, such as security and privacy, lower data rate, higher cost in development, dependency with wireless system, and only covers one area. In this research, the fog computing was proposed to decentralize smart parking system by using Firebase to cover several areas or malls in one system and interface. Instead of using NB-IoT, this research employed decentralized local server as a fog node to deliver a fast data exchange. Firestore database (Firebase) was also used to secure, manage, and analyze the data in the cloud. Conjunctively, the Android application was created as a user interface to book and find the availability of parking spots. The Android application was built using Android Studio and implemented authentication to keep the data access secure and private. The testing scenario was done following the design unified modeling language (UML). The research results confirmed that the fog computing system successfully supported the decentralized smart parking system and was able to be implemented for covering several areas or malls in one system.

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.