Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Fog computing: A taxonomy, systematic review, current trends and research challenges

Application placement in fog computing with AI approach : taxonomy and a state of the art survey

The rapid development of Internet of Things (IoT)-based applications and the era of 5G networks has led to an exponential increase in the amount of data required for processing the IoT services. The fog computing paradigm has emerged as a distributed computing solution for serving these applications using available fog nodes near the IoT devices. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) requirements that can be deployed on the fog nodes with different resource capabilities in the fog ecosystem, finding an efficient service placement plan is one of the challenging issues to be considered. In this paper, we propose an efficient IoT service placement solution based on the autonomic methodology for deploying IoT applications on the fog infrastructure. Our proposed solution monitors the QoS requirements of IoT services and capabilities of available fog nodes to determine an efficient service placement plan using the whale optimization algorithm (WOA) meta-heuristic technique. Besides, our evolutionary-based mechanism utilized the throughput and the energy consumption as objective functions for finding desirable IoT service placement plan while meeting the QoS requirements of each IoT service. Also, we develop an autonomous service placement framework according to a three-tier architecture of the fog ecosystem to show the interaction between the main components of the IoT device and fog layers for deploying IoT applications. The simulation results demonstrate that the proposed solution increases the resource usage and service acceptance ratio and reduces the service delay and the energy consumption compared with the other metaheuristic-based mechanisms. 

A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment

Towards edge computing in intelligent manufacturing: Past, present and future

Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment

There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.

Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

Internet of Thing (IoT) applications require an efficient platform for processing big data. Different computing techniques such as Cloud, Edge, and Fog are used for processing big data. The main challenge in the fog computing environment is to minimize both energy consumption and makespan for services. The service allocation techniques on a set of virtual machines (VMs) is the decidable factor for energy consumption and latency in fog servers. Hence, the service allocation in fog environment is referred to as NP-hard problem. In this work, we developed a hybrid algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve this NP-hard problem. The proposed GA-PSO is used for optimal allocation of services while minimizing the total makespan, energy consumption for IoT applications in the fog computing environment. We implemented the proposed GA-PSO using customized C++ simulator, and the results demonstrate that the proposed GA-PSO outperforms both GA and PSO techniques when applied individually.

GA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithm

Nowadays many Smart City applications make use of Internet of Things (IoT) devices for monitoring the environment. The increase in use of IoT for smart city applications causes exponential increase in the volume of data. Using centralised cloud for time sensitive IoT applications is not feasible due to more delay because of the network congestion. Hence, fog computing is used for processing the data near to the edge of the network, where processing is done by distributed network nodes. But, there is a challenge to select the fog nodes which can host and process the application modules. The placement of application module on these fog devices is known as NP-hard problem. Hence, we need better placement strategies to decide placement of application modules in fog infrastructure to minimize the application latency. In this paper, we design a First-Fit Decreasing (FFD) heuristic based approach for placing IoT application modules on Fog-Cloud and carried out the experiment using iFogsim simulator. The simulation results demonstrate that the proposed method shows significant decrease in both the application latency and energy consumption of Fog-Cloud as compared to the benchmark method.

Heuristic-Based IoT Application Modules Placement in the Fog-Cloud Computing Environment

Recent advancements in computer vision led to the development of a real-time surveillance system which ensures the safety and security of the people in public places. An aerial surveillance system will be advantageous in this scenario using a platform like Unmanned Aerial Vehicle (UAV) will be very reliable and can be considered as a cost-effective option for this task. To make the system fully autonomous, we require real-time abnormal event detection. But, this is computationally complex and time-consuming due to the heavy load on the UAV, which affords limited processing and payload capacity. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the large computation tasks to the cloud while keeping limited computation on-board UAV device using edge computing technique. Further, our proposed system will maintain the minimum communication between UAV and cloud. Thus it not only reduces the network traffic but also reduces the end-to-end delay. The proposed method is based on the state-of-the-art YOLO (You Only Look Once) technique for real-time object detection deployed on edge computing device using Intel neural compute stick Movidius VPU (Vision Processing Unit), and we applied abnormal event detection using motion influence map on the cloud. Experimental results demonstrate that the proposed system reduces the end-to-end delay. Further, Tiny YOLO is six times faster while processing the frames per second (fps) when compared to other state-of-the-art methods.

B V Natesha

Papers

Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment

Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

GA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithm

Heuristic-Based IoT Application Modules Placement in the Fog-Cloud Computing Environment

UAV based cost-effective real-time abnormal event detection using edge computing