Fog Based Frameworks for Iot/Iiot Service Placement and Data Analytics In Smart Application Environments

Abstract

There is an exponential increase in Internet of Things (IoT) devices in smart envi- ronments to monitor and control activities. The use of IoT devices in these environments increased the computational and storage resources requirement. Cloud computing pro- vides computational and storage resources, but it requires entire data to be transferred to the cloud. Using cloud computing for all IoT/Industrial IoT (IIoT) applications is not feasible as some of these applications are delay-sensitive and require service in real-time to avoid significant failures. Hence, a distributed fog computing architecture is developed to provide the computational and storage resources at the network edge to process and analyze the data. The main research challenges in a fog computing environ- ment are: to realize the fog computing infrastructure on resource constrained devices using the virtualization technique to provide the computational resources. Further, it is challenging to use these fog nodes for service placement and deploy a machine learn- ing model for real-time data analytics. This research work focuses on developing fog frameworks for IoT/IIoT service placement and the machine learning model deploy- ment to process and analyze the sensor data to reduce the service time and resource consumption and thus enable real-time monitoring of the smart environments. The Fog-Cloud computing environment is used to place the IoT/IIoT services based on the resource availability and deadline to address the above research challenges. The service placement problem in the fog-cloud computing environment is formulated as a multi-objective optimization problem and a novel cost-efficient deadline-aware ser- vice placement algorithm is developed to place the services on the Fog-Cloud resources to ensure the QoS of the IoT/IIoT services in terms of deadline, service cost and re- source availability. Using simulators or virtual machines based resource provisioning framework is not feasible as it takes more time and consumes more resources. Hence, the container-based fog computing framework is developed on 1.4 GHz 64-bit quad- core processor devices to realize the fog computing architecture on the resource con- strained devices. Further, the service placement problem in the fog computing envi- ronment is formulated as a multi-objective optimization problem and the meta-heuristic algorithms such as Elite Genetic Algorithm (EGA), Modified Genetic Algorithm with Particle Swarm Optimization (MGAPSO) and EGA with Particle Swarm Optimization (EGAPSO) are developed for IoT/IIoT service placement in the fog computing envi- ronment. The experimental results show that using a hybrid EGAPSO based service placement on the fog nodes reduces service time, cost and energy consumption. Using fog nodes for deploying the machine learning models to analyze the data re- duces the size of the data to be transferred to the cloud, which might reduce the network congestion, reduce the service time and thus enable to make quick decisions. The fog server-based framework is developed as a prototype for intelligent machine malfunction monitoring in the Industry 4.0 environment. The various supervised machine learning models are developed and deployed on the fog server at the network edge to analyze the data and thus enable real-time monitoring in the smart industry/Industry 4.0 envi- ronment. The fog server framework is used for industrial machine monitoring at Smart Industry/Industry 4.0 to detect and classify the machine as normal and abnormal using the machine operating sounds. The experimental results show the machine learning models’ performance for the various machines’ sounds recorded with different Sig- nal to Noise Ratio levels for normal and abnormal operations using Linear Prediction Coefficients and Mel Frequency Cepstral Coefficient audio features. Using fog server prototype for monitoring will reduce the total time and thus avoids the significant ma- chines failures in the industrial environment.