Multi-tier fog computing with large-scale IoT data analytics for smart cities

Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multi-tier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multi-tier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. QoS aware admission control, offloading and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multi-tier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing

The goal of the Internet of Things (IoT) is to convert the physical world into a smart space in which physical objects, called things, are equipped with computing and communication capabilities. Those things can connect with anything, anyone at any time, any space via any network or service. The predominant Internet of Things (IoT) system model today is cloud centric. This model introduces latencies into the application execution, as data travels first upstream for processing and secondly the results, i.e., control commands, travel downstream to the devices. In contrast with the cloud-model, the cloud-fog-based model pushes computing capability to the edge of the network, which is closer to the data sources. This enables lower latency and a faster response time. The end-device can directly receive the service from the fog node instead of sending all the data to the central cloud server. In addition, with the application of microservice containerization technology, fog nodes can quickly set up various environments for heterogeneous services. Compared with cloud computing, fog computing needs to consider users’ mobility and geographic location. The application scenarios that fog computing is more dynamic and flexible. Therefore, fog computing requires real-time data monitoring and service management. In this thesis, we will explore how to deploy fog computing resources, what data is needed in the deployment process, and how to implement data monitoring

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin

Simulating IoT Workflows in DISSECT-CF-Fog

The modelling of IoT applications utilising the resources of cloud and fog computing is not straightforward because they have to support various trigger-based events that make human life easier. The sequence of tasks, such as performing a service call, receiving a data packet in the form of a message sent by an IoT device, and managing actuators or executing a computational task on a virtual machine, are often associated with and composed of IoT workflows. The development and deployment of such IoT workflows and their management systems in real life, including communication and network operations, can be complicated due to high operation costs and access limitations. Therefore, simulation solutions are often applied for such purposes. In this paper, we introduce a novel simulator extension of the DISSECT-CF-Fog simulator that leverages the workflow scheduling and its execution capabilities to model real-life IoT use cases. We also show that state-of-the-art simulators typically omit the IoT factor in the case of the scientific workflow evaluation. Therefore, we present a scalability study focusing on scientific workflows and on the interoperability of scientific and IoT workflows in DISSECT-CF-Fog

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy-efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes that provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements