Distributed Intelligence Platform to the Edge Computing

With the explosion of information, devices, and interactions, cloud design on its own cannot handle the flow of data. While the cloud provides us access to compute, storage, and even connectivity that we can access easily and cost-effectively, these centralized resources can create delays and performance issues for devices and information that are far from a centralized public cloud or information center source. Internet of things-connected devices are a transparent use for edge computing architecture. In this chapter, the author discusses the main differences between edge, fog, and cloud computing; pros and cons; and various applications, namely, smart cars and traffic control in transportation scenario, visual and surveillance security, connected vehicle, and smart ID card.

Object Detection in Fog Computing Using Machine Learning Algorithms

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

Towards Efficient Resource Management in Fog Computing

Cloud computing is an emerging field. With its three key features and its natural favorable circumstances, it has had a few difficulties in the recent years. The gap between the cloud and the end devices must be reduced in latency specific applications (i.e., disaster management). Right now, fog computing is an advanced mechanism to reduce the latency and congestion in IoT networks. It emphasizes processing the data as close as possible to the edge of the networks, instead of sending/receiving the data from the data centre by using large quantity of fog nodes. The virtualization of these fog nodes (i.e., nodes are invisible to the users) in numerous locations across the data centres enabled the fog computing to become more popular. The end users need to purchase the computing resources from the cloud authorities to process their excessive workload. Since computing resources are heterogeneous and resource are constrained and dynamic in nature, allocating these resources to the users becomes an open research issue and must be addressed as the first priority.

Internet of Things and Fog Computing Applications in Intelligent Transportation Systems

Self-driving vehicles such as autonomous cars are manufactured mostly with smart sensors and IoT devices with artificial intelligence (AI) techniques. In most of the cases, smart sensors are networked with IoT devices to transmit the data in real-time. IoT devices transmit the sensor data to the processing unit to do necessary actions based on sensor output data. The processing unit executes the tasks based on pre-defined instructions given to the processor with embedded and AI coding techniques. Continuous streaming of sensors raw data to the processing unit and for cloud storage are creating a huge load on cloud devices or on servers. In order to reduce the amount of stream data load on the cloud, fog computing, or fogging technology, helps a lot. Fogging is nothing but the pre-processing of the data before deploying it into the cloud. In fog environment, data optimization and analytical techniques take place as a part of data processing in a data hub on IoT devices or in a gateway.

Analysis of Identity-Based Cryptography in Internet of Things (IoT)

Fog networking supports the internet of things (IoT) concept, in which most of the devices used by humans on a daily basis will be connected to each other. Security issues in fog architecture are still a major research area as the number of security threats increases every day. Identity-based encryption (IBE) has a wide range of new cryptographic schemes and protocols that are particularly found to be suitable for lightweight architecture such as IoT and wireless sensor networks. This chapter focuses on these schemes and protocols in the background of wireless sensor networks. Also, this chapter analyses identity-based encryption schemes and the various attacks they are prone to.

Security Issues in Fog Computing for Internet of Things

The inherent features of internet of things (IoT) devices, like limited computational power and storage, lead to a novel platform to efficiently process data. Fog computing came into picture to bridge the gap between IoT devices and data centres. The main purpose of fog computing is to speed up the computing processing. Cloud computing is not feasible for many IoT applications; therefore, fog computing is a perfect alternative. Fog computing is suitable for many IoT services as it has many extensive benefits such as reduced latency, decreased bandwidth, and enhanced security. However, the characteristics of fog raise new security and privacy issues. The existing security and privacy measures of cloud computing cannot be directly applied to fog computing. This chapter gives an overview of current security and privacy concerns, especially for the fog computing. This survey mainly focuses on ongoing research, security challenges, and trends in security and privacy issues for fog computing.

Agribot

This robotic vehicle is a farming machine of significant power and incredible soil clearing limit. This multipurpose system gives a propel technique to sow, furrow, water, and cut the harvests with the least labor and work. The machine will develop the ranch by considering specific line and a section settled at a fixed distance depending on the crop. Moreover, the vehicle can be controlled through voice commands connected via Bluetooth medium using an Android smartphone. The entire procedure computation, handling, checking is planned with engines and sensor interfaced with the microcontroller. The major modules of the vehicle are cultivating, sowing seeds, watering, harvesting the crop. The vehicle will cover the field with the help of the motors fixed which is being controlled with the help of the voice commands given by the user. The main motto of this project is to make the vehicle available and should be operated by everyone even without any technical knowledge.

Fog Resource Allocation Through Machine Learning Algorithm

Internet of things (IoT) prevails in almost all the equipment of our daily lives including healthcare units, industrial productions, vehicle, banking or insurance. The unconnected dumb objects have started communicating with each other, thus generating a voluminous amount of data at a greater velocity that are handled by cloud. The requirements of IoT applications like heterogeneity, mobility support, and low latency form a big challenge to the cloud ecosystem. Hence, a decentralized and low latency-oriented computing paradigm like fog computing along with cloud provide better solution. The service quality of any computing model depends on resource management. The resources need to be agile by nature, which clearly demarks virtual container as the best choice. This chapter presents the federation of Fog-Cloud and the way it relates to the IoT requirements. Further, the chapter deals with autonomic resource management with reinforcement learning (RL), which will forward the fog computing paradigm to the future generation expectations.

Cloud Security Architecture Based on Fully Homomorphic Encryption

Cloud computing is a new environment in computer-oriented services. The high costs of network platforms, development in client requirements, data volumes and weight on response time pushed companies to migrate to cloud computing, providing on-demand web facilitated IT services. Cloud storage empowers users to remotely store their information and delight in the on-demand high quality cloud applications without the affliction of local hardware management and programming administration. In order to solve the problem of data security in cloud computing system, by introducing fully homomorphism encryption algorithm in the cloud computing data security, another sort of information security solution to the insecurity of the cloud computing is proposed, and the scenarios of this application is hereafter constructed. This new security arrangement is completely fit for the processing and retrieval of the encrypted data, successfully prompting the wide relevant prospect, the security of data transmission, and the stockpiling of the cloud computing.