When it comes to storage and computation of large scales of data, Cloud Computing has acted as the de-facto solution over the past decade. However, with the massive growth in intelligent and mobile devices coupled with technologies like Internet of Things (IoT), V2X Communications, Augmented Reality (AR), the focus has shifted towards gaining real-time responses along with support for context-awareness and mobility. Due to the delays induced on the Wide Area Network (WAN) and location agnostic provisioning of resources on the cloud, there is a need to bring the features of the cloud closer to the consumer devices. This led to the birth of the Edge Computing paradigm which aims to provide context aware storage and distributed Computing at the edge of the networks. In this paper, we discuss the three different implementations of Edge Computing namely Fog Computing, Cloudlet and Mobile Edge Computing in detail and compare their features. We define a set of parameters based on which one of these implementations can be chosen optimally given a particular use-case or application and present a decision tree for the selection of the optimal implementation.

Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas

Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.

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Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the Internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas, including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of AI of things (AIoT). This article presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: 1) perceiving; 2) learning; 3) reasoning; and 4) behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event’s location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments.

A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing

Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a mini-cloud, located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.

Jordi Garcia Almiñana

Papers

What is a Fog Node A Tutorial on Current Concepts towards a Common Definition