All one needs to know about fog computing and related edge computing paradigms: A complete survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Fog Computing for the Internet of Things: A Survey

The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper.

A Survey on Edge Computing Systems and Tools

Cloud computing has become the de facto computing platform for application processing in the era of the Internet of Things (IoT). However, limitations of the cloud model, such as the high transmission latency and high costs are giving birth to a new computing paradigm called edge computing (a.k.a fog computing). Fog computing aims to move the data processing close to the network edge so as to reduce Internet traffic. However, since the servers at the fog layer are not as powerful as the ones in the cloud, there is a need to balance the data processing in between the fog and the cloud. Moreover, besides the data offloading issue, the energy efficiency of fog computing nodes has become an increasing concern. Densely deployed fog nodes are a major source of carbon footprint in IoT systems. To reduce the usage of the brown energy resources (e.g., powered by energy produced through fossil fuels), green energy is an alternative option. In this paper, we propose employing dual energy sources for supporting the fog nodes, where solar power is the primary energy supply and grid power is the backup supply. Based on that, we present a comprehensive analytic framework for incorporating green energy sources to support the running of IoT and fog computing-based systems, and to handle the tradeoff in terms of average response time, average monetary, and energy costs in the IoT. This paper describes an online algorithm, Lyapunov optimization on time and energy cost (LOTEC), based on the technique of Lyapunov optimization. LOTEC is a quantified near optimal solution and is able to make control decision on application offloading by adjusting the two-way tradeoff between average response time and average cost. We evaluate the performance of our proposed algorithm by a number of experiments. Rigorous analysis and simulations have demonstrated its performance.

Yong Dou

Papers

Adaptive Energy-Aware Computation Offloading for Cloud of Things Systems