A Review of Quality of Service in Fog Computing for the Internet of Things

A Review of Quality of Service in Fog Computing for the Internet of Things

William Tichaona Vambe (University of Fort Hare, Alice, South Africa), Chii Chang (University of Melbourne, Melbourne, Australia) and Khulumani Sibanda (University of Fort Hare, Alice, South Africa)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJFC.2020010102
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With the advent of the paradigm of the Internet of Things, many computing elements need many modifications to promote Quality of Service (QoS). Quality of Service is a pillar that promotes real-time reaction to time-critical tasks. Any impediments to QoS should be resolved and handled. In 2012, fog computing was implemented to enhance QoS in current systems in a bid to tackle QoS problems encountered by using cloud computing alone. Currently, the primary focus in fog computing is now on enhancing QoS. The primary goal of this study is, therefore, to critically review and evaluate the literature on the work done to improve elements of QoS in fog computing. This study begins by examining the roots of history, characteristics, and advantages of fog computing. Secondly, it discusses the important elements of QoS parameters. Finally, open problems that still affect fog computing are identified and discussed in order to achieve enhanced QoS.
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The Internet of Things (IoT) is defined as a vibrant worldwide data network composed of internet-connected objects such as radio-frequency identifiers, sensors, and actuators, as well as other devices and smart devices that are becoming an essential part of the Internet (Perera, Liu, Jayawardena, & Chen, 2014). The word IoT can be traced back to the early 1990s when Kelvin Ashton introduced it (S. Albishi, Soh, Ullah, & Algarni, 2017). Over the years, IoT has received considerable attention due to the capacity to interact and execute some tasks together or react to incidents without specific instructions (Perera, Zaslavsky, Christen, & Georgakopoulos, 2014). Intelligence, Connectivity, Dynamic Scale, Enormous Nature, Sensing, Heterogeneity, and Security are the key fundamental characteristics which drive IoT (Ericsson, 2011). The above-mentioned features have contributed considerably to the successful adoption plus the use of IoT in current information systems and applications, creating value and support for human operations (Perera, Liu, et al., 2014). Collected works demonstrate that IoT has been implemented in various fields, leading to the development of smart cities, intelligent energy, and electrical grids, intelligent homes, smart buildings and infrastructure, intelligent health just to mention a few (Saad et al., 2017). This “smart world” has changed the manner in which people live and work by saving time and organizational resources whilst bringing new opportunities for knowledge formation, innovation, and development (Capossele, Cervo, Petrioli, & Spenza, 2016).

After the realization that the “things” that make up the IoT ecosystem have limited processing power and storage, cloud computing was introduced and integrated into IoT to provide scalable storage and processing services to meet IoT demands (Atlam, Walters, & Wills, 2018). In spite of cloud computing advantages in terms of storage and processing services, it still suffers mostly in providing low latency (Satria, Park, & Jo, 2017). This is because of its geographical location to the devices it wants to offer services. High latency compromise QoS which cause communication delays due to unstable and intermittent network connectivity. Explicitly, the unprecedented amount of data produced by IoT devices (Dastjerdi & Buyya, 2016) burden the network resulting in network transmission delays. Additionally, sending such huge data to and from the cloud requires exceptionally high network bandwidth (Atlam et al., 2018).

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