Abstract
Realization of Internet of Things (IoT) has revolutionized the scope of connectivity and reachability ubiquitously. Under the umbrella of IoT, every object which is smart enough to communicate with other object leads to the enormous data generation of varying sizes and nature. Cloud computing (CC) employs centralized data centres for the provisioning of remote services and resources. However, for the reason of being far away from client devices, CC has their own limitations especially for time and resource critical applications. The remote and centralized characteristics of CC often result in creating bottle necks, being latent, and hence deteriorate the quality of service (QoS) in the provisioning of services. Here, the concept of fog computing (FC) emerges that tends to leverage CC and end devices for data congestion and processing locally in a distributed and decentralized way. However, addressing latency and bottleneck issues for time critical applications are still challenging. In this work, a lightweight framework is proposed which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location awareness. The proposed lightweight location-aware fog framework (LAFF) persistently satisfies QoS by providing an accurate location-aware algorithm. A comparative analysis is also presented to analyse network usage, service time, latency, and RAM and CPU utilization. The comparison results depicts that the LAFF reduces latency, network use, and service time by 11.01%, 7.51%, and 14.8%, respectively, in contrast to the state-of-the-art frameworks. Moreover, considering RAM and CPU utilization, the proposed framework supersedes IFAM and TPFC targeting IoT applications. The RAM consumption and CPU utilization are reduced by 8.41% and 16.23% as compared with IFAM and TPFC, respectively, making the framework lightweight. Hence, the proposed LAFF improves QoS while accessing remote computational servers for the outsourced applications in fog computing.
Highlights
Introduction e concept ofInternet of ings (IoT), supported by computational intelligence, has revolutionized in almost all domains of life
We propose a lightweight location-aware fog framework (LAFF) which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location. e proposed LAFF persistently improves quality of service (QoS) by employing location-aware algorithm
Results and Discussion e lightweight LAFF is compared with two other fog-based frameworks: IFAM and TPFC [7, 8]. e primary motivation behind this evaluation is to confirm the adequacy of the LAFF in terms of reducing latency, service time, and network use to facilitate users by providing better QoS
Summary
Internet of ings (IoT), supported by computational intelligence, has revolutionized in almost all domains of life. Many new applications and domains in IoT and computational intelligence are emerging to help mankind in one way or other. On the other hand, providing such applications to general public has opened new horizons of business as well. In IoT, such businesses and applications mostly rely on the sensory data that has to be gathered for effective decision making. For fusion or manipulating big data (that may be some streaming data or in shape of batches), there are some requirements, i.e., distributed processing capability, effective communication and uncompromised network so that decision making process may yield better accuracy. Clouds being service providers tend to solve these problems. For the reason of being faraway from client
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