A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm

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.

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

“…Fog computing extends the cloud by migrating data processing closer to the production site, thereby accelerating the system’s responsiveness to events. The Fog infrastructure allows the management of multiple data from IoMT devices that send the information to the Fog node reducing the latency, response time or data delay [ 32 , 33 , 34 ]. Several low-cost applications using IoT and Fog devices can be found in the literature in order to streamline diagnostics.…”

IoT Provisioning QoS based on Cloud and Fog Computing

“…Some works were done on ready protocols or standards such as PRL and NDN to make them more feasible for provisioning QoS in IoT system [82,98]. In terms of the network model, most of the reviewed studies relied on FC paradigm to propose their schemes [79,81,83,[88][89][90]97]. The reviewed studies also included provisioning schemes for IoT environments that needed resources allocation for NFV [81,89,90,97].…”

“…Shaheen et al [79] pointed out that the considerable distance among users and end-devices expand the number of routers' hops, resulting in rising latency and network utilization. Consequently, infrastructure provisioning in real-time is obstructed, and the QoS is reduced when using remote FNs for outsourced applications.…”

“…Latency [79, 81, 82, 84 -86, 88, 91 -97] Network Usage [79, 87, 91 -94, 97, 98] Service Time [79,83,97] RAM Consumption [79] CPU Utilization [79] Stability [80, 91, [94] Scalability [80,88] Energy efficiency [80,82,90,93,95] Throughput [81,82,85,87,[91][92][93][94]98] Availability [81,[86][87][88][90][91][92] Reliability [82, 84, 87-93, 96, 98] Response Time [83,97] jitter [84,96] Device/Network Cost [88-[90, 97] V. CONCLUSION All the mentioned studies had legit problems to solve, and they addressed it with brilliant solutions. According to Table II, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage.…”

Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis

“…Intrabalanced LEACH (IBLEACH) [25] improves network lifetime by balancing the workload in the network. IBLEACH has introduced presteady state phase before the steady state phase.…”