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.

中文翻译：

---

轻量级的位置感知雾框架（LAFF），用于物联网范例中的QoS

物联网（IoT）的实现无所不在地彻底改变了连接性和可达性的范围。在物联网的保护下，每个足够智能以与其他对象通信的对象都会导致生成大小和性质各异的巨大数据。云计算（CC）使用集中式数据中心来提供远程服务和资源。但是，由于远离客户端设备，CC有其自身的局限性，特别是对于时间和资源要求严格的应用程序。CC的远程和集中式特性通常会导致产生瓶颈，潜在问题，从而降低服务提供中的服务质量（QoS）。这里，出现了雾计算（FC）概念，该概念倾向于利用CC和终端设备以分布式和分散的方式在本地进行数据拥塞和处理。但是，为时间紧迫的应用程序解决延迟和瓶颈问题仍然充满挑战。在这项工作中，提出了一个轻量级框架，该框架采用了雾头节点的概念，该雾头节点在用户注册和位置感知方面跟踪其他雾节点。所提出的轻量级位置感知雾框架（LAFF）通过提供准确的位置感知算法来持久满足QoS。还提供了比较分析来分析网络使用率，服务时间，延迟以及RAM和CPU利用率。比较结果表明，LAFF分别将延迟，网络使用和服务时间减少了11.01％，7.51％和14.8％。与最新框架相反。此外，考虑到RAM和CPU利用率，建议的框架取代了针对物联网应用的IFAM和TPFC。与IFAM和TPFC相比，RAM消耗和CPU利用率分别降低了8.41％和16.23％，从而使该框架轻巧。因此，提出的LAFF在为雾计算中的外包应用访问远程计算服务器时提高了QoS。