Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

TLDR

Analysis of IoT sensor data is essential for achieving city smartness. The study proposes a multitier fog computing model with large‑scale data analytics services for smart city applications. The multitier fog consists of ad‑hoc and dedicated fogs, employs Raspberry Pi nodes with a distributed engine, and implements QoS‑aware admission control, offloading, and resource allocation based on availability and cost models, all evaluated through a scalable simulator. Experiments demonstrate that the multitier fog delivers more efficient analytics services, markedly reducing job‑blocking probability and boosting service utility compared to a cloud‑only model, validating the proposed QoS schemes.

Abstract

Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.