Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering

Abstract

Large-scale IoT applications with dozens of thousands of geo-distributed IoT devices creating enormous volumes of data pose a big challenge for designing communication systems that provide data delivery with low latency and high scalability. In this paper, we investigate a hierarchical Edge-Cloud publish/subscribe brokers model using an efficient two-tier routing scheme to alleviate these issues when transmitting event notifications in wide-scale IoT systems. In this model, IoT devices take advantage of proximate edge brokers strategically deployed in edge networks for data delivery services in order to reduce latency. To deliver data more efficiently, we propose a proactive mechanism that applies collaborative filtering techniques to efficiently cluster edge brokers with geographic proximity that publish and/or subscribe to similar topics. This allows brokers in the same cluster to exchange data directly with each other to further reduce data delivery latency. In addition, we devise a coordinative scheme to help brokers discover and bridge similar topic channels in the whole system, informing other brokers for data delivery in an efficient manner. Extensive simulation results prove that our model can adeptly support event notifications in terms of low latency, small amounts of relay traffic, and high scalability for large-scale, delay-sensitive IoT applications. Specifically, in comparison with other similar Edge-Cloud approaches, our proposal achieves the best in terms of relay traffic among brokers, about 7.77% on average. In addition, our model’s average delivery latency is approximately 66% of PubSubCoord-alike’s one.

Flextory: Flexible Software Factory of IoT Data Consumers

“…The main weakness of the generated FLEX-consumers is, presumably, that it can only receive data from one message broker and from one topic (or queue). For instance, in [ 33 ], the authors present an IoT Edge-Cloud hybrid architecture in which consumers have to dynamically connect to different message brokers according to different conditions. In order to adapt FLEX-consumers to new IoT architectures or even to a changing environment, we can adopt solutions from the SPL community, such as [ 5 , 6 ], that require different models (e.g., variability and goal models) to generate IoT applications.…”

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

“…TRAINING in this period is identified as a highly developed and isolated topic. This term captures the process of teaching and preparing an IoT system to perform specific tasks [20,62]. This involves using algorithms and machine learning models to analyse the data collected by IoT devices and extract relevant information.…”

“…This helps to identify patterns, trends, and problems and provides recommendations to optimise traffic flows, reduce congestion, and improve the efficiency of the overall transportation system [29]. In this sense, according to Pharm V. et al [62], the mobility and transportation area in a smart city can take advantage of these collaborative filtering and knowledge tools as hybrid filtering in several ways:…”

A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

“…The event driven service dynamic collaboration mechanism is founded on the event-driven architecture and utilizes a publish/subscribe system to connect multiple services in alignment with the producer and consumer patterns [18]. Services can serve as producers for generating and publishing event streams, as well as consumers for subscribing to events of interest.…”