FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments (1710.01801v1)

Abstract: Smart city vision brings emerging heterogeneous communication technologies such as Fog Computing (FC) together to substantially reduce the latency and energy consumption of Internet of Everything (IoE) devices running various applications. The key feature that distinguishes the FC paradigm for smart cities is that it spreads communication and computing resources over the wired/wireless access network (e.g., proximate access points and base stations) to provide resource augmentation (e.g., cyberforaging) for resource and energy-limited wired/wireless (possibly mobile) things. Moreover, smart city applications are developed with the goal of improving the management of urban flows and allowing real-time responses to challenges that can arise in users' transactional relationships. This article presents a Fog-supported smart city network architecture called Fog Computing Architecture Network (FOCAN), a multi-tier structure in which the applications running on things jointly compute, route, and communicate with one another through the smart city environment to decrease latency and improve energy provisioning and the efficiency of services among things with different capabilities. An important concern that arises with the introduction of FOCAN is the need to avoid transferring data to/from distant things and instead to cover the nearest region for an IoT application. We define three types of communications between FOCAN devices (e.g., interprimary, primary, and secondary communication) to manage applications in a way that meets the quality of service standards for the IoE. One of the main advantages of FOCAN is that the devices can provide the services with low energy usage and in an efficient manner. Simulation results for a selected case study demonstrate the tremendous impact of the FOCAN energy-efficient solution on the communication performance of various types of things in smart cities.

• The paper presents a novel multi-tier fog computing framework that significantly reduces latency and energy consumption in smart city IoE applications.
• It details a hierarchical communication model—interprimary, primary, and secondary—to optimize data transfer and maintain high quality of service.
• Numerical simulations using the iFogSim platform demonstrate FOCAN’s superior performance over traditional cloud and D2D systems for real-time processing.

Insights into FOCAN: A Fog-supported Smart City Network Architecture

The paper "FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments" by Paola G. Vinueza Naranjo et al., presents a novel framework for efficiently managing smart city applications within Internet of Everything (IoE) environments. The proposed Fog Computing Architecture Network (FOCAN) is designed to address the critical requirements of latency reduction and energy efficiency in the context of IoE that conventional cloud computing struggles to satisfy.

Overview of FOCAN

FOCAN is introduced as a multi-tier architecture that integrates fog computing principles at the edge of the network, thereby providing efficient resource augmentation and cyberforaging capabilities. This architecture is particularly significant in smart cities where the proliferation of IoE devices demands low-latency and energy-efficient communication and computing solutions. FOCAN's primary contribution is its structure, composed of interconnected Fog Nodes (FNs), which manage application processes across varied device capabilities while ensuring reduced data traffic and processing delays.

Key Features

FOCAN processes application communication through a hierarchical model that primarily localizes processing tasks. It defines three types of communication: interprimary, primary, and secondary. This classification ensures that applications maintain requisite quality of service (QoS) standards by optimizing data transfer paths and proximity-based processing. Such a model benefits real-time, latency-sensitive applications prevalent in smart city environments, such as traffic management and health monitoring systems, which cannot accommodate the delays of cloud-centralized architectures.

Numerical and Simulation Insights

The paper presents robust numerical simulations using the iFogSim platform to substantiate FOCAN's claims of energy efficiency and enhanced service quality. The results showcased a marked improvement in average power consumption compared to legacy systems like device-to-device (D2D) platforms. Particularly, the paper specifies how FOCAN's routing algorithm and application flow management yield superior performance in task handling and energy expenditure.

Implications and Future Directions

Practically, FOCAN provides a scalable, energy-efficient platform for the burgeoning smart city infrastructure where IoE applications increasingly demand sophisticated, real-time data processing spanning vast urban networks. Theoretically, the introduction of a fog-based architecture serves as a significant push towards decentralizing computing resources, thereby reducing dependency on geographically distant cloud servers.

The paper opens avenues for further research and enhancement, particularly in integrating emerging technologies such as 5G and mobile edge computing to accommodate even higher data volumes and demand variability. Future research could explore optimizing the hierarchical structure to support increasingly complex IoT ecosystems, including diverse application types and user mobility patterns.

Conclusion

In summary, FOCAN represents a critical step towards realizing the potential of fog computing in smart city ecosystems. By addressing key limitations of existing cloud-based frameworks, it paves the path for more responsive and energy-aware IoE application management. The proposed architecture not only enhances computing efficiency but also contributes significantly to the sustainability of urban environments through intelligent resource management and reduced operational costs.