FROG: A Robust and Green Wireless Sensor Node for Fog Computing Platforms

Abstract

Over the past few years, we have witnessed the widespread deployment of wireless sensor networks and distributed data management facilities: two main building blocks of the Internet of things (IoT) technology. Due to the spectacular increase on the demand for novel information services, the IoT-based infrastructures are more and more characterized by their geographical sparsity and increasing demands giving rise to the need of moving from a cloud to a fog model: a novel deployment paradigm characterized by the provisioning of elastic resources geographically located as close as possible to the end user. Despite the large number of wireless sensor networks already available in the market, there are still many issues to be addressed on the design and deployment of robust network platforms capable of meeting the demand and quality of fog-based systems. In this paper, we undertake the design and development of a wireless sensor node for fog computing platforms addressing two of the main issues towards the development and deployment of robust communication services, namely, energy consumption and network resilience provisioning. Our design is guided by examining the relevant macroarchitecture features and operational constraints to be faced by the network platform. We based our solution on the integration of network hardware platforms already available on the market supplemented by smart power management and network resilience mechanisms.

An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

“…As it has been observed, one of the main fundamentals to deploy a fog computing architecture is to reduce the latency in the final applications. Likewise, we can observe that the enhancement of this metric entails improvements in different ones, such as, for example, the reduction of energy consumption [31], improving the QoS [32], maximising the Quality of Experience (QoE) [33], among others. In this sense, for the analysis of the distribution of computational resources it is necessary to be able to evaluate this type of architectures.…”

“…2). The Fog Node is the point of link between the edge level and core level of the platform, besides being able to analyse and make decisions [31]. Therefore, the Fog Node in an IoT network has the main role of acquiring data sensed by the end-points and collected by the gateways, analysing them and taking actions, that is, sending them to the Cloud or notifying the end users.…”

Wireless Sensor Network Synchronization for Precision Agriculture Applications

“…To support the main cloud infrastructure and achieve reduced response time and even distribution of the network load, a subject of critical importance for system success, especially with the future coming of 5G technologies, the research community has turned to alternative solutions that take advantage of the fog computing paradigm to propose new hybrid cloud/fog models. For instance, Castillo-Cara et al [70] proposed a scalable network architecture for monitoring and managing farms in rural areas that embeds fog computing capabilities to increase coverage and throughput. To even further enhance agricultural automation and remote sensing, these systems require processing of enormous amounts of remotely sensed data from different platforms and, therefore, greater attention is currently being devoted to machine learning methods and artificial intelligence algorithms [71,72].…”

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

“…Many works have been reported in the literature that attempt to tackle this obstacle. An example is the work found in [79], where a robust and green WSN node for fog platforms, called "FROG", was presented for efficient power management in smart farming environments. In the current project, the WSN power consumption limitation is counterbalanced with the use of low-energy consumption sensory nodes (i.e., Arduino Uno devices), that have been proven to be power-efficient (e.g., in [55]), along with attached power banks and load distribution mechanics, which push the energy boundaries, allowing for increased reliability.…”