Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

Ferrández-Pastor, Francisco-Javier; Mora, Higinio; Jimeno-Morenilla, Antonio; Volckaert, Bruno

doi:10.3390/su10113832

Cited by 86 publications

(53 citation statements)

References 38 publications

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“…Ferrández-Pastor et al [94] proposed a method for smart devices based on edge computing to tackle the issues related to scalability of services and interoperability. The model has automated the rule designing for better decision making based on installation behavior.…”

Section: Edge Computing and Fog Computing-based Techniques In Smart Hmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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Section: Edge Computing and Fog Computing-based Techniques In Smart Hmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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“…A design method for a smart building (where various subsystems must be integrated to develop intelligent facilities), comprised of a two-level architecture (edge and fog layers) and embedded devices in edge or fog nodes, was proposed and implemented in [25]. A resource preservation net (RPN) framework for emergency department (ED) systems was proposed in [26].…”

Section: Edge Computing-based Solutionsmentioning

confidence: 99%

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

2020

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Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

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“…Few studies assume a flexible mapping of data sources over the fog nodes. The study by the authors of [25] proposes a framework to design services for smart building-based on edge and fog computing paradigms, where the IoT sensors communicate data through the MQTT protocol: this study assumes a potential flexible mapping for design purposes but does not propose any specific (optimized) solution to address this issue. The authors of [26,27] propose a coordination scheme between cloud and fog nodes applied to a healthcare-driven IoT application and to a real-time streaming application, respectively.…”

Section: Related Workmentioning

confidence: 99%

GASP: Genetic Algorithms for Service Placement in Fog Computing Systems

Canali

Lancellotti

2019

Algorithms

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Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.

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Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

Cited by 86 publications

References 38 publications

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

GASP: Genetic Algorithms for Service Placement in Fog Computing Systems

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