Fog Computing with the Integration of Internet of Things: Architecture, Applications and Future Directions

Wadhwa, Heena; Aron, Rajni

doi:10.1109/bdcloud.2018.00144

Cited by 29 publications

(18 citation statements)

References 15 publications

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“…A fog computing environment comprises with devices of different characteristics. To maintain an uninterrupted connection among these devices, a proper integration and management scheme is necessary [25]. Fogernetes, a fog computing platform that enables management and deployment of fog applications, is presented in [26].…”

Section: Related Workmentioning

confidence: 99%

A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment

et al. 2020

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Recently, IoT (Internet of Things) has been an attractive area of research to develop smart home, smart city environment. IoT sensors generate data stream continuously and majority of the IoT based applications are highly delay sensitive. The initially used cloud based IoT services suffers from higher delay and lack of efficient resources utilization. Fog computing is introduced to improve these problems by bringing cloud services near to edge in small scale and distributed nature. This work considers an integrated fogcloud environment to minimize resource cost and reduce delay to support real-time applications at a lower operational cost. We first present a cooperative three-layer fog-cloud computing environment, and propose a novel optimization model in this environment. This model has a composite objective function to minimize the bandwidth cost and provide load balancing. We consider balancing load in both links' bandwidth and servers' CPU processing capacity level. Simulation results show that our framework can minimize the bandwidth cost and balance the load by utilizing the cooperative environment effectively. We assign weight factors to each objective of the composite objective function to set the level of priority. When minimizing bandwidth cost gets higher priority, at first, the demand generated from the traffic generator sensors continues to be satisfied by the regional capacity of layer-1 fog. If the demand of a region goes beyond the capacity of that region, remaining demand is served by other regions layer-1 fog, then by layer-2 fog, and finally by the cloud. However, when load balancing is the priority, the demand is distributed among these resources to reduce delay. Link level load balancing can reduce the queueing delay of links while server level load balancing can decrease processing delay of servers in an overloaded situation. We further analyzed how the unit bandwidth cost, the average and maximum link utilization, the servers' resources utilization, and the average number of servers used vary with different levels of priority on different objectives. As a result, our optimization formulation allows tradeoff analysis in the cooperative three-layer fog-cloud computing environment. INDEX TERMS Fog Computing, IoT, Optimal Resource management, Load Balancing, Task Offloading I. INTRODUCTION Internet of Things (IoT) devices such as home voice controllers, smart TVs, smart locks in smart homes, road traffic and air quality monitors in smart cities, continuous glucose monitor, and fitness bands in healthcare are only a few examples, which are introduced in recent years. A huge growth in the number of IoT devices equipped with sensors is observed recently. These sensors collect the data from these devices. Later, the collected raw data is used to produce aggregated information and to take automated decision. It is projected that 26 billion IoT devices are to be installed by 2020 [1], and 75.44 billion by 2025 [2]. This is a fivefold increase from

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Section: Related Workmentioning

confidence: 99%

A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment

et al. 2020

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“…IoT, транспортные средства [34], «умный» город [11], здравоохранение [23], «умная» доставка (в том числе с использование использованием беспилотников) [74], подземная съемка в реальном времени, видеонаблюдение и др. [82]…”

Section: туманные и краевые вычисленияunclassified

“…В парадигме туманных вычислений туманные узлы размещаются на границе локальной сети, зачастую они разворачиваются на базе роутеров, беспроводных точек доступа (если данные устройства поддерживают требуемые технологии для разворачивания туманного узла) [74]. В отличие от туманных вычислений, краевые вычисления размещаются еще «ближе» к конечным устройствам, уже внутри самой локальной сети на промежуточных точках доступа, а иногда и сами конечные устройства могут выступать краевыми вычислительными узлами: смартфоны, планшеты, другие вычислительные устройства с достаточными вычислительными возможностями и поддержкой развертывания вычислительных узлов [70].…”

Section: понятия туманных и краевых вычисленийunclassified

Overview of Fog Computing Organization Technologies

2020

CMSE

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Поскольку Интернет вещей (IoT) становится частью нашей повседневной жизни, наблюдается быстрый рост числа подключенных устройств. Устоявшийся подход, основанный на технологиях облачных вычислений, не может обеспечить необходимое качество обслуживания в таких условиях, в частности, в вопросах уменьшения времени задержки при передаче данных. Технология туманных вычислений сегодня рассматриваются как многообещающее решение для обработки большого объема критически важных и чувствительных ко времени данных. В этой статье рассмотрена технология облачных вычислений, а также анализируются предпосылки к эволюционному развитию этого подхода и появлению концепции туманных вычислений. В рамках обзора ключевых особенностей туманных вычислений произведен разбор часто встречающейся путаницы с объединением понятий туманных и краевых вычислений. Приведен обзор технологий организации туманных вычислений: виртуализация, контейнеризация и оркестрация, а также систематический анализ наиболее популярных платформ, обеспечивающих поддержку туманных вычислений. В результате анализа нами предлагается два подхода к классификации платформ туманных вычислений: по принципу открытости/закрытости компонентов, а также трехуровневая классификация на основе предоставляемого функционала платформы (Deploy-, Platform-и Ecosystem as a Service). Ключевые слова: облачные вычисления, туманные вычисления, краевые вычисления, интернет вещей.

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“…-Cloud Computing Cloud computing is a paradigm [10] that involves a network of multiple systems connecting to an infrastructure which has the capacity to scale over a period of time. This is usually facilitated through data centers which communicate to consumers via the internet.…”

Section: B Centralized Iot Ecosystemmentioning

confidence: 99%

Internet of Things Device Capability Profiling Using Blockchain

Ajayi

Rafferty

Morrow

et al. 2019

2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

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Currently, Internet of Things (IoT) devices can integrate into an existing network where they may interact with a myriad of other devices that may host a range of capabilities. Such IoT devices may need to share data that is consumed by other devices or services. This data is generated by the capabilities built into devices within the ecosystem. A typical IoT ecosystem that is heterogeneous in nature should be able to have devices that offer a range of capabilities that could be explored in the event a device breakdown or malfunction. This is to ensure that the system is self-sustaining, and adequately perform during undesirable conditions. Hence, an IoT ecosystem should be able to collaborate, self-organize itself to explore these capabilities towards achieving an overall goal. As such, interoperability of these devices which will improve functionality, availability, and robustness of the IoT ecosystem must be achieved. Also, Several IoT representations today store their data centrally which gives rise to inherent issues such as single point of failure, and other possible vulnerabilities. Addressing these deficiencies alongside proper profiling of IoT device capability and other device details is viewed as the first stage in securing IoT ecosystems and this was explored in this research. This study presents the use of Distributed Ledger Technology which has the inherent property of being secure to profile the capability of IoT devices within self-organized IoT ecosystems. A system overview, data structures, and algorithms are presented.

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Fog Computing with the Integration of Internet of Things: Architecture, Applications and Future Directions

Cited by 29 publications

References 15 publications

A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment

A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment

Overview of Fog Computing Organization Technologies

Internet of Things Device Capability Profiling Using Blockchain

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