A survey on fog computing for the Internet of Things

Bellavista, Paolo; Berrocal, Javier; Corradi, Antonio; Das, Sajal K.; Foschini, Luca; Zanni, Alessandro

doi:10.1016/j.pmcj.2018.12.007

Cited by 233 publications

(130 citation statements)

References 91 publications

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“…Depending on the computational capacity of the edge servers or gateway devices, such fog-based services can include not only conventional tasks such as protocol conversion but also local data processing applications, some of which are outlined as follows. There is a variety of applications such as data filtering and data fusion to ensure high-level data quality at the edge, improving the data accuracy and performing data abstraction [36,9]. Such applications can decrease the amount of data that should be sent to the cloud server and subsequently save external bandwidth.…”

Section: Fog Computing and Its Benefitsmentioning

confidence: 99%

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

et al. 2020

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Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient's contextual information -including health and activity status -can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient's contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a run-time distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of decision making process over a certain threshold. Our optimization algorithm resides in the Fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy-efficiency with a bounded loss in accuracy.

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Section: Fog Computing and Its Benefitsmentioning

confidence: 99%

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

et al. 2020

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“…Therefore, network performance can be enhanced given that the processes are not only executed in centralized cloud servers, but also along the path to them. The OpenFog Consortium (Fremont, CA, USA) [19] defines the architecture of fog computing as "a horizontal system-level architecture that distributes computing, storage, control, and networking functions closer to the users along a cloud-to-thing continuum." Further details of fog computing are provided in Section IV.…”

Section: Fog Computingmentioning

confidence: 99%

“…However, these three computing paradigms have different characteristics and architectures. As stated by the OpenFog Consortium [19], the edge computing architecture comprises servers, applications, or small clouds at the edge. In addition, edge computing runs its nodes in silos, whereas data should be transferred back through the cloud to establish peer-to-peer traffic.…”

Section: Edge Computingmentioning

confidence: 99%

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Ramli¹,

Bhardwaj²,

Kim³

2019

Preprint

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Reliability is essential in industrial networks. In addition, most of the data from nodes of industrial Internet of Things (IIoT) are generated in real time. Thus, those data are mainly used for the time-sensitive applications. Furthermore, device failures should be considered when modeling reliable fog computing for IIoT. In this paper, we provide fundamental aspects to model reliable fog computing for IIoT. First, existing models of fog computing are compared. Then, the most feasible communication type to achieve a reliable system is determined from model analysis. Interaction modes are elaborated to study the advantages and drawbacks when communication is deployed in fog computing for IIoT, and challenges and solutions for reliable fog computing are discussed.

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“…IoT is nowadays being active technology where things are all interconnected at home, road, and buildings. Hence, actuators, sensors are commonly used to interact between devices for data transmission and processing [4]. However, since sensors are tied up with life span due to the battery lifetime and energy, this cause a critical hurdle for such type of communication to be more spread and utilized in communication and networking [5].…”

Section: Introductionmentioning

confidence: 99%

Edge IoT Networking

Alhaisoni¹

2020

IJACSA

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Data transmission has witnessed a new wave of emerging technologies such as IoT. This new way of communication could be done through smart communication such as smart sensors and actuators. Thus, data traffic keeps traversing to the main servers in order to accomplish the tasks at the sensors side. However, this way of communication has encountered certain issues related to network due to the nature of routing forth and back from the end users to the main servers. Subsequently, this incurs high delay and packet loss which successively degrades the overall Quality of Service (QoS). On the other hand, the new way of data transmission, which is called "edge IoT network", has not only helped on reducing the load over the network but also made the nodes to be more self-manage at the edge. However, this approach has some limitations due to the power consumption and efficiency, which would lead to node failure and data loss. Therefore, this paper presents a new model of combining network science and computer network in order to enhance the edge IoT efficiency. Simulation results have shown a clear evidence in improving the efficiency, communicability, degree, and overall closeness.

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A survey on fog computing for the Internet of Things

Cited by 233 publications

References 91 publications

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Edge IoT Networking

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