Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)

Rahman, Hasibur; Rahmani, Rahim

doi:10.1016/j.aci.2017.05.001

Cited by 37 publications

(24 citation statements)

References 10 publications

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“…Based on the ability to offload the training of the model into a more powerful infrastructure, not only the data of a user, but the data of many users can be used to train the model. Following the idea of distributed machine learning approaches Google recently proposed a system design (Bonawitz et al 2019) that employs the concept of federated learning (McMahan et al 2016;Rahman and Rahmani 2018). Tailored to the domain of mobile devices, federated learning establishes a distributed intelligence by using the recorded context data to train forecast models on the respective mobile device.…”

Section: Architecturementioning

confidence: 99%

Saving bandwidth and energy of mobile and IoT devices with link predictions

Orsini

Posdorfer

Lamersdorf

2020

J Ambient Intell Human Comput

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Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised. Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android. We evaluated our approach with real sensor data and showed how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.

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Section: Architecturementioning

confidence: 99%

Saving bandwidth and energy of mobile and IoT devices with link predictions

Orsini

Posdorfer

Lamersdorf

2020

J Ambient Intell Human Comput

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“…While the gateway performs data collection, awareness, and reporting, the cloud stores the reported data, and adjusts the data collection and awareness policy. Rahman et al [37] proposed a Distributed Intelligence model for IoT gateway based on a belief network concept to learn, predict, and make decisions. This system starts from a small number of predefined rules, and then the system can change the rules based on past experiences.…”

Section: Related Workmentioning

confidence: 99%

The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

Rababah¹,

Alam²,

Eskicioglu³

2020

Preprint

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Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

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“…But, beyond response times and network loads, some distributed applications need a decentralized intelligence approach in order to better meet the environment and application requirements. There are proposals to move intelligence to the edge in order to offer low-level intelligence for IoT applications [ 59 ]. In this line, decentralized multiagent systems provides ways of dealing with autonomy and heterogeneity [ 60 ].…”

Section: Background Of Iot Distributed Computingmentioning

confidence: 99%

Collaborative Working Architecture for IoT-Based Applications

Mora

Pont

Gil

et al. 2018

Sensors

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The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

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Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)

Cited by 37 publications

References 10 publications

Saving bandwidth and energy of mobile and IoT devices with link predictions

Saving bandwidth and energy of mobile and IoT devices with link predictions

The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

Collaborative Working Architecture for IoT-Based Applications

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