Knowledge-based systems in decision support context: A literature review

Handoyo, Ikut Tri; Sensuse, Dana Indra

doi:10.1109/conmedia.2017.8266035

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Between the cloud and the end devices (sensors), at the edge layer, gateway (computer board such as Arduino) shortened the development, but the complication is still at the software level. Those boards are not intelligent enough to extract the knowledge from the raw data coming from sensors and make the decision [36]. Figure 3 illustrates the current approach; the sensors collect the raw data from the environment around and send it to the gateway at the edge layer.…”

Section: Distributed Intelligence In Iotmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

Section: Distributed Intelligence In Iotmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

show abstract

“…Between the cloud and the end devices (sensors), at the edge layer, gateway (computer board such as Arduino) shortened the development, but the complication is still at the software level. Those boards are not intelligent enough to extract the knowledge from the raw data coming from sensors, and to make decision [79]. Figure 3 illustrates the current approach; the sensors collect the raw data from the environment around and send it to the gateway at the edge layer.…”

Section: Distributed Intelligence In Iotmentioning

confidence: 99%

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

Rababah¹,

Alam²,

Eskicioglu³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Poli,

Cuntò,

Muccio

et al. 2024

Land Use Policy

View full text Add to dashboard Cite

Knowledge-based systems in decision support context: A literature review

Cited by 4 publications

References 32 publications

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

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

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

A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Contact Info

Product

Resources

About