Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT

Rahman, Hasibur; Rahmani, Rahim; Kanter, Theo

doi:10.1016/j.procs.2017.05.360

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…Numerous context-aware applications in the literature use ML techniques for reasoning or "mining" context data and for extracting meaningful information from these data. Examples include applications in health care [1], smart cities [2] and IoT [3] [4], just to cite a few. Most of them focus on specific applications, and demonstrate the interest of applying ML approaches to context data.…”

Section: Towards Context Mining As Facilitymentioning

confidence: 99%

Machine Learning for a Context Mining Facility

Rabah

Pinheiro²,

Grand³

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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This paper considers generalizing context reasoning capabilities through a context mining facility offered to all Information System applications. This facility requires mining context data at the system scale, which raises several challenges for Machine Learning approaches used for such mining. Through a detailed literature review, we analyze these approaches with regard to the requirements of such a context mining facility at the Information System level, pointing to the potential and to the challenges raised by this perspective.

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Section: Towards Context Mining As Facilitymentioning

confidence: 99%

Machine Learning for a Context Mining Facility

Rabah

Pinheiro²,

Grand³

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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“…Moreover, their solution is limited to neighboring nodes. To the best of our knowledge, the concept of logical clustering of nodes based on context is new and [19,20] was the first attempt of the proposed concept. This new concept will allow resources (data, services) to be shared among different spatial distributed nodes and they can share resources through distributed collaboration.…”

Section: System Model Of the Proximity Consensus Algorithmmentioning

confidence: 99%

“…Once the clustering is done, then each cluster is identified through a context-ID, which is defined based on context similarity and published on the Internet. In this study, we extended the context-ID [19,20] to be a relational proximity spatial distribution by assigning different weights to the connections between nodes to reduce traffic on critical links and besides scalability, the algorithm also allows different context-based parts to run different combinations of consensus, ledger, and transaction models. Furthermore, dynamically clustering can easily inter-operate between clusters.…”

Section: System Model Of the Proximity Consensus Algorithmmentioning

confidence: 99%

A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

Rahmani

Firouzi

Alam

2020

7th International Electronic Conference on Sensors and Applications

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The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

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“…It is in this situation where the context-aware computing brings value by deducing knowledge and providing better understanding of raw data. The work of Lalanda et al 21 and Rahman et al 22 takes advantage of the Edge-computing and pervasive applications to propose context-aware platforms to provide a reasoner in charge of deducing knowledge and dealing with the environment by using context management. The design of these platforms is based on a service component model or OSGi specification that describes a modular system and a service platform for the Java programming language.…”

Section: Previous Workmentioning

confidence: 99%

Enabling smart behavior through automatic service composition for Internet of Things–based Smart Homes

Santofimia

Villa

Aceña

et al. 2018

International Journal of Distributed Sensor Networks

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This article presents an Internet of Things architecture for Smart Homes that specifically targets service composition and reconfiguration as enablers for the actuation and smart behavior capabilities. To this end, the main challenge that has to be addressed is the support to a seamless integration, composition, and reconfiguration of Internet of Things objects. Two enabling technologies are proposed here: a planning strategy based on a common-sense reasoning approach for service composition and a virtual-network protocol for Inter-Domain Messaging. The planner will identify the services that, properly connected, will cater for arisen, and therefore, unexpected needs. The virtual-network protocol will provide the support for this interconnection to take place in a transparent and orthogonal manner. This is particularly important to enable autonomous systems to instantiate composite services. To demonstrate the capabilities of the resulting framework, two use cases are presented, which under real circumstances demonstrate the potential of the proposed approach.

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Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT

Cited by 11 publications

References 12 publications

Machine Learning for a Context Mining Facility

Machine Learning for a Context Mining Facility

A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

Enabling smart behavior through automatic service composition for Internet of Things–based Smart Homes

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