QoS management mechanisms for Enhanced Living Environments in IoT

Banouar, Yassine; Ouedraogo, Clovis Anicet; Chassot, Christophe; Zyane, Abdellah

doi:10.23919/inm.2017.7987454

“…S X X X [18] It proposes PRISMA, a resource-oriented publish/subscribe IoT platform, which main goals are to provide: programming abstractions, services, runtime support, and QoS mechanisms, to meet application constraints. P X X X [19] It proposes an autonomic IoT platform for the QoS management composed of a set of QoS-oriented mechanisms that can be dynamically executed at the platform level to correct QoS degradation.…”

Section: S2mentioning

confidence: 99%

“…The programming abstractions are provided through REpresentational State Transfer (REST) interfaces; the main services provided are asynchronous communication, resource discovery and topology control; and the runtime support is for the creation, configuration, and execution of new applications in wireless sensor networks (WNS). In [19] is proposed an autonomic IoT platform-level QoS management for IoT Ecosystems. It is defined by a set of QoS-oriented mechanisms dynamically executed to solve QoS degradation problems.…”

Section: S2mentioning

confidence: 99%

An Autonomic Management System for IoT Platforms based on Data Analysis Tasks

Ouedraogo¹,

Chassot²,

Medjiah³

et al. 2022

IJCNDS

0

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In this work, we propose an Autonomic Management System (AMS) for the Internet of Things (IoT) platforms, which uses the concept of autonomic cycle of data analysis tasks to improve and maintain the performance in the IoT platforms. The concept of "Autonomic Cycle of Data Analysis Tasks" is a type of autonomous intelligent supervision that allows reaching strategic objectives around a given problem. In this paper, we propose the conceptualization of the architecture of an AMS composed by an autonomic cycle to optimize the Quality of Services (QoS), and to improve the Quality of Experiences (QoE), in IoT platforms. The autonomous cycle detects and discoveries the current operational state in the IoT platform, and determines the set of tasks to guarantee a given performance (QoS/QoE). This paper presents the details of the architecture of the AMS (components, knowledge models, etc.), and its utilization in two case studies: in a typical application in an IoT context, and in a Tactile Internet System.

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“…S X X X [18] It proposes PRISMA, a resource-oriented publish/subscribe IoT platform, which main goals are to provide: programming abstractions, services, runtime support, and QoS mechanisms, to meet application constraints. P X X X [19] It proposes an autonomic IoT platform for the QoS management composed of a set of QoS-oriented mechanisms that can be dynamically executed at the platform level to correct QoS degradation.…”

Section: S2mentioning

confidence: 99%

An autonomic management system for IoT platforms based on data analysis tasks

Ouedraogo

¹

,

Aguilar

²

,

Chassot

³

et al. 2022

IJCNDS

0

View full text Add to dashboard Cite

In this work, we propose an Autonomic Management System (AMS) for the Internet of Things (IoT) platforms, which uses the concept of autonomic cycle of data analysis tasks to improve and maintain the performance in the IoT platforms. The concept of "Autonomic Cycle of Data Analysis Tasks" is a type of autonomous intelligent supervision that allows reaching strategic objectives around a given problem. In this paper, we propose the conceptualization of the architecture of an AMS composed by an autonomic cycle to optimize the Quality of Services (QoS), and to improve the Quality of Experiences (QoE), in IoT platforms. The autonomous cycle detects and discoveries the current operational state in the IoT platform, and determines the set of tasks to guarantee a given performance (QoS/QoE). This paper presents the details of the architecture of the AMS (components, knowledge models, etc.), and its utilization in two case studies: in a typical application in an IoT context, and in a Tactile Internet System.

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“…The proposed scheme selects the secure licensed channel for delay-sensitive IoT device based on the channel fading conditions and information on PU activities. In order to improve QoS degradation for IoT networks an autonomic QoS-aware middleware was introduced in [21]. In [22], an adaptive and QoS-aware architecture was introduced for wireless sensors networks based IoT by using a modular approach.…”

Section: Related Workmentioning

confidence: 99%

Quality of Service Provisioning for Heterogeneous Services in Cognitive Radio-Enabled Internet of Things

Ali

¹

,

Feng

²

,

Bashir

³

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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The Internet of Things (IoT) is a network of interconnected objects, in which every object in the world seeks to communicate and exchange information actively. This exponential growth of interconnected objects increases the demand for wireless spectrum. However, providing wireless channel access to every communicating object while ensuring its guaranteed quality of service (QoS) requirements is challenging and has not yet been explored, especially for IoT-enabled mission-critical applications and services. Meanwhile, Cognitive Radio-enabled Internet of Things (CR-IoT) is an emerging field that is considered the future of IoT. The combination of CR technology and IoT can better handle the increasing demands of various applications such as manufacturing, logistics, retail, environment, public safety, healthcare, food, and drugs. However, due to the limited and dynamic resource availability, CR-IoT cannot accommodate all types of users. In this paper, we first examine the availability of a licensed channel on the basis of its primary users' activities (e.g., traffic patterns). Second, we propose a priority-based secondary user (SU) call admission and channel allocation scheme, which is further based on a priority-based dynamic channel reservation scheme. The objective of our study is to reduce the blocking probability of higher-priority SU calls while maintaining a sufficient level of channel utilization. The arrival rates of SU calls of all priority classes are estimated using a Markov chain model, and further channels for each priority class are reserved based on this analysis. We compare the performance of the proposed scheme with the greedy non-priority and fair proportion schemes in terms of the SU call-blocking probability, SU call-dropping probability, channel utilization, and throughput. Numerical results show that the proposed priority scheme outperforms the greedy non-priority and fair proportion schemes.

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QoS management mechanisms for Enhanced Living Environments in IoT

Cited by 6 publications

References 8 publications

An Autonomic Management System for IoT Platforms based on Data Analysis Tasks

An Autonomic Management System for IoT Platforms based on Data Analysis Tasks

An autonomic management system for IoT platforms based on data analysis tasks

Quality of Service Provisioning for Heterogeneous Services in Cognitive Radio-Enabled Internet of Things

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