Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home

Mostafa, Salama A.; Gunasekaran, Saraswathy Shamini; Mohammed, Mazin Abed; Abduallah, Wafaa Mustafa

doi:10.1007/978-3-030-20473-0_29

Cited by 39 publications

(20 citation statements)

References 17 publications

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“…They categories the SLA issues and analyze the SLA issues. Results are basically focused on academically or limited industrial setting intend of real industrial environments and the lack of tools as well as evolution perspectives supports in SLA managements and focused on assessments like qualitative and quantitatively of QoS in SLA (3,4). In the cloud computing SLA-aware deployment are take place and analyze workload with case study.…”

Section: Related Workmentioning

confidence: 99%

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Kumar

2021

Baghdad Sci.J

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Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

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Section: Related Workmentioning

confidence: 99%

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Kumar

2021

Baghdad Sci.J

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“…Virtualization means creating a virtual version of something, rather than actual. In cloud computing, virtualization is a technique of replicating a version of something real digitally [19], [20]. It allows sharing a single physical resource or an application among many clients and organizations.…”

Section: Cloud Computing Virtualizationmentioning

confidence: 99%

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Shukur

Zeebaree

Zebari

et al. 2020

JASTT

103

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Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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“…Building an autonomous smart home requires a huge amount of changes to the current infrastructure of smart homes [ 14 , 15 , 16 ]. The proposed scheme in [ 14 ] presented an infrastructure of controlling the things inside a smart home by dividing the things into two groups, i.e., active and passive. These things are control with the help of agents attached to each appliance.…”

Section: Related Workmentioning

confidence: 99%

Towards Energy Efficient Home Automation: A Deep Learning Approach

Khan

Seo

Kim

2020

Sensors

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Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

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Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home

Cited by 39 publications

References 17 publications

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Towards Energy Efficient Home Automation: A Deep Learning Approach

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