Sustainability in intelligent building environments using weighted priority scheduling algorithm

Shahi, Ahmad; Sulaiman, Nasir; Mustapha, Norwati; Perumal, Thinagaran; Parizi, Reza M.

doi:10.3233/ais-170462

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…No clear definition of the mentioned conflict. Simultaneously arrived conflict for events has been explored in Shahi et al (2017) and depending on weighted-priority scheduling for solving such conflict. The work focused on one conflict, ignoring other conflict types.…”

Section: Current Conflicts’ Classifications and Detection Methodsmentioning

confidence: 99%

A conflicts’ classification for IoT-based services: a comparative survey

Ibrhim

Hassan

Nabil

2021

PeerJ Computer Science

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Recently, Internet of Things (IoT)-based systems, especially automation systems, have become an indispensable part of modern-day lives to support the controlling of the networked devices and providing context-aware and intelligent environments. IoT-based services/apps developed by the end-users interact with each other and share concurrent access to devices according to their preferences, which increases safety, security, and correctness issues in IoT systems. Due to the critical impacts resulting from these issues, IoT-based apps require a customized type of compilers or checking tools that capable of analyzing the structures of these apps and detecting different types of errors and conflicts either in intra-IoT app instructions or in inter-IoT apps interactions. A plethora of approaches and frameworks have been proposed to assist the best practices for end-users in developing their IoT-based apps and mitigate these errors and conflicts. This paper focuses on conflict classification and detection approaches in the context of IoT systems by investigating the current research techniques that provided conflicts’ classification or detection in IoT systems (published between 2014 and 2020). A classification of IoT-based apps interaction conflicts is proposed. The proposed conflicts’ classification provides a priori conflicts detection method based on the analysis of IoT app instructions’ relationships with utilizing the state-of-the-art Satisfiability Modulo Theories (SMT) model checking and formal notations. The current detection approaches are compared with each other according to the proposed conflicts’ classification to determine to which extend they cover different conflicts. Based on this comparison, we provide evidence that the existing approaches have a gap in covering different conflicts’ levels and types which yields to minimize the correctness and safety of IoT systems. We point out the need to develop a safety and security compiler or tool for IoT systems. Also, we recommend using a hybrid approach that combines model checking with a variety of languages and semantic technologies in developing future IoT-based apps verification frameworks to cover all levels and types of conflicts to guarantee and increase the safety, security, and correctness of IoT systems.

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Section: Current Conflicts’ Classifications and Detection Methodsmentioning

confidence: 99%

A conflicts’ classification for IoT-based services: a comparative survey

Ibrhim

Hassan

Nabil

2021

PeerJ Computer Science

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“…No clear definition of the mentioned conflict. Simultaneously arrived conflict for events has been explored Manuscript to be reviewed Computer Science in (Shahi et al, 2017) and depending on weighted-priority scheduling for solving such conflict. The work focused on one conflict, ignoring other conflict types.…”

Section: Multiple User Iot Apps Conflicts Detectionmentioning

confidence: 99%

Peer Review #2 of "A conflicts’ classification for IoT-based services: a comparative survey (v0.1)"

2021

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“…Te classifcation problem is an important branch of machine learning [1][2][3][4], and because the training process of k nearest neighbor (i.e., KNN) classifcation method does not require the assumption of consistent data distribution, this advantage makes it an irreplaceable method for all classifcation algorithms. In addition, the KNN classifcation method has other characteristics, for example, it is nonparameter learning, i.e., no training parameters, and the Euclidean distance between the training samples and testing sample are used to predict the label of the testing sample.…”

Section: Introductionmentioning

confidence: 99%

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Zhang,

Bian,

Wang

2024

International Journal of Intelligent Systems

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While the classical KNN (k nearest neighbor) shares its avoidance of the consistent distribution assumption between training and testing samples to achieve fast prediction, it still faces two challenges: (a) its generalization ability heavily depends on an appropriate number k of nearest neighbors; (b) its prediction behavior lacks interpretability. In order to address the two challenges, a novel Bayes-decisive linear KNN with adaptive nearest neighbors (i.e., BLA-KNN) is proposed to obtain the following three merits: (a) a diagonal matrix is introduced to adaptively select the nearest neighbors and simultaneously improve the generalization capability of the proposed BLA-KNN method; (b) the proposed BLA-KNN method owns the group effect, which inherits and extends the group property of the sum of squares for total deviations by reflecting the training sample class-aware information in the group effect regularization term; (c) the prediction behavior of the proposed BLA-KNN method can be interpreted from the Bayes-decision-rule perspective. In order to do so, we first use a diagonal matrix to weigh each training sample so as to obtain the importance of the sample, while constraining the importance weights to ensure that the adaptive k value is carried out efficiently. Second, we introduce a class-aware information regularization term in the objective function to obtain the nearest neighbor group effect of the samples. Finally, we introduce linear expression weights related to the distance measure between the testing and training samples in the regularization term to ensure that the interpretation of Bayes-decision-rule can be performed smoothly. We also optimize the proposed objective function using an alternating optimization strategy. We experimentally demonstrate the effectiveness of the proposed BLA-KNN method by comparing it with 7 comparative methods on 15 benchmark datasets.

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Sustainability in intelligent building environments using weighted priority scheduling algorithm

Cited by 7 publications

References 35 publications

A conflicts’ classification for IoT-based services: a comparative survey

A conflicts’ classification for IoT-based services: a comparative survey

Peer Review #2 of "A conflicts’ classification for IoT-based services: a comparative survey (v0.1)"

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

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