A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Namoun, Abdallah; Sen, Adnan Ahmed Abi; Tufail, Ali; Alshanqiti, Abdullah; Nawaz, Waqas; Benrhouma, Oussama

doi:10.3390/s22145142

Cited by 8 publications

(4 citation statements)

References 89 publications

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“…The proposed method is compared to various algorithms such as GQSC [21], ANN-PSO Algorithm [22], DRL [23], and ML [24] algorithm which is mainly used for service discovery and composition. All algorithms showed identical fitness values for all data sets.…”

Section: Resultsmentioning

confidence: 99%

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Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm

Sefati

Arasteh

Halunga

et al. 2023

IEEE Internet Things J.

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Despite significant advancements in Internet of Things (IoT)-based smart cities, service discovery and composition continue to pose challenges. Current methodologies face limitations in optimizing Quality of Service (QoS) in diverse network conditions, thus creating a critical research gap. This study presents an original and innovative solution to this issue by introducing a novel three-layered Recurrent Neural Network (RNN) algorithm. Aimed at optimizing QoS in the context of IoT service discovery, our method incorporates user requirements into its evaluation matrix. It also integrates Long Short-Term Memory (LSTM) networks and a unique Black Widow Optimization (BWO) algorithm, collectively facilitating the selection and composition of optimal services for specific tasks. This approach allows the RNN algorithm to identify the top-K services based on QoS under varying network conditions. Our methodology's novelty lies in implementing LSTM in the hidden layer and employing backpropagation through time (BPTT) for parameter updates, which enables the RNN to capture temporal patterns and intricate relationships between devices and services. Further, we use the BWO algorithm, which simulates the behavior of black widow spiders, to find the optimal combination of services to meet system requirements. This algorithm factors in both the attractive and repulsive forces between services to isolate the best candidate solutions. In comparison with existing methods, our approach shows superior performance in terms of latency, availability, and reliability. Thus, it provides an efficient and effective solution for service discovery and composition in IoT-based smart cities, bridging a significant gap in current research.

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Section: Resultsmentioning

confidence: 99%

“…Namoun, et al [24] propose a two-phase machine learning (ML) algorithm that considers the context of the user and the QoS requirements of the selected services. The first phase of the framework involves training a ML model on historical data to predict the QoS of the services.…”

Section: Related Workmentioning

confidence: 99%

Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm

Sefati

Arasteh

Halunga

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…[14] designed a behavioral decision tree machine learning classification approach to build a contextaware predictive model based on diverse user activities with smartphones. [15] suggested a machine learning-based framework for selecting services to create assistive services for disabled people.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning

Hallou,

Fissaa,

Hafiddi

et al. 2024

Int. J. Onl. Eng.

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Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.

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“…Moreover, various tools were developed to integrate data, services, and web resources [25]. To guide service selection for people with impairments, the authors in [26] proposed a machine learning-driven framework taking into account user context and disability factors. However, tools dedicated to empowering the development of accessible services are still rare.…”

Section: Te Foundations Of Servicementioning

confidence: 99%

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

Namoun

Tufail

Nawaz

et al. 2023

Computational Intelligence and Neuroscience

Self Cite

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Integrating smart heterogeneous objects, IoT devices, data sources, and software services to produce new business processes and functionalities continues to attract considerable attention from the research community due to its unraveled advantages, including reusability, adaptation, distribution, and pervasiveness. However, the exploitation of service-oriented computing technologies (e.g., SOC, SOA, and microservice architectures) by people with special needs is underexplored and often overlooked. Furthermore, the existing challenges in this area are yet to be identified clearly. This research study presents a rigorous literature survey of the recent advances in service-oriented composition approaches and solutions for disabled people, their domains of application, and the major challenges, covering studies published between January 2010 and October 2022. To this end, we applied the systematic literature review (SLR) methodology to retrieve and collate only the articles presenting and discussing service composition solutions tailored to produce digitally accessible services for consumption by people who suffer from an impairment or loss of some physical or mental functions. We searched six renowned bibliographic databases, particularly IEEE Xplore, Web of Science, Springer Link, ACM Library, ScienceDirect, and Google Scholar, to synthesize a final pool of 38 related articles. Our survey contributes a comprehensive taxonomy of service composition solutions, techniques, and practices that are utilized to create assistive technologies and services. The seven-facet taxonomy helps researchers and practitioners to quickly understand and analyze the fundamental conceptualizations and characteristics of accessible service composition for people with disabilities. Key findings showed that services are fused to assist disabled persons to carry out their daily activities, mainly in smart homes and ambient intelligent environments. Despite the emergence of immersive technologies (e.g., wearable computing), user-service interactions are enabled primarily through tactile and speech modalities. Service descriptions mainly incorporate functional features (e.g., performance, latency, and cost) of service quality, largely ignoring accessibility features. Moreover, the outstanding research problems revolve around (1) the unavailability of assistive services datasets, (2) the underspecification of accessibility aspects of disabilities, (3) the weak adoption of accessible and universal design practices, (4) the abstraction of service composition approaches, and (5) the rare experimental testing of composition approaches with disabled users. We conclude our survey with a set of guidelines to realize effective assistive service composition in IoT and cloud environments. Researchers and practitioners are advised to create assistive services that support the social relationships of disabled users and model their accessibility needs as part of the quality of service (QoS). Moreover, they should exploit AI/ML models to address the evolving requirements of disabled users in their unique environments. Furthermore, weaknesses of service composition solutions and research challenges are exposed as notable opportunities for future research.

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A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Cited by 8 publications

References 89 publications

Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm

Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm

Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

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