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A note on exploration of IoT generated big data using semantics
yesWelcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies
TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
Recently, with the rapid deployment of service APIs, personalized service
recommendations have played a paramount role in the growth of the e-commerce
industry. Quality-of-Service (QoS) parameters determining the service
performance, often used for recommendation, fluctuate over time. Thus, the QoS
prediction is essential to identify a suitable service among functionally
equivalent services over time. The contemporary temporal QoS prediction methods
hardly achieved the desired accuracy due to various limitations, such as the
inability to handle data sparsity and outliers and capture higher-order
temporal relationships among user-service interactions. Even though some recent
recurrent neural-network-based architectures can model temporal relationships
among QoS data, prediction accuracy degrades due to the absence of other
features (e.g., collaborative features) to comprehend the relationship among
the user-service interactions. This paper addresses the above challenges and
proposes a scalable strategy for Temporal QoS Prediction using Multi-source
Collaborative-Features (TPMCF), achieving high prediction accuracy and faster
responsiveness. TPMCF combines the collaborative-features of users/services by
exploiting user-service relationship with the spatio-temporal auto-extracted
features by employing graph convolution and transformer encoder with multi-head
self-attention. We validated our proposed method on WS-DREAM-2 datasets.
Extensive experiments showed TPMCF outperformed major state-of-the-art
approaches regarding prediction accuracy while ensuring high scalability and
reasonably faster responsiveness.Comment: 10 Pages, 7 figure
Composition de services basée sur les relations sociales entre objets dans l’IoT Service composition based on social relations between things in IoT
With the rapid development of service-oriented computing applications and social Internet ofthings (SIoT), it is becoming more and more difficult for end-users to find relevant services to create value-added composite services in this big data environment. Therefore, this work proposes S-SCORE (Social Service Composition based on Recommendation), an approach for interactive web services composition in SIoT ecosystem for end-users. The main contribution of this work is providing a novel recommendation approach, which enables to discover and suggest trustworthy and personalized web services that are suitable for composition. The first proposed model of recommendation aims to face the problem of information overload, which enables to discover services and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our approach, seven variant algorithms of different approaches (popularity-based, user-based and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. The second proposed approach is a novel recommendation mechanism for service composition, which enables to suggest trustworthy and personalized web services that are suitable for composition. The process of recommendation consists of online and offline stages. In the offline stage, two models of similarity computation are presented. Firstly, an improved users’ similarity model is provided to filter the set of advisors for an active user. Then, a new service collaboration model is proposed that based on functional and non-functional features of services, which allows providing a set of collaborators
for the active service. The online phase makes rating prediction of candidate services based on a hybrid algorithm that based on collaborative filtering technique. The proposed method gives considerable improvement on the prediction accuracy. Firstly, it achieves the lowest value in MAE (Mean Absolute Error) metric and the highest coverage values than other compared traditional collaborative filtering-based prediction approaches
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
A Location-sensitive and Network-aware Broker for Recommending Web Services
Collaborative Filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real ”quality” of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web Services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, andWeb services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
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