Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

With the advancement in Machine Learning (ML) techniques, a wide range of applications that leverage ML have emerged across research, industry, and society to improve application performance. However, existing ML schemes used within such applications struggle to attain high model accuracy due to the heterogeneous and distributed nature of their generated data, resulting in reduced model performance. In this paper we address this challenge by proposing PPFM: an adaptive and hierarchical Peer-to-Peer Federated Meta-learning framework. Instead of leveraging a conventional static ML scheme, PPFM uses multiple learning loops to dynamically self-adapt its own architecture to improve its training effectiveness for different generated data characteristics. Such an approach also allows for PPFM to remove reliance on a fixed centralized server in a distributed environment by utilizing peer-to-peer Federated Learning (FL) framework. Our results demonstrate PPFM provides significant improvement to model accuracy across multiple datasets when compared to contemporary ML approaches.

Sensor networks. --- Sensor networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint presents a collection of original research and survey articles that tackle the practical challenges in large-scale and rapid deployment of sensors for diverse applications as well as the resulting Big Data processing. The complexity of the generated data ranges from large-scale sensor networks to smartphone-enabled citizen sensing data from social networks and personal health devices, which requires advanced data processing, mining and fusion methods. Solutions listed in this book include those that address issues of the interoperability of IoT solutions and data fragmentation through crawling, indexing and searching IoT data sources and the predictive maintenance of sensors. Social networks are also in scope, through a visualisation system for the analysis of anomalies in social graphs, detecting context-aware sociability patterns and assessing the effectiveness of fine tuning and pretrained word embedding in generating interpretable topics from short texts in social networks. Applications in scope include smart tourism, fall detection through personal health sensors and an energy management expert assistant.

Sensor networks.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Human activity recognition (HAR) and human behavior recognition (HBR) play increasingly important roles in the digital age. High-quality sensory observations applicable to recognizing users' activities and behaviors, including electrical, magnetic, mechanical (kinetic), optical, acoustic, thermal, and chemical biosignals, are inseparable from sensors' sophisticated design and appropriate application. Traditional sensors suitable for HAR and HBR, including external sensors for smart homes, optical sensors such as cameras for capturing video signals, and bioelectrical, biomagnetic, and biomechanical sensors for wearable applications, have been studied and verified adequately. They continue to be researched in depth for more effective and efficient usage, and brand-new areas facilitated by sensor-based HAR/HBR are emerging, such as interactive edutainment, single-motion duration analysis, time series information retrieval, handcrafted and high-level feature design, and fall detection. Meanwhile, innovative sensor research for HAR or HBR is also very active in the academic community, including new sensors appropriate for HAR/HBR, new designs and applications of the above-mentioned traditional sensors, and the usage of non-traditional HAR-/HBR-related sensor types, among others.

Sensor networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Sensor networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint presents a collection of original research and survey articles that tackle the practical challenges in large-scale and rapid deployment of sensors for diverse applications as well as the resulting Big Data processing. The complexity of the generated data ranges from large-scale sensor networks to smartphone-enabled citizen sensing data from social networks and personal health devices, which requires advanced data processing, mining and fusion methods. Solutions listed in this book include those that address issues of the interoperability of IoT solutions and data fragmentation through crawling, indexing and searching IoT data sources and the predictive maintenance of sensors. Social networks are also in scope, through a visualisation system for the analysis of anomalies in social graphs, detecting context-aware sociability patterns and assessing the effectiveness of fine tuning and pretrained word embedding in generating interpretable topics from short texts in social networks. Applications in scope include smart tourism, fall detection through personal health sensors and an energy management expert assistant.

Sensor networks.