3 research outputs found
Context-Aware Service Recommendation System for the Social Internet of Things
The Social Internet of Things (SIoT) enables interconnected smart devices to
share data and services, opening up opportunities for personalized service
recommendations. However, existing research often overlooks crucial aspects
that can enhance the accuracy and relevance of recommendations in the SIoT
context. Specifically, existing techniques tend to consider the extraction of
social relationships between devices and neglect the contextual presentation of
service reviews. This study aims to address these gaps by exploring the
contextual representation of each device-service pair. Firstly, we propose a
latent features combination technique that can capture latent feature
interactions, by aggregating the device-device relationships within the SIoT.
Then, we leverage Factorization Machines to model higher-order feature
interactions specific to each SIoT device-service pair to accomplish accurate
rating prediction. Finally, we propose a service recommendation framework for
SIoT based on review aggregation and feature learning processes. The
experimental evaluation demonstrates the framework's effectiveness in improving
service recommendation accuracy and relevance
Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature
Wearable devices like smart glasses have gained popularity across various
applications. However, their limited computational capabilities pose challenges
for tasks that require extensive processing, such as image and video
processing, leading to drained device batteries. To address this, offloading
such tasks to nearby powerful remote devices, such as mobile devices or remote
servers, has emerged as a promising solution. This paper focuses on analyzing
task-offloading scenarios for a healthcare monitoring application performed on
smart wearable glasses, aiming to identify the optimal conditions for
offloading. The study evaluates performance metrics including task completion
time, computing capabilities, and energy consumption under realistic
conditions. A specific use case is explored within an indoor area like an
airport, where security agents wearing smart glasses to detect elevated body
temperature in individuals, potentially indicating COVID-19. The findings
highlight the potential benefits of task offloading for wearable devices in
healthcare settings, demonstrating its practicality and relevance
A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
The Social Internet of Things (SIoT), is revolutionizing how we interact with
our everyday lives. By adding the social dimension to connecting devices, the
SIoT has the potential to drastically change the way we interact with smart
devices. This connected infrastructure allows for unprecedented levels of
convenience, automation, and access to information, allowing us to do more with
less effort. However, this revolutionary new technology also brings an eager
need for service recommendation systems. As the SIoT grows in scope and
complexity, it becomes increasingly important for businesses and individuals,
and SIoT objects alike to have reliable sources for products, services, and
information that are tailored to their specific needs. Few works have been
proposed to provide service recommendations for SIoT environments. However,
these efforts have been confined to only focusing on modeling user-item
interactions using contextual information, devices' SIoT relationships, and
correlation social groups but these schemes do not account for latent semantic
item-item structures underlying the sparse multi-modal contents in SIoT
environment. In this paper, we propose a latent-based SIoT recommendation
system that learns item-item structures and aggregates multiple modalities to
obtain latent item graphs which are then used in graph convolutions to inject
high-order affinities into item representations. Experiments showed that the
proposed recommendation system outperformed state-of-the-art SIoT
recommendation methods and validated its efficacy at mining latent
relationships from multi-modal features