4 research outputs found
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
Securing federated learning with blockchain : a systematic literature review
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.open access</p
Cyber Threats to Smart Grids : Review, Taxonomy, Potential Solutions, and Future Directions
Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks' influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.open accessThese materials are a result of research supported by The European Union-The Internal Security Fund (ISF), A431.678/2016.</p