1,702 research outputs found
Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization
The design and optimization of wireless networks have mostly been based on
strong mathematical and theoretical modeling. Nonetheless, as novel
applications emerge in the era of 5G and beyond, unprecedented levels of
complexity will be encountered in the design and optimization of the network.
As a result, the use of Artificial Intelligence (AI) is envisioned for wireless
network design and optimization due to the flexibility and adaptability it
offers in solving extremely complex problems in real-time. One of the main
future applications of AI is enabling user-level personalization for numerous
use cases. AI will revolutionize the way we interact with computers in which
computers will be able to sense commands and emotions from humans in a
non-intrusive manner, making the entire process transparent to users. By
leveraging this capability, and accelerated by the advances in computing
technologies, wireless networks can be redesigned to enable the personalization
of network services to the user level in real-time. While current wireless
networks are being optimized to achieve a predefined set of quality
requirements, the personalization technology advocated in this article is
supported by an intelligent big data-driven layer designed to micro-manage the
scarce network resources. This layer provides the intelligence required to
decide the necessary service quality that achieves the target satisfaction
level for each user. Due to its dynamic and flexible design, personalized
networks are expected to achieve unprecedented improvements in optimizing two
contradicting objectives in wireless networks: saving resources and improving
user satisfaction levels
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning agents
to build an accurate and robust machine learning models without sharing raw
data. By allowing mobile handsets and devices to collaboratively learn a global
model without explicit sharing of training data, FL exhibits high privacy and
efficient spectrum utilization. While there are a lot of survey papers
exploring FL paradigms and usability in 6G privacy, none of them has clearly
addressed how FL can be used to improve the protocol stack and wireless
operations. The main goal of this survey is to provide a comprehensive overview
on FL usability to enhance mobile services and enable smart ecosystems to
support novel use-cases. This paper examines the added-value of implementing FL
throughout all levels of the protocol stack. Furthermore, it presents important
FL applications, addresses hot topics, provides valuable insights and explicits
guidance for future research and developments. Our concluding remarks aim to
leverage the synergy between FL and future 6G, while highlighting FL's
potential to revolutionize wireless industry and sustain the development of
cutting-edge mobile services.Comment: 32 pages, 7 figures; 9 Table
Privacy-preserving human mobility and activity modelling
The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis.
The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users.
To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Generative Artificial Intelligence (GAI) possesses the capabilities of
generating realistic data and facilitating advanced decision-making. By
integrating GAI into modern Internet of Things (IoT), Generative Internet of
Things (GIoT) is emerging and holds immense potential to revolutionize various
aspects of society, enabling more efficient and intelligent IoT applications,
such as smart surveillance and voice assistants. In this article, we present
the concept of GIoT and conduct an exploration of its potential prospects.
Specifically, we first overview four GAI techniques and investigate promising
GIoT applications. Then, we elaborate on the main challenges in enabling GIoT
and propose a general GAI-based secure incentive mechanism framework to address
them, in which we adopt Generative Diffusion Models (GDMs) for incentive
mechanism designs and apply blockchain technologies for secure GIoT management.
Moreover, we conduct a case study on modern Internet of Vehicle traffic
monitoring, which utilizes GDMs to generate effective contracts for
incentivizing users to contribute sensing data with high quality. Finally, we
suggest several open directions worth investigating for the future popularity
of GIoT
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated
Vehicles (CAV), including perception, planning, and control. However, its
reliance on vehicular data for model training presents significant challenges
related to in-vehicle user privacy and communication overhead generated by
massive data volumes. Federated learning (FL) is a decentralized ML approach
that enables multiple vehicles to collaboratively develop models, broadening
learning from various driving environments, enhancing overall performance, and
simultaneously securing local vehicle data privacy and security. This survey
paper presents a review of the advancements made in the application of FL for
CAV (FL4CAV). First, centralized and decentralized frameworks of FL are
analyzed, highlighting their key characteristics and methodologies. Second,
diverse data sources, models, and data security techniques relevant to FL in
CAVs are reviewed, emphasizing their significance in ensuring privacy and
confidentiality. Third, specific and important applications of FL are explored,
providing insight into the base models and datasets employed for each
application. Finally, existing challenges for FL4CAV are listed and potential
directions for future work are discussed to further enhance the effectiveness
and efficiency of FL in the context of CAV
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Agent-based modeling and simulation has evolved as a powerful tool for
modeling complex systems, offering insights into emergent behaviors and
interactions among diverse agents. Integrating large language models into
agent-based modeling and simulation presents a promising avenue for enhancing
simulation capabilities. This paper surveys the landscape of utilizing large
language models in agent-based modeling and simulation, examining their
challenges and promising future directions. In this survey, since this is an
interdisciplinary field, we first introduce the background of agent-based
modeling and simulation and large language model-empowered agents. We then
discuss the motivation for applying large language models to agent-based
simulation and systematically analyze the challenges in environment perception,
human alignment, action generation, and evaluation. Most importantly, we
provide a comprehensive overview of the recent works of large language
model-empowered agent-based modeling and simulation in multiple scenarios,
which can be divided into four domains: cyber, physical, social, and hybrid,
covering simulation of both real-world and virtual environments. Finally, since
this area is new and quickly evolving, we discuss the open problems and
promising future directions.Comment: 37 page
A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback
A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback
- …