4,273 research outputs found
Toward Open and Programmable Wireless Network Edge
Increasingly, the last hop connecting users to their enterprise and home networks is wireless. Wireless is becoming ubiquitous not only in homes and enterprises but in public venues such as coffee shops, hospitals, and airports. However, most of the publicly and privately available wireless networks are proprietary and closed in operation. Also, there is little effort from industries to move forward on a path to greater openness for the requirement of innovation. Therefore, we believe it is the domain of university researchers to enable innovation through openness. In this thesis work, we introduce and defines the importance of open framework in addressing the complexity of the wireless network. The Software Defined Network (SDN) framework has emerged as a popular solution for the data center network. However, the promise of the SDN framework is to make the network open, flexible and programmable. In order to deliver on the promise, SDN must work for all users and across all networks, both wired and wireless. Therefore, we proposed to create new modules and APIs to extend the standard SDN framework all the way to the end-devices (i.e., mobile devices, APs). Thus, we want to provide an extensible and programmable abstraction of the wireless network as part of the current SDN-based solution. In this thesis work, we design and develop a framework, weSDN (wireless extension of SDN), that extends the SDN control capability all the way to the end devices to support client-network interaction capabilities and new services. weSDN enables the control-plane of wireless networks to be extended to mobile devices and allows for top-level decisions to be made from an SDN controller with knowledge of the network as a whole, rather than device centric configurations. In addition, weSDN easily obtains user application information, as well as the ability to monitor and control application flows dynamically. Based on the weSDN framework, we demonstrate new services such as application-aware traffic management, WLAN virtualization, and security management
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
The field of mobile, wearable, and ubiquitous computing (UbiComp) is
undergoing a revolutionary integration of machine learning. Devices can now
diagnose diseases, predict heart irregularities, and unlock the full potential
of human cognition. However, the underlying algorithms are not immune to biases
with respect to sensitive attributes (e.g., gender, race), leading to
discriminatory outcomes. The research communities of HCI and AI-Ethics have
recently started to explore ways of reporting information about datasets to
surface and, eventually, counter those biases. The goal of this work is to
explore the extent to which the UbiComp community has adopted such ways of
reporting and highlight potential shortcomings. Through a systematic review of
papers published in the Proceedings of the ACM Interactive, Mobile, Wearable
and Ubiquitous Technologies (IMWUT) journal over the past 5 years (2018-2022),
we found that progress on algorithmic fairness within the UbiComp community
lags behind. Our findings show that only a small portion (5%) of published
papers adheres to modern fairness reporting, while the overwhelming majority
thereof focuses on accuracy or error metrics. In light of these findings, our
work provides practical guidelines for the design and development of ubiquitous
technologies that not only strive for accuracy but also for fairness
A survey of urban drive-by sensing: An optimization perspective
Pervasive and mobile sensing is an integral part of smart transport and smart
city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is
gaining popularity in both academic research and field practice. The DS
paradigm has an inherent transport component, as the spatial-temporal
distribution of the sensors are closely related to the mobility patterns of
their hosts, which may include third-party (e.g. taxis, buses) or for-hire
(e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is
therefore essential to understand, assess and optimize the sensing power of
vehicle fleets under a wide range of urban sensing scenarios. To this end, this
paper offers an optimization-oriented summary of recent literature by
presenting a four-step discussion, namely (1) quantifying the sensing quality
(objective); (2) assessing the sensing power of various fleets (strategic); (3)
sensor deployment (strategic/tactical); and (4) vehicle maneuvers
(tactical/operational). By compiling research findings and practical insights
in this way, this review article not only highlights the optimization aspect of
drive-by sensing, but also serves as a practical guide for configuring and
deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
The Prom Problem: Fair and Privacy-Enhanced Matchmaking with Identity Linked Wishes
In the Prom Problem (TPP), Alice wishes to attend a school dance with Bob and needs a risk-free, privacy preserving way to find out whether Bob shares that same wish. If not, no one should know that she inquired about it, not even Bob. TPP represents a special class of matchmaking challenges, augmenting the properties of privacy-enhanced matchmaking, further requiring fairness and support for identity linked wishes (ILW) – wishes involving specific identities that are only valid if all involved parties have those same wishes.
The Horne-Nair (HN) protocol was proposed as a solution to TPP along with a sample pseudo-code embodiment leveraging an untrusted matchmaker. Neither identities nor pseudo-identities are included in any messages or stored in the matchmaker’s database. Privacy relevant data stay within user control. A security analysis and proof-of-concept implementation validated the approach, fairness was quantified, and a feasibility analysis demonstrated practicality in real-world networks and systems, thereby bounding risk prior to incurring the full costs of development.
The SecretMatchâ„¢ Prom app leverages one embodiment of the patented HN protocol to achieve privacy-enhanced and fair matchmaking with ILW. The endeavor led to practical lessons learned and recommendations for privacy engineering in an era of rapidly evolving privacy legislation. Next steps include design of SecretMatchâ„¢ apps for contexts like voting negotiations in legislative bodies and executive recruiting. The roadmap toward a quantum resistant SecretMatchâ„¢ began with design of a Hybrid Post-Quantum Horne-Nair (HPQHN) protocol. Future directions include enhancements to HPQHN, a fully Post Quantum HN protocol, and more
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