Mission-critical voice (MCV) communications systems have been a critical tool for the public safety community for over eight decades. Public safety users expect MCV systems to operate reliably and consistently, particularly in challenging conditions. Because of these expectations, the Public Safety Communications Research (PSCR) Division of the National Institute of Standards and Technology (NIST) has been interested in correlating impairments in MCV communication systems and public safety user quality of experience (QoE). Previous research has studied MCV voice quality and intelligibility in a controlled environment. However, such research has been limited by the challenges inherent in emulating real-world environmental conditions. Additionally, there is the question of the best metric to use to reflect QoE accurately. This paper describes our efforts to develop the methodology and tools for human-subject experiments with MCV. We illustrate their use in human-subject experiments in emulated real-world environments. The tools include a testbed for emulating real-world MCV systems and an automated speech recognition (ASR) robot approximating human subjects in transcription tasks. We evaluate QoE through a Levenshtein Distance-based metric, arguing it is a suitable proxy for measuring comprehension and the QoE. We conducted human-subject studies with Amazon MTurk volunteers to understand the influence of selected system parameters and impairments on human subject performance and end-user QoE. We also compare the performance of several ASR system configurations with human-subject performance. We find that humans generally perform better than ASR in accuracy-related MCV tasks and that the codec significantly influences the end-user QoE and ASR performance.

We propose to enhance the dependability of large-scale IoT systems by separating the management and operation plane. We innovate the management plane to enforce overarching policies, such as safety norms, operation standards, and energy restrictions, and integrate multi-faceted management entities, including regulatory agencies and manufacturers, while the current IoT operational workflow remains unchanged. Central to the management plane is a meticulously designed, identity-independent policy framework that employs flexible descriptors rather than fixed identifiers, allowing for proactive deployment of overarching policies with adaptability to system changes. Our evaluation across three datasets indicates that the proposed framework can achieve near-optimal expressiveness and dependable policy enforcement.

Synthetic traffic generation can produce sufficient data for model training of various traffic analysis tasks for IoT networks with few costs and ethical concerns. However, with the increasing functionalities of the latest smart devices, existing approaches can neither customize the traffic generation of various device functions nor generate traffic that preserves the sequentiality among packets as the real traffic. To address these limitations, this paper proposes IoTGemini, a novel framework for high-quality IoT traffic generation, which consists of a Device Modeling Module and a Traffic Generation Module. In the Device Modeling Module, we propose a method to obtain the profiles of the device functions and network behaviors, enabling IoTGemini to customize the traffic generation like using a real IoT device. In the Traffic Generation Module, we design a Packet Sequence Generative Adversarial Network (PS-GAN), which can generate synthetic traffic with high fidelity of both per-packet fields and sequential relationships. We set up a real-world IoT testbed to evaluate IoTGemini. The experiment result shows that IoTGemini can achieve great effectiveness in device modeling, high fidelity of synthetic traffic generation, and remarkable usability to downstream tasks on different traffic datasets and downstream traffic analysis tasks.

IoT devices have significantly altered the methods of interaction, operation, and functionality within home environments. However, individuals, particularly those with limited technical proficiency who stand to gain the most from these advancements, likely encounter challenges stemming from the intricate setup processes, a critical stage with the potential to limit their widespread adoption. Thus, we focus on the user experience during the setup phase of mainstream smart home devices and conduct an empirical study of 15 representative smart home IoT devices. We scrupulously examine their setup processes, as well as accompanying instructions and user manuals, to assess multi-faceted usability concerns. Our findings reveal 19 usability issues, indicating notable barriers, inconsistencies, and a lack of intuitiveness, which may deter consumers from successfully configuring and using these devices.

IoT devices benefit from nuanced policy enforcement to ensure safe, reliable, and efficient operations. However, the practical implementation of IoT policy enforcement systems involves multifaceted complexities. This paper delves into three-dimensional challenges: the intricacies of architecture and interoperational support, the expressiveness and safety concerns of policy languages, and the steep learning curve associated with policy creation. We investigate these areas, evaluate options, and prototype systems, considering the WoT, Pkl, and LLMs as potential solutions to address each challenge, respectively.

Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.

eSIM(embedded SIM) is an advanced alternative to traditional physical SIM cards initially developed by the GSM Association(GSMA) in 2013 [1][2]. The eSIM technology has been deployed in many commercial products such as mobile devices. However, the application of the eSIM technology in IoT devices has yet to start being primarily deployed. Understanding the eSIM architecture and the basic ideas of the eSIM provisioning and operations is very important for engineers to promote eSIM technology deployment in more areas, both academics and industries. The report focuses on the eSIM technology in the IoT architecture and two major operations of Remote SIM Provisioning(RSP) procedure: the Common Mutual Authentication procedure, a process used to authenticate eSIM trusted communication parties over the public internet, and the Profile Downloading procedure, the way to download the Profile from the operator SM-DP+ server and eventually remotely provision the end-user devices.

Policy Points Current medical device regulatory frameworks date back half a century and are ill suited for the next generation of medical devices that involve a significant software component. Existing Food and Drug Administration efforts are insufficient because of a lack of statutory authority, whereas international examples offer lessons for improving and harmonizing domestic medical device regulatory policy. A voluntary alternative pathway built upon two-stage review with individual component review followed by holistic review for integrated devices would provide regulators with new tools to address a changing medical device marketplace.

Since the emergence of the Session Initiation Proto-col (SIP) enabled Voice over IP (VoIP) communication, security has been considered in multiple aspects in the standards supporting SIP VoIP. While the basic tools to secure the voice network existed for decades, there has not been a significant coordinated effort to implement such solutions. However, with increased cyber awareness, and increased adoption of zero-trust architectures, we design and implement a testbed to examine and practically test a comprehensive architecture for secure voice communication. In this paper, we summarize our approach, lessons learned, observations, and recommendations.

Enforcing overarching policies such as safety norms and energy restrictions becomes critical as IoT scales and integrates into large systems. These policies should be applied preemptively and capable of adapting to system changes. Traditional IoT systems, reliant on fixed device identities, limit reliability, scalability, and resilience. Thus, we propose Identity-Independent IoT (I3oT), centered on adopting flexible descriptors to enforce policies. I3oT introduces a separate management plane on top of the standard operational workflow, thereby enhancing safety in scalable and integrated IoT systems.

IoT devices are susceptible to botnet malware due to inherent system limitations. Once compromised, these devices can be exploited to leak private user information or launch DDoS attacks. In this paper, we present Wital, an adaptable mitigation strategy that curtails the malicious use of IoT devices through a stringent whitelist-based approach. This method combines static manufacturer usage profiling and dynamic traffic monitoring into a client-side firewall. Our strategy ensures that, even if devices are compromised, their impact is minimized. Using the P4 language for programmable data planes, we prototype the firewall solutions and demonstrate that this solution can significantly reduce potential device exploitation.

On November 28--29, 2023, Northwestern University hosted a workshop titled "Towards Re-architecting Today's Internet for Survivability" in Evanston, Illinois, US. The goal of the workshop was to bring together a group of national and international experts to sketch and start implementing a transformative research agenda for solving one of our community's most challenging yet important tasks: the re-architecting of tomorrow's Internet for "survivability", ensuring that the network is able to fulfill its mission even in the presence of large-scale catastrophic events. This report provides a necessarily brief overview of two full days of active discussions.

The "Measuring Broadband America" program, run by the United States Federal Communications Commission (FCC), continually measures and releases data on the performance of consumer broadband access networks in the US. This paper presents a retrospective on the program, from its beginnings in 2010 to the present. It also reviews the underlying measurement approaches, philosophies, distinguishing features, and lessons learned over the program's duration thus far. We focus on fixed broadband access since it is the program component with the longest history. We also discuss future directions and challenges.

The FCC-proposed broadband consumer labels are designed to enable quality and price transparency, and thus competition, for residential broadband services. Key quality indicators for residential networks are download and upload speeds, typically the only quality-of-service differentiator disclosed at the time of purchase. Even as speed remains the most-commonly accepted quality-of-service indicator, its use in advertisements has been problematic. Often, the fine print indicates “up to” speeds, and the terms of service often disclaim any warranties on the speed advertised. Actual achieved speeds can vary significantly depending on the measurement methodology and on how the statistical variation in speeds across time and space is represented. We have compared several different ways of measuring speed and performance using the most recent FCC Measuring Broadband America (MBA) data, from 'classical' measures such as mean and median, to percentiles and the MBA 80/80 consistency metric. Our results show that well-performing networks can offer speeds close to or above their advertised number almost all the time and across all of their measurement locations, with 5th percentile measurements differing only marginally from the median or 80/80 speeds. Therefore, we argue that leaving the measurement method of speed to providers will likely lead to the use of the most favorable speed metric possible, particularly since the format will confer a notion of government-approved legitimacy on the numbers presented. Low-percentile cut-off metrics reward high-quality networks, without having to disclose hard-to-understand metrics such as packet loss, and makes it far less likely that a new customer finds themselves residing in a service territory with unreliable service.

This Viewpoint reviews previous interoperability efforts centered on electronic health record systems, draws lessons from the telecommunications industry in the standard setting, and identifies opportunities for and benefits of standard setting to promote interoperability in the medical device industry.

During the COVID-19 pandemic, many smaller conferences have moved entirely online and larger ones are being held as hybrid events. Even beyond the pandemic, hybrid events reduce the carbon footprint of conference travel and makes events more accessible to parts of the research community that have difficulty traveling long distances, while preserving most advantages of in-person gatherings. While we have developed a solid understanding of how to design virtual events over the last two years, we are still learning how to properly run hybrid events. We present guidelines and considerations-spanning technology, organization and social factors-for organizing successful hybrid conferences. This paper summarizes and extends the discussions held at the Dagstuhl seminar on "Climate Friendly Internet Research" held in July 2021.

It is our great pleasure to welcome you to the 30th edition of the IEEE International Conference on Network Protocols (ICNP).This year, we made every possible effort to bring ICNP back as an in-person event.The evolution of the COVID pandemic has not allowed us to organize a full in-person conference, but we are grateful to offer a hybrid format with many authors and participants physically attending.Continuing its tradition from the previous years, this year's ICNP features an extensive and exciting program that spans four days, covering a wide range of research topics related to network protocols, ranging from fundamental networking topics such as network congestion control and load balancing, to important trending topics such as novel wireless technologies and machine learning for networking.

Edge computing attempts to deliver low-latency services by offloading data storage and processing from remote data centers to distributed edge servers near end users, whereas network protocols, designed for centralized management, do not internally scale to distributed edge scenarios. In this paper, we establish the message dissemination support of MQTT, a de facto protocol for Internet of Things, for fully distributed edge networks. We summarize and formulate existing mechanisms, namely publication flooding and subscription flooding, and propose a topic-centric solution called selective subscription forwarding, which forwards subscriptions only when necessary by leveraging the topic containment of MQTT messages and therefore reduces inter-broker traffics. Evaluation results demonstrate that compared with existing solutions, more than 40% subscription traffic can be reduced with the proposed mechanism.

We propose and implement Directory-Based Access Control (DBAC), a flexible and systematic access control approach for geographically distributed multi-administration IoT systems. DBAC designs and relies on a particular module, IoT directory, to store device metadata, manage federated identities, and assist with cross-domain authorization. The directory service decouples IoT access into two phases: discover device information from directories and operate devices through discovered interfaces. DBAC extends attribute-based authorization and retrieves diverse attributes of users, devices, and environments from multi-faceted sources via standard methods, while user privacy is protected. To support resource-constrained devices, DBAC assigns a capability token to each authorized user, and devices only validate tokens to process a request.