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Distributed dataset synchronization, or Sync in short, plays the role of a transport service in the Named Data Networking (NDN) architecture. A number of NDN Sync protocols have been developed over the last decade. In this paper, we conduct a systematic examination of NDN Sync protocol designs, identify common design patterns, reveal insights behind different design approaches, and collect lessons learned over the years. We show that (i) each Sync protocol can be characterized by its design decisions on three basic components - dataset namespace representation, namespace encoding for sharing, and change notification mechanism, and (ii) two or three types of choices have been observed for each design component. Through analysis and experimental evaluation, we reveal how different design choices influence the latency, reliability, overhead, and security of dataset synchronization. We also discuss the relationship between transport and application naming, the implications of namespace encoding for Sync group scalability, and the fundamental reason behind the need for Sync Interest multicast. 

Named Data Networking (NDN) secures network communications by requiring all data packets to be signed upon production. This requirement makes usable and efficient NDN certificate issuance and revocation essential for NDN operations. In this paper, we first investigate and clarify core concepts related to NDN certificate revocation, then proceed with the design of CertRevoke, an NDN certificate revocation framework. CertRevoke utilizes naming conventions and trust schema to ensure certificate owners and issuers legitimately produce in-network cacheable records for revoked certificates. We evaluate the security properties and performance of CertRevoke through case studies. Our results show that deploying CertRevoke in an operational NDN network is feasible. 

Stressful conversation is a frequently occurring stressor in our daily life. Stressors not only adversely affect our physical and mental health but also our relationships with family, friends, and coworkers. In this paper, we present a model to automatically detect stressful conversations using wearable physiological and inertial sensors. We conducted a lab and a field study with cohabiting couples to collect ecologically valid sensor data with temporally-precise labels of stressors. We introduce the concept of stress cycles, i.e., the physiological arousal and recovery, within a stress event. We identify several novel features from stress cycles and show that they exhibit distinguishing patterns during stressful conversations when compared to physiological response due to other stressors. We observe that hand gestures also show a distinct pattern when stress occurs due to stressful conversations. We train and test our model using field data collected from 38 participants. Our model can determine whether a detected stress event is due to a stressful conversation with an F1-score of 0.83, using features obtained from only one stress cycle, facilitating intervention delivery within 3.9 minutes since the start of a stressful conversation.