With the surge in cloud storage adoption, enterprises face challenges managing data duplication and exponential data growth. Deduplication mitigates redundancy, yet maintaining redundancy ensures high availability, incurring storage costs. Balancing these aspects is a significant research concern. We propose FASTEN, a distributed cloud storage scheme ensuring efficiency, security, and high availability. FASTEN achieves fault tolerance by dispersing data subsets optimally across servers and maintains redundancy for high availability. Experimental results show FASTEN's effectiveness in fault tolerance, cost reduction, batch auditing, and file and block-level deduplication. It outperforms existing systems with low time complexity, strong fault tolerance, and commendable deduplication performance.

There is an increasing interest in crowd monitoring as we return to normal life after the Covid-19 pandemic. Some major obstacles to implementing a monitoring system are: privacy, scalability, and accuracy. Several entities have developed their own branded safety reassurance programs using the Internet of Things and sensors during the pandemic, and many cities are planning for the post-pandemic era. However, the lack of inclusiveness and the fear of personal information leakage often limit their usage. To this end, we developed X-Fidence, a real-time density monitoring system to avoid crowds which preserves user privacy. X-Fidence anonymously tracks visitors in certain places using Bluetooth technology. The system consists of a signal receiver module, a mobile application, and a monitoring server where the citywide current occupancy data syncs automatically. The mobile application provides density data in real-time and average historic crowds information that helps to make decisions more confidently. The X-Fidence mobile app warns residents if any area experiences more visitors than maximum safe capacity. We also present our results from an experiment to measure the feasibility and accuracy of the system.

Alerts and warnings are intended to educate the public and keep them safe in an emergency or natural disaster. Historically, public alerts and warnings have been delivered mainly via broadcast media and roadside signage. However, deploying these signals is time-consuming and they may not be noticed by people who need them. With the increasing use of smart devices and other technological advancements, the nation’s alerting capabilities must be upgraded to target vulnerable sub-populations more effectively. An easy-to-deploy, automatic alert/warning system with no need for a line of sight would be useful. To this end, we developed Insight – an alerting system that detects signals from Bluetooth beacons indicating danger zones [1]. It requires no Internet or communication infrastructure and thus, it is robust to communications failures during catastrophes. Here, we demonstrate the use of the Insight mobile application and the underlying ad hoc Beacon infrastructure.

In the United States, an estimated 7,005 (crude rate 2.13) pedestrians were killed in traffic crashes in 2020, according to the Centers for Disease Control and Prevention (CDC). This statistic is currently increasing annually and research suggests that distraction by smartphones may be a primary reason for the increasing number of pedestrian injuries and deaths. Timely interruptions may alert inattentive pedestrians and prevent fatalities. To this end, we developed StreetBit, a Bluetooth beacon-based system that warns distracted pedestrians with a visual and/or audible interruption when they approach a potentially dangerous traffic intersection while distracted by their smartphones. We posit that by using StreetBit, we can educate distracted pedestrians and elicit behavioral change to reduce or remove smartphone-based distractions when they enter and cross roadways. To demonstrate the feasibility of StreetBit, we conducted a field study with 385 participants. Results show that the system demonstrates adequate feasibility and behavior change in response to the StreetBit program.

Video Conferencing has emerged as a new paradigm of communication in the age of COVID-19 pandemic. This technology is allowing us to have real-time interaction during the social distancing era. Even before the current crisis, it was increasingly commonplace for organizations to adopt a video conferencing tool. As people adopt video conferencing tools and access data with potentially less secure equipment and connections, meetings are becoming a target to cyber attackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. To design the video conferencing system's security and privacy model, an exhaustive threat model must be adopted. Threat modeling is a process of optimizing security by identifying objectives, vulnerabilities, and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.

The purpose of alerts and warnings is to provide necessary information to the public that will lead to their safety in emergencies. The nation's alerting capabilities need to evolve and progress with the extensive use of smartphones, and newer technologies become available, especially to be more precisely targeted to sub-populations at risk. Historically, this has been a challenge as the delivery of alerts and warning messages to the public is primarily through broadcast media and signs. However, deploying such signs takes time and may not be visible to people imminent of natural hazards. Especially for road closing, marking hazards, emergency evacuation, etc., it would be beneficial to have an easy-to-deploy and automated alert/warning system that requires no line of sight. To this end, we have developed Insight - a Bluetooth beacon-based system that uses a smartphone application to sense signals from beacons marking hazard zones. The system does not require any Internet or communication infrastructure and therefore, it is resilient to breakdowns in communications during disasters. To demonstrate the feasibility of Insight, we conducted a study in an urban university campus location. The system demonstrated adequate usability and feasibility.

The safety of distracted pedestrians presents a significant public health challenge in the United States and worldwide. An estimated 6,704 American pedestrians died and over 200,000 pedestrians were injured in traffic crashes in 2018, according to the Centers for Disease Control and Prevention (CDC) [1]. This number is increasing annually and many researchers posit that distraction by smartphones is a primary reason for the increasing number of pedestrian injuries and deaths. One strategy to prevent pedestrian injuries and death is to use intrusive interruptions that warn distracted pedestrians directly on their smartphones. To this end, we developed StreetBit, a Bluetooth beacon-based mobile application that alerts distracted pedestrians with a visual and/or audio interruption when they are distracted by their smartphones and are approaching a potentially-dangerous traffic intersection. In this paper, we present the background, architecture, and operations of the StreetBit Application.