On Achieving Diversity in the Presence of Outliers in Participatory Camera Sensor Networks

This paper addresses the problem of collection and delivery of a representative subset of pictures, in participatory camera networks, to maximize coverage when a significant portion of the pictures may be redundant or irrelevant. Consider, for example, a rescue mission where volunteers and survivors of a large-scale disaster scout a wide area to capture pictures of damage in distressed neighborhoods, using handheld cameras, and report them to a rescue station. In this participatory camera network, a significant amount of pictures may be redundant (i.e., similar pictures may be reported by many) or irrelevant (i.e., may not document an event of interest). Given this pool of pictures, we aim to build a protocol to store and deliver a smaller subset of pictures, among all those taken, that minimizes redundancy and eliminates irrelevant objects and outliers. While previous work addressed removal of redundancy alone, doing so in the presence of outliers is tricky, because outliers, by their very nature, are different from other objects, causing redundancy minimizing algorithms to favor their inclusion, which is at odds with the goal of finding a representative subset. To eliminate both outliers and redundancy at the same time, two seemingly opposite objectives must be met together. The contribution of this paper lies in a new prioritization technique (and its in-network implementation) that minimizes redundancy among delivered pictures, while also reducing outliers.unpublishedis peer reviewe

Denial in DTNs

Disruption-tolerant network (DTN) is an intermittently connected network where the traditional end-to-end data communication between a source-destination pair is hardly possible. Instead, nodes opportunistically replicate the same packet several times and try to deliver them through multiple paths. Due to this multi-copy forwarding scheme, denial of service (DoS) attacks in DTN is believed to be a non-trivial task. Unlike classical DoS attacks for the Internet where an attacker renders some service at a particular node unavailable for legitimate users, in DTN, attackers do not have any node-level attack objective. They rather seek to diminish some network-wide global performance (e.g., total packet delivery). In this report, we describe a few possible DoS attacks for DTNs and propose a set of handful countermeasures against those attacks under the assumptions of our threat model. We show that in the presence of adversaries network performance declines and our protection mechanisms attempt to limit the overall effect to a considerable amount. We simulate our scheme in ns-2 and show that significant trade-off exists in choosing protection mechanisms commensurately with attack models and resource utilization.unpublishednot peer reviewe

Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties.

The spread of the COVID-19 pandemic is observed to follow the shape of "waves" (i.e., the rise and fall of population-adjusted daily new infection cases with time). Different geographic regions of the world have experienced different position and span of these waves over time. The presence and strength of these waves broadly characterize the dynamics of the pandemic spread in a given area, so their characterization is important to draw meaningful intervention and mitigation plans tailored for that area. In this paper, we propose a novel technique to represent the trend of COVID-19 spread as a sequence of a fixed-length text string defined on three symbols: R (rise), S (Steady), and F (fall). These strings, termed as trend strings, enabled us searching for specific patterns in them (such as for waves). After analyzing county-level infection data, we observe that, US counties-despite their wide variation in trend strings-can be grouped into a number of heterogeneous classes each of which might have a representative COVID spread pattern over time (in terms of presence and propensity of waves). To this end, we conduct a latent class analysis to cluster 3142 US counties into four distinct classes based on their wave characteristics for one year pandemic data (January 2020 to January 2021). We observe that counties in each class have distinct socio-demographics, location, and human mobility characteristics. In short summary, counties have differing number of waves (class 1 counties have only one wave and class 3 counties have three) and their positions also vary (class 1 had the wave later in the year whereas class 3 had waves throughout the year). We believe that this way of characterizing pandemic waves would provide better insights in understanding the complex dynamics of COVID-19 spread and its future evolution, and would, therefore, help in taking class-specific policy interventions

On Schedulability and Time Composability of Multisensor Data Aggregation Networks

This paper develops a framework to analyze the latency and delay composition of work???ows in a real-time networked aggregation system. These work???ows are characterized by different inputs that are processed along parallel branches that eventually merge or fuse to compute the aggregation result. The results for each ???ow must be produced within certain end-to-end deadlines or else the information would become stale and useless. We consider an end-to-end view of the aggregation system that allows us to derive a much tighter analysis of the end-to-end delay compared to traditional analysis techniques. The framework extends results developed by the authors recently to analyze end-to-end latency of various work???ow topologies. We then provide a reduction of the aggregation network system to an equivalent hypothetical uniprocessor for the purposes of schedulability analysis. Extensive simulations show that latency bound obtained from the analysis framework is signi???cantly more accurate than that of traditional analysis techniques.unpublishednot peer reviewe

Structural modeling of COVID-19 spread in relation to human mobility.

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly

PhotoNet: A Similarity-aware Picture Delivery Service for Situation Awareness

We propose PhotoNet, a picture delivery service for camera sensor networks. PhotoNet is motivated by the needs of disaster-response applications, where a group of survivors and first responders may survey damage and send images to a rescue center in the absence of a functional communication infrastructure. The protocol runs on mobile devices, handling opportunistic forwarding (when they come in contact) and in-network storage. It assigns priorities to images for forwarding and replacement depending on the degree of similarity (or dissimilarity) among them, such that scarce resources are assigned to delivery of most ???deserving??? content first. Prioritization aims at reducing semantic redundancy such as that between pictures of the same scene at the same location taken from slightly different angles. This is in contrast to redundancy among identical objects and among time series data. PhotoNet delivers more diverse pictures in terms of event coverage suppressing logically redundant content belonging to the same event. We show that, in resource constrained networks, reducing semantic redundancy can significantly improve the utility of the service.W911NF-09-2-0053unpublishednot peer reviewe