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This paper describes the implementation of a service to identify and geo-locate real world events that may be present as social activity signals in two different social networks. Specifically, we focus on content shared by users on Twitter and Instagram in order to design a system capable of fusing data across multiple networks. Past work has demonstrated that it is indeed possible to detect physical...
Some of the most widely deployed IoT devices in urban areas are smartphones in the possession of urban individuals. Their proliferation has led to the emergence of crowdsensing/crowdsourcing services, where humans collect data about their environment (using phones), and servers aggregate the data for various application purposes of interest. With the emergence of social media, a common alternative...
In this demo we present a tool that allows us to visualize the real world events on a map interface using the contents shared by users on Twitter and Instagram. We incorporate a novel algorithm thatanalyzes the data from both Twitter and Instagram for fusing the contents corresponding to the same event thereby enhancing the corroboration of the event detection techniques for the individualnetworks...
This paper presents the design and evaluation of GreenDrive, a smartphone-based system that helps drivers save fuel by judiciously advising on driving speed to match the signal phase and timing (SPAT) of upcoming signalized traffic intersections. In the absence of such advice, the default driver behavior is usually to accelerate to (near) the maximum legally allowable speed, traffic conditions permitting...
Sensor networks and the Internet of Things motivate novel classes of job scheduling problems where each "job" corresponds to downloading some data object from the sensor network, i.e., querying the network for some portion of its current state. The purpose of scheduling a set of jobs may be to support decision tasks, where the user will make a choice, prior to a deadline, informed by all...
This paper addresses the problem of choosing the right sources to solicit data from in sensing applications involving broadcast channels, such as those crowdsensing applications where sources share their observations on social media. The goal is to select sources such that expected fusion error is minimized. We assume that soliciting data from a source incurs a cost and that the cost budget is limited...
This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from...
Emerging distributed in-memory computing frameworks, such as Apache Spark, can process a huge amount of cached data within seconds. This remarkably high efficiency requires the system to well balance data across tasks and ensure data locality. However, it is challenging to satisfy these requirements for applications that operate on a collection of dynamically loaded and evicted datasets. The dynamics...
In this demo, we introduce the tweet-based newsfeed summary service, called iApollo, running on a named data network (NDN) stack. This novel application provides a customized newsfeed service to individual readers based on their interests. Data sampling is essential in iApollo because of the large volume of tweets. Espresso, the automatic naming agent, translates this sampling problem into the simple...
Recent work suggested that, in the age of data overload produced by sensors, social media, and IoT devices, a key new type of network transport protocols will be one that offers representative summaries of requested data, retrieved at a consumer-controlled degree of granularity. Given the over-abundance of data, consumers will seldom need all data on a topic, but rather will increasingly favor an...
This paper develops an algorithm that exploits picture-oriented social networks to localize urban events. We choose picture-oriented networks because taking a picture requires physical proximity, thereby revealing the location of the photographed event. Furthermore, most modern cell phones are equipped with GPS, making picture location, and time metadata commonly available. We consider Instagram as...
This paper presents unsupervised algorithms to uncover polarization in social networks (namely, Twitter) and identify polarized groups. The approach is language-agnostic and thus broadly applicable to global and multilingual media. In cases of conflict, dispute, or situations involving multiple parties with contrasting interests, opinions get divided into different camps. Previous manual inspection...
This paper presents a new schedulability test for safety-critical software undergoing a transition from single-core to multicore systems — a challenge faced by multiple industries today. Our migration model consists of a schedulability test and execution model. Its properties enable us to obtain a utilization bound that places an allowable limit on total task execution times. Evaluation results demonstrate...
In this demo we present a tool that allows us to visualize the real world events on a map interface using the contents shared by users on Twitter and Instagram. Social networks have become popular in recent times for sharing contents about observations made by users. Our tool incorporates a novel algorithm that analyzes the data from both Twitter and Instagram for fusing the contents corresponding...
The Internet of Things heralds a new generation of data-centric applications, where controllers connect to large numbers of heterogeneous sensing devices. We consider a model, where the control loop does not execute periodically. Instead, controllers are prompted by contextual cues to make one-off decisions, resulting in sporadic activations. Since the need for data arises only sporadically, sensors...
In this paper, we present the "Slow Start Problem" in participatory sensing applications where a service is provided based on data collected by participants. The slow start problem refers to the initial stage in participatory sensing service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned...
The paper considers the challenge of maximizing the quality of information collected to meet decision needs of real-time Internet-of-Things applications. A novel scheduling model is proposed, where applications need multiple data items to make decisions, and where individual data items can be captured at different levels of quality. We assume the existence of a single bottleneck over which data objects...
This paper develops a simplified dependency model for sources on social networks that is shown to improve the quality of fact-finding -- assessing veracity of observations shared on social media. Recent literature developed a mathematical approach for exploiting social networks, such as Twitter, as noisy sensor networks that report observations on the state of the physical world. It was shown that...
"Cold Start" in participatory sensing applications refers to the initial stage in service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned from data, offer a way to generalize from sparse observations, but the models themselves need to be statistically reliable to offer a reliable service...
The paper develops a recursive state estimator for social network data streams that allows exploitation of social networks, such as Twitter, as sensor networks to reliably observe physical events. Recent literature suggested using social networks as sensor networks leveraging the fact that much of the information upload on the former constitutes acts of sensing. A significant challenge identified...
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