Conference on Computer Communications Workshops 

About: Conference on Computer Communications Workshops is an academic conference. The conference publishes majorly in the area(s): Cloud computing & Wireless network. Over the lifetime, 1871 publications have been published by the conference receiving 22877 citations.

Predicting Flu Trends using Twitter data

Abstract: Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes collecting influenza-like illness (ILI) activity data from “sentinel” medical practices. Typically there is a 1–2 week delay between the time a patient is diagnosed and the moment that data point becomes available in aggregate ILI reports. In this paper we present the Social Network Enabled Flu Trends (SNEFT) framework, which monitors messages posted on Twitter with a mention of flu indicators to track and predict the emergence and spread of an influenza epidemic in a population. Based on the data collected during 2009 and 2010, we find that the volume of flu related tweets is highly correlated with the number of ILI cases reported by CDC. We further devise auto-regression models to predict the ILI activity level in a population. The models predict data collected and published by CDC, as the percentage of visits to “sentinel” physicians attributable to ILI in successively weeks. We test models with previous CDC data, with and without measures of Twitter data, showing that Twitter data can substantially improve the models prediction accuracy. Therefore, Twitter data provides real-time assessment of ILI activity.

Characterizing and classifying IoT traffic in smart cities and campuses

Abstract: Campuses and cities of the near future will be equipped with vast numbers of IoT devices. Operators of such environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This paper proposes the use of network traffic analytics to characterize IoT devices, including their typical behaviour mode. We first collect and synthesize traffic traces from a smart-campus environment instrumented with a diversity of IoT devices including cameras, lights, appliances, and health-monitors; our traces, collected over a period of 3 weeks, are released as open data to the public. We then analyze the traffic traces to characterize statistical attributes such as data rates and burstiness, activity cycles, and signalling patterns, for over 20 IoT devices deployed in our environment. Finally, using these attributes, we develop a classification method that can not only distinguish IoT from non-IoT traffic, but also identify specific IoT devices with over 95% accuracy. Our study empowers operators of smart cities and campuses to discover and monitor their IoT assets based on their network behaviour.

A Reservation-based Smart Parking System

Abstract: Finding a parking space in most metropolitan areas, especially during the rush hours, is difficult for drivers. The difficulty arises from not knowing where the available spaces may be at that time; even if known, many vehicles may pursue very limited parking spaces to cause serious traffic congestion. In this paper, we design and implement a prototype of Reservation-based Smart Parking System (RSPS) that allows drivers to effectively find and reserve the vacant parking spaces. By periodically learning the parking status from the sensor networks deployed in parking lots, the reservation service is affected by the change of physical parking status. The drivers are allowed to access this cyber-physical system with their personal communication devices. Furthermore, we study state-of-the-art parking policies in smart parking systems and compare their performance. The experiment results show that the proposed reservation-based parking policy has the potential to simplify the operations of parking systems, as well as alleviate traffic congestion caused by parking searching.

An Energy Saving Routing Algorithm for a Green OSPF Protocol

Abstract: In this paper we analyze the challenging problem of energy saving in IP networks. A novel network-level strategy based on a modification of current link-state routing protocols, such as OSPF, is proposed; according to this strategy, IP routers are able to power off some network links during low traffic periods. The proposed solution is a three-phases algorithm: in the first phase some routers are elected as "exporter" of their own Shortest Path Trees (SPTs); in the second one the neighbors of these routers perform a modified Dijkstra algorithm to detect links to power off; in the last one new network paths on a modified network topology are computed. Performance study shows that, in an actual IP network, even more than the 60% of links can be switched off.

A First Step Towards Understanding Popularity in YouTube

Abstract: Being popular in YouTube is becoming a fundamental way of promoting one's self, services or products. In this paper, we conduct an in depth study of fundamental properties of video popularity in YouTube. We collect and study arguably the largest dataset of YouTube videos, roughly 37 million, accounting for 25% of all YouTube videos. We analyze popularity in a comprehensive fashion by looking at properties and patterns in time and considering various popularity metrics. We further study the relationship of the popularity metrics and we find that four of them are highly correlated (viewcount, #comments, #ratings, #favorites) while the fifth one, the average rating, exhibits very little correlation with the other metrics. We also find a "magic number" in the average behavior of videos: for every 400 times a video is viewed, we have one of each of the following user actions: leaving a comment, rating the video and adding to one's favorite set.