Monitoring crowds is receiving much attention. An increasingly popular technique is to scan mobile devices, notably smartphones. We take a look at scanning such devices based on transmitted WiFi messages. Although research on capturing crowd patterns using WiFi detections has been done, there are not many published results when it comes to tracking movements. This is not surprising when realizing that the data provided by WiFi scanners is susceptible to many seemingly erroneous and missed detections, caused by the use of randomized network addresses, overlap between scanners, high variance in WiFi detection ranges, among other sources. In this paper, we investigate various techniques for cleaning up sets of raw detections to sets that can subsequently be used for crowd analytics. To this end, we introduce two different quality metrics to measure the effects of applying various data filters. We test our approach using a data set collected from 27 WiFi scanners spread across the downtown area of a Dutch city where at that time a 3-day multi-stage festival took place attended by some 130,000 people.

No abstract

No abstract

No abstract

When mobile devices are unable to establish direct communication, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to facilitate communication through opportunistic networks. These types of networks are formed when mobile devices communicate only using short-range transmission protocols, usually when users are close, can help applications exchange data. Routes are built dynamically, since each mobile device is acting according to the store-carry-and-forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks the routing protocol is of vital importance -and today we witness quite a number of routing algorithms that have been proposed to maximize the success rate of message delivery whilst minimizing the communication cost. Such protocols take advantage of the devices' history of contacts, or information about users carrying the mobile devices, to make their forwarding decision. Our contribution in this paper is two-fold: First, we present a new simplified, fast simulator, designed to minimize the work needed to conduct extensive tests for opportunistic routing algorithm on multiple traces; next we present an extensive analysis of several of the most popular routing algorithms through extensive simulations conducted using our simulation platform. We highlight their pros and cons in different scenarios, considering different realworld mobility data traces.