A Mobility Model for Crowd Sensing Simulation

A Mobility Model for Crowd Sensing Simulation

Jose Mauricio Nava Auza (Center for Studies in Telecommunications, Pontifical Catholic University of Rio de Janeiro PUC/Rio, Rio de Janeiro, Brazil) and Jose Roberto B. de Marca (Center for Studies in Telecommunications, Pontifical Catholic University of Rio de Janeiro PUC/Rio, Rio de Janeiro, Brazil)
DOI: 10.4018/IJITN.2017010102


Mobile Crowd Sensing (MCS) is a class of sensor networks that uses mobile devices for large scale sensing. These networks have some very specific characteristics because of human (smartphone owners) involvement in its operations. Hence, it is important to have a model that takes into account the unique characteristics and opportunities of human mobility. In this paper, the authors present a Mobility Model for the ns-3 platform that considers human activities in a specific scenario and a simulation example to validate their model.
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1. Introduction

In recent years we have observed a great progress in wireless sensor networks. The complexity of its applications has also increased causing the need of a higher number of nodes to cover bigger areas and enhanced computing capabilities. To use a traditional sensor network for a scenario of large scale sensing, we need a vast number of sensor nodes to guarantee the coverage and have an enough amount of data, this makes the deployment more expensive. The complexity of installation and maintenance should also be considered as installing a large number of nodes in an urban or rural area could be quite complicated.

Fortunately, at the same time, we witnessed the advances of mobile devices (e.g., wearable devices, smartphones, music players, tablets) and their increasing popularity. One important indicator of the growth of these products is the worldwide combined shipment, because it demonstrates the high demand of these devices. According to Gartner, Inc. (information technology research and advisory company) worldwide combined shipment of devices are expected to reach 2.4 billion units in 2016 (Woods & Van der Meulen, 2016), this amount represents almost a two percent increase from 2015. The expected increase for 2018 is more than six percent. Considering that almost all of these devices have internet access, the number of internet users around the world will keep growing. Nowadays there is almost three and a half billion internet users, considering as a user, a device that has internet access. The world population is near six and a half billion people, if we consider the whole population, the number of connected devices per person may seem low. However, reducing the population sample to people actually connected to the internet, the density of connected devices per person rises dramatically. According to Evans (2011) the Cisco Internet Business Solutions Group predicts that there will be fifty billion devices connected to the internet by 2020.

Another important characteristic of these devices is that they have many built-in sensors (e.g., compass, GPS, camera, microphone). Thanks to these characteristics, mobile devices represent an opportunity to produce large scale sensing of the physical world and share the data via Internet. This new type of sensing is known, as Mobile Crowd Sensing (MCS) or people centric sensing, enables various applications and recently has been the focus of several publications (Baguena et al., 2015; Guo et al., 2014; Higuchi et al., 2014). A MCS network begins with a controller that gives a task to a certain group of people to collect some information using their sensing and computing devices. Once the task is completed, they share the information using the Internet. Using resources of devices that are already deployed in the field and in movement because of the human mobility has opened up a new range of possibilities such as: monitoring the traffic congestion of a whole city (Mohan et al., 2008; Hull et al., 2006; Pan et al., 2013), parking solutions (Chen & Liu, 2016; Villanueva et al., 2015) and measuring pollution levels in an urban area (Dutta et al., 2009). It is expected that in the near future more sensors will be added to the mobile devices bringing an infinite amount of opportunities to enable better management of cities, systems and offer a better control of our daily activities.

MCS gives us a number of advantages and solutions to some design constraints, that the classic sensor networks have. Mobile devices have more storage and computing resources than a regular sensor. One of the biggest constraints in wireless sensor networks (WSN) was the energy consumption. In MCS the users take care of charging their devices in a daily basis. A network can be deployed at lower cost because the mobile devices are already spread in all the potential areas the network is going to cover. MCS is more extensible because if more nodes are needed we will just have to recruit more users. Another important characteristic of MCS is the mobility of the devices; since they belong to humans, their movements have pretty specific characteristics. The mobility pattern of humans is defined by the paths they use to reach places where they conduct their daily activities (Ekman et al., 2008; Karamshuk et al., 2011). That is why human mobility will define the connectivity, topology and coverage of an MCS network.

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