Design of a Local Information Incentive Mechanism for Mobile Crowdsensing.

Jose Mauricio Nava Auza,Jose Roberto Boisson De Marca,Glaucio Lima Siqueira

doi:10.3390/s19112532

Jose Mauricio Nava Auza, Jose Roberto Boisson De Marca + Show 1 more

Open Access

https://doi.org/10.3390/s19112532

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Abstract

The world of telecommunications has seen the growing popularity of mobile devices and its massive technological advancements and innovations (e.g., smartphones, smart watches, among others). One critical particularity is that these devices have a series of built-in sensors and continuous network connectivity. Therefore, they present a great opportunity to perform large-scale sensing of different activities in the physical world. This new sensor application, better known as Mobile crowd-sensing (MCS), has lately become a focus of research. One of the challenges when developing a MCS-based network is to attract and convince users to participate. In this paper, we present a framework for MCS that includes a model to represent the behavior of the users and a novel incentive mechanism. The model aims to characterize the behavior of users considering the availability of their resources and the non-homogeneity of their responses. The incentive mechanism proposed assigns different values of incentives and in it the users only consider their local information to decide their participation in the framework. The performance of the proposed framework is evaluated through simulations. The results allow us to prove the uncertainty of participation of the users and that they react in different ways to the incentives offered. They also prove that the incentive mechanism estimates satisfactorily the type of users and the incentive that will be offered to each user. In addition, we show the advantages of an incentive mechanism that considers different values of payments.

Highlights

Wireless sensor networks are one of the areas that have experienced a significative growth over the last few years
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Summary

Introduction

Wireless sensor networks are one of the areas that have experienced a significative growth over the last few years. The advances in research and industry have brought more complexity to its applications, demanding the need for enhanced computing capabilities and a higher number of nodes to cover bigger areas. Note that a traditional sensor network for a large-scale sensing scenario needs a vast number of sensor nodes in order to possess the sufficient amount of data and guarantee the coverage. We witnessed the advances of mobile devices (e.g., wearable devices, smartphones, music players, tablets) and their increasing popularity. According to the information technology research and advisory company Gartner Incorporation the worldwide combined shipment of devices will reach 2.4 billion units in 2019 [1] proving the high demand of these devices

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