<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bada, Mousaab</style></author><author><style face="normal" font="default" size="100%">Djallel Eddine Boubiche</style></author><author><style face="normal" font="default" size="100%">Nasreddine Lagraa</style></author><author><style face="normal" font="default" size="100%">Kerrache, Chaker Abdelaziz</style></author><author><style face="normal" font="default" size="100%">Imran, Muhammad</style></author><author><style face="normal" font="default" size="100%">Shoaib, Muhammad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A policy-based solution for the detection of colluding GPS-Spoofing attacks in FANETs</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Part A: Policy and PracticeTransportation Research Part A: Policy and Practice</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">149</style></volume><pages><style face="normal" font="default" size="100%">300-318</style></pages><isbn><style face="normal" font="default" size="100%">0965-8564</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Djallel Eddine Boubiche</style></author><author><style face="normal" font="default" size="100%">Imran, Muhammad</style></author><author><style face="normal" font="default" size="100%">Maqsood, Aneela</style></author><author><style face="normal" font="default" size="100%">Shoaib, Muhammad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mobile crowd sensing &amp;ndash; Taxonomy, applications, challenges, and solutions</style></title><secondary-title><style face="normal" font="default" size="100%">Computers in Human BehaviorComputers in Human Behavior</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">101</style></volume><pages><style face="normal" font="default" size="100%">352-370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional&amp;nbsp;sensor&amp;nbsp;networks. Then, a&amp;nbsp;taxonomy&amp;nbsp;of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Djallel Eddine Boubiche</style></author><author><style face="normal" font="default" size="100%">Imran, Muhammad</style></author><author><style face="normal" font="default" size="100%">Maqsood, Aneela</style></author><author><style face="normal" font="default" size="100%">Shoaib, Muhammad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mobile crowd sensing–taxonomy, applications, challenges, and solutions</style></title><secondary-title><style face="normal" font="default" size="100%">Computers in Human BehaviorComputers in Human Behavior</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">101</style></volume><pages><style face="normal" font="default" size="100%">352-370</style></pages><isbn><style face="normal" font="default" size="100%">0747-5632</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>