Centre de documentation
scientifique

Task selection in spatial crowdsourcing from worker’s perspective / Dingxiong Deng in Geoinformatica, vol 20 n° 3 (July - September 2016)  

Public [article]  inGeoinformatica > vol 20 n° 3 (July - September 2016) . - pp 529 – 568 Titre : Task selection in spatial crowdsourcing from worker’s perspective Type de document : Article/Communication Auteurs : Dingxiong Deng, Auteur ; Cyrus Shahabi, Auteur ; Ugur Demiryurek, Auteur ; Linhong Zhu, Auteur Année de publication : 2016 Article en page(s) : pp 529 – 568 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appareil portable [Termes IGN] approximation [Termes IGN] données localisées des bénévoles [Termes IGN] données spatiotemporelles [Termes IGN] géopositionnement [Termes IGN] ordonnancement de tâches [Termes IGN] programmation dynamique [Termes IGN] prospective [Termes IGN] téléphonie mobile [Termes IGN] travail coopératif Résumé : (auteur) With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we propose different approximation algorithms. Finally, to strike a compromise between efficiency and accuracy, we present a progressive algorithms. We conducted a thorough experimental evaluation with both real-world and synthetic data on desktop and mobile platforms to compare the performance and accuracy of our proposed approaches. Numéro de notice : A2016-380 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0251-4 En ligne : http://dx.doi.org/10.1007/s10707-016-0251-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81147 [article]