6 research outputs found

    A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing

    Full text link
    Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International Conference on Microelectronics (ICM

    A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

    Full text link
    Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.Comment: Published in 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS

    Reassessment of the size of the Scopoli's Shearwater population at its main breeding site resulted in a tenfold increase: implications for the species conservation

    No full text
    International audienceScopoli's Shearwater (Calonectris diomedea) is a Procellariiform endemic to the Mediterranean Basin which is considered to be vulnerable in Europe due to recent local declines and its susceptibility to both marine and terrestrial threats. In the 1970s-1980s, its population size was estimated at 57,000-76,000 breeding pairs throughout the Mediterranean Basin, with the largest colony, estimated at 15,000-25,000 pairs, found on Zembra Island, Tunisia. The objectives of our study were to re-estimate the size of the breeding population on Zembra Island, to reassess the global population size of the species, and to analyse the implications of these findings on status and conservation of this species in the Mediterranean. Using distance sampling, we estimated the Zembra breeding population to be 141,780 pairs (95 % confidence interval 113,720-176,750 pairs). A review of the most recent data on populations of this species throughout the Mediterranean Basin led us to estimate its new global population size at 141,000-223,000 breeding pairs. Using the demographic invariant and potential biological removal approaches, we estimated the maximum number of adults which could be killed annually by all non-natural causes without causing a population decline to be 8800 (range 7700-9700) individuals, of which could be 3700 breeders. Although these results are less alarming in the context of species conservation than previously thought, uncertainties associated with global population size, trends and major threats still raise questions on the future of this species. More generally, we show how a monitoring strategy for a bird supposed to be relatively well known overall can be potentially misleading due to biases in survey design. The reduction of such biases would therefore appear to be an unavoidable prerequisite in cryptic species monitoring before any reliable inference on the conservation status of the species can be drawn