Models And Methods For Task Matching In Spatial Crowdsourcing

Smart city has developed rapidly due to the proliferation of smart devices and the advancement of computing capabilities.It can improve the resource utilization efficiency of a city in different aspects,including the urban designing,the traffic optimization,the environmental protection,and the emergency response.Spatial crowdsourcing is a widely used solution for solving fundamental problems in a smart city.It utilizes the wisdom of the group and the carried intelligent equipments to complete spatial tasks,which are often labor-intensive if traditional solutions are used.In a spatial crowdsourcing process,the matching problem between tasks and participants is a key issue,because it determines the execution efficiency and quality of tasks.Therefore,this paper focuses on the matching problem in spatial crowdsourcing.Spatial crowdsourcing usually includes three components,the task publisher,the platform in cloud,and the task executor(i.e.,participants).The platform is responsible for matching the tasks and participant while satisfying the task requirements uploaded by task publishers and executor characteristics determined by participants.Compared with the common crowdsourcing,the spatial crowdsourcing emphasizes the spatial attributes of tasks.According to the different spatial requirements of tasks,spatial tasks can be di-vided into two categories,area tasks and location tasks.An area task indicates that the target of this task is an area,a location task indicates that the target of this task is one(or more)specific location.This paper focuses on the two kinds of categories and propose proper matching mechanisms,by tackling the challenges brought by the different spatial requirements of tasks and the mobility feature of participants.The main contributions of this paper include:(1)A trajectory segment selection mechanism is proposed for the area task.There are two kinds of redundancy in the existing literature,one is brought by the incomplete coverage assessment,while the other one is brought by the traditional participant selection process.Since paying for redundant data leads to budget waste,existing works cannot solve the participant selection problem commendably under limited budget.To address such issues,we first propose a coverage assessment considering both uniform coverage and maximum coverage,then design a trajectory segment selection scheme.(2)A profit-driven online participant selection mechanism is proposed for compres-sive spatial crowdsourcing.A crucial problem of spatial crowdsourcing is to maximize the profit of the platform,i.e.,the charge of a sensing task minus the payments to partici-pants that execute the task.In this paper,the profit is improved via the data reconstruction method,which brings new challenges as it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants.In order to tackle this prob-lem,we propose to predict the reconstruction quality via an exponential-based method.In addition,this paper analyzes the spatial dispersion of participants to further improve the reconstruction quality by using the 2-D entropy based optimization method.In particular,two Profit-driven Online Participant Selection(POPS)problems under different situations are studied,and multiple algorithms are proposed.(3)Ridesharing is a typical location task,this paper proposes a non-stop package delivery mechanism by utilizing multi-hop ridesharing.City-wide package delivery be-comes popular due to the dramatic rise of online shopping.It places a tremendous bur-den on the traditional logistics industry,which relies on dedicated couriers and is labor-intensive.Leveraging the ridesharing systems is a promising alternative,yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays.This paper proposes a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free.Specifically,a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting suc-cessive passengers.(4)A capacity assessment mechanism is proposed for the taxis-based logistics.Many efforts have been done to optimize the taxi-based logistics in recent literature.However,a fundamental problem still remains open,i.e.,measuring the maximum capacity of taxi-based logistics at the urban scale.In this paper,we first propose an accurate and efficient measurement mechanism to tackle this problem in the Non-stop package delivery method.Then,we expand our measurement mechanism to be used in other taxi-based package delivery methods after a few adaptations,including the One-hop method and the Stop-and-wait method.At last,we evaluate our measurement mechanism and compare the maximum urban capacity of various package delivery methods with a real-world dataset from an online taxi-taking platform.