Spatial Task Management in Mobile Crowd Sensing Sample Clauses

Spatial Task Management in Mobile Crowd Sensing. Spatial tasks require the participant to be at a specific place in order to fulfill a task. With the increasing use of smart phones with integrated GPS, the number of applications in which, tasks are assigned based on the location of participants has also grown. Monitoring and reporting speed, traffic, and road conditions are some examples of spatial tasks. Some of these tasks are opportunistic; they run in the background with little or no involvement from the participant which can be used to detect traffic speed, bumps, inclination, and elevation of the road [30, 42, 72]. In contrast, participatory tasks may ask the users to report potholes or the quality of the road as they drive around in their normal commute [31,90]. Spatial tasks are not restrained to reporting road conditions. For example, a participatory spatial task could require that the participants search for the best prices located at different stores and report them to provide other users with the best prices in the region [15, 28]. Spatial task management in mobile crowd sensing can be categorized into two major approaches: (i) Autonomous task selection, and (ii) Coordinated task assignment. In autonomous task selection, participants select their tasks autonomously from a set of existing tasks received from a task distribution entity. They might or might not inform the distributor about their selection choices. Examples of these approaches can be found in [20–22, 27, 84]. Since the selected tasks in these methods are not optimized globally, these approaches might not be efficient with respect to sensing cost or global utility. Our proposed task selection approach in Chapter 4 seeks to overcome this shortcoming by utilizing RkNN queries to increase the global task coverage while preserving privacy. Coordinated task assignment aims at optimizing the process of data sensing by efficient assessment of available sensing resources to meet the requirements of ap- plications. The criteria for optimization of task assignment include sensing costs, coverage of targets of interest, quality, and credibility of sensed data. Examples of this approach can be found in [26, 50, 51, 79, 80, 85]. Xxxxx et al. [79] proposed a coverage-based task assessment that finds the least costly subset of participants to achieve the coverage goal. Xxxxxxx-Xxxx et al. [85] also proposed a coverage-based task assignment method for assigning viewpoints to a group of moving participants. Our proposed task assignment in Chapter ...