An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Abstract

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants' handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users' incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users' incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes. . 2015. An event-driven QoI-aware participatory sensing framework with energy and budget constraints.

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

“…Existing studies show that knowing the worker mobility pattern a priori can improve the efficiency of the task assignment [Ji et al 2016;Zhang et al 2015]. Even though, our solution does not consider individual worker mobility pattern, i.e.…”

“…It is worth noting that these studies focus on worker/task matching problem; however, our aim is to select the best workers to maximize task coverage. Similar to [Zhang et al 2015;Tran-Thanh et al 2013], the budget in [Miao et al 2016] refers to payment to workers, while in our study, we consider budget as the number of workers to select and focus on allocating a total budget across multiple time periods.…”

Message Relaying and Collaboration Motivating for Mobile Crowdsensing Service: An Edge‐Assisted Approach

“…Considering different incentive requirements, associated sensing capabilities, and uncontrollable mobility, a multitask-oriented QoI (Quality of Information) optimization problem is discussed and converted to a nonlinear knapsack problem. Zhang [18,19] proposed an event-driven QoI-aware participatory sensing framework with energy and budget constraints, where the main method is boundary detection. He [20] devised an efficient local ratio based algorithm and designed a motivating mechanism that decides the fair prices of sensing tasks.…”

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

“…A maximum sensing data credibility problem is defined then a trustable participants selection algorithm is presented to obtain a near optimal solution. [19] proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints and a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. However, to satisfy the constraints of the budget, these papers still only focus on selecting participants rather than tasks.…”