QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints

Abstract

Abstract-By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called "DPS," which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

“…There are many existing studies [29][30][31][32] that focus on the incentive mechanism. This paper summarizes the existing research into the following categories.…”

“…This type of incentive mechanism achieves efficient data collection through selecting high QCS reporters. Data coverage is also studied by Song et al [32], which recruits the most matching participants to maximize coverage. But the actual performance of these strategies is not stratifying due to three reasons as follows.…”

“…Crowd sensing (CS) refers to sensing a wide range of human activities and their surrounding environment by kinds of sensing devices such as smartphones, iPad, or sensor nodes [32,[39][40][41][42]. Because the sensing devices is so vast, it is given a new name crowdsourcing networks (CN) [43].…”

“…Because the sensing devices is so vast, it is given a new name crowdsourcing networks (CN) [43]. Due to quick development of mobile sensing devices, collecting the data by these devices is so rich, real-time, and low cost that the traditional way of data collection has a fundamental change [29][30][31][32]43]. As a result, there is a huge change in the network [43].…”

“…The center of the network computing migrates from the network center to the network edge [44], which is from cloud computing to edge computing [44], then to big data network [45]. The root of these technological developments lies in the collection of high-quality data [29][30][31][32]. Thus, how to obtain high-quality data becomes the focus of research.…”