Sparse sensing data–based participant selection for people finding

With the emerging of participatory sensing, crowdsensing-based lost people finding is arising. As a special location-centric task, participant selection is a key factor to determine success or failure of lost people finding result. Besides traditional influence, like Quality of Information contribution, candidate’s spatial proximity to the lost people is crucial in participant selection procedure. In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people’s probable position. To overcome these issues, we study the problem of selecting optimal participants according to each candidate’s spatial proximity and Quality of Information contribution. In the first procedure, we proposed a Received Signal Strength Indicator Positioning–Determined Compressive Sensing algorithm to interpolate missing Received Signal Strength Indicator data. Then, a location-important marking method is put forward to select a set of high-quality data for estimating missing people’s location. In the third procedure, a double-condition greedy participant selection approach, which guarantees candidates’ spatial proximity and Quality of Information contribution, is executed to select optimal participants. Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.