Incentivizing Opportunistic Data Collection for Time-Sensitive IoT Applications

Abstract

Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm

“…Some papers focused on collecting the sensed data from the workers and delivering it to the platform. In [27], a mechanism was introduced to transmit sensed data to the data requester according to the time constraint. In [28], the researchers aimed to collect sensed data from workers that have high coverage properties of MCS fulfilled.…”