Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct Wi-CaL , a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).

Since participatory sensing relies on users' active participation, several monetary incentive mechanisms for attracting users' participation have been proposed. However, to make users participate in “heavy” sensing tasks (either physically or mentally), stronger incentive is required. Thus, the total amount of rewards paid by the client will quickly rise. In this paper, we propose a novel incentive mechanism based on gamification for participatory sensing to reduce the total amount of rewards paid by the client. The proposed incentive mechanism incorporates a status level scheme depending on earned reward points like airline's mileage services, so that users with higher status can earn more reward points. We also introduce a ranking scheme among users and a badge scheme based on gamification so that users are attracted by getting not only monetary reward points but also sense of accomplishment. We formulate the problem of sensing given PoI (points of interest) with minimal reward points and devise a heuristic algorithm for deriving the set of users to which requests are sent and appropriate reward points for each request. The algorithm requires the participation probability distribution for each tuple of a user, reward points, and the burden of a sensing task. To obtain the probability distribution, we implemented a prototype of a participatory sensing system with the proposed incentive mechanism and conducted an experiment with 18 users for 30 days. As a result, we confirmed that the gamification mechanism increased participation probability from 53% (without gamification) to 73%.

Aiming to achieve sensing coverage for a given Area of Interest (AoI) in a People-Centric Sensing (PCS) manner, we propose a concept of (α, T)-coverage of the target field where each point in the field is sensed by at least one node with probability of at least α during the time period T. Our goal is to achieve (α, T)-coverage by a minimal set of mobile sensor nodes for a given AoI, coverage ratio α, and time period T. We model pedestrians as mobile sensor nodes moving according to a discrete Markov chain. Based on this model, we propose two algorithms: the inter-location and inter-meeting-time algorithms, to meet a coverage ratio α in time period T. These algorithms estimate the expected coverage of the specified AoI for a set of selected nodes. The inter-location algorithm selects a minimal number of mobile sensor nodes from nodes inside the AoI taking into account the distance between them. The inter-meeting-time selects nodes taking into account the expected meeting time between the nodes. We conducted a simulation study to evaluate the performance of the proposed algorithms for various parameter setting including a realistic scenario on a specific city map. The simulation results show that our algorithms achieve (α, T)-coverage with good accuracy for various values of α, T, and AoI size.