Neighbor discovery on duty-cycled mobile devices in group settings arises in many applications. In such scenarios, it is sufficient for an arbitrary node in a group to discover a new node. While pairwise neighbor discovery schemes can be directly applied to group settings, their performance can be severely limited as they are not designed to coordinate the efforts of group members. Explicit coordination among the group members, however, can incur large overhead in mobile networks, where the group membership changes dynamically over time. In this paper, we focus on schemes that require no explicit communication among the group members, and nodes follow deterministic schedules that can be succinctly represented. We first define the notion ofideal duty cyclefor a group, and then develop two deterministic neighbor discovery schemes for group settings, and show that both of them achieve effective duty cycle close to the ideal duty cycle. In addition, we show that the schemes are lightweight and easy to implement using experiments in a testbed. Last, we use a case study to demonstrate the usage of our proposed schemes and show that a simple enhancement leveraging the deterministic nature of the schemes leads to significant performance improvement, at the cost of only slight extra overhead.

Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While location information can be conveniently gathered by GPS, typical datasets suffer from significant periods of missing data due to various factors (e.g., phone power dynamics, limitations of GPS). A common approach is to remove the time periods with significant missing data before data analysis. In this paper, we develop an approach that fuses location data collected from two sources: GPS and WiFi association records, on smartphones, and evaluate its performance using a dataset collected from 79 college students. Our evaluation demonstrates that our data fusion approach leads to significantly more complete data. In addition, the features extracted from the more complete data present stronger correlation with self-report depression scores, and lead to depression prediction with much higher$F_1$scores (up to 0.76 compared to 0.5 before data fusion). We further investigate the scenario when including an additional data source, i.e., the data collected from a WiFi network infrastructure. Our results show that, while this additional data source leads to even more complete data, the resultant$F_1$scores are similar to those when only using the location data (i.e., GPS and WiFi association records) from the phones.

Neighbor discovery is a fundamental problem in wireless networks. In this paper, we study asynchronous neighbor discovery on duty-cycled mobile devices. Most existing studies develop integer schedules where time proceeds in discrete slots and a node is awake or asleep for an entire slot duration. We show that integer schedules can lead to significant waste of resources, and develop a generalized non-integer model, where time is continuous and a node may become awake or asleep at any point of time (subject to a few constraints) so that the resultant schedules can be significantly more efficient than integer schedules. In addition, we provide a reduction that transforms any schedule in the integer model to a corresponding schedule in the generalized non-integer model while reducing the discovery latency by up to a factor of two. Applying this reduction, an optimal schedule in the integer model becomes an optimal schedule in the non-integer model. We further demonstrate the practicality of non-integer schedules in a testbed, and compare the worst-case discovery latency of several existing schemes under both integer and non-integer models. Last, we establish a family of lower bounds for the best achievable latency guarantee. These lower bounds are applicable to both integer and non-integer models, covering both symmetric and asymmetric settings, and encompassing the existing lower bounds that are only for a subset of settings as special cases.