The emergence of Bluetooth low energy (BLE) beacons has promoted the development of proximity-based service (PBS), which is a context-aware application delivered subject to the Proximity of Interest (PoI). Most commercial applications use the sequential proximity detection with a fixed scanning mechanism to identify the target PoI. Such sequential execution, though is able to produce reliable detection, suffers severe performance degradation especially when the number of deployed beacons in the vicinity increases. To understand the effects of dense deployment, we conduct an empirical investigation and derive the statistical properties of both received signal strength (RSS) and signal inter-arrival time. In light of the statistical insights, this paper proposes a high resolution proximity detection using an adaptive scanning mechanism fusion with a spontaneous Differential Evolution (AS+sDE). This novel approach enables the receiver to adapt its scanning duration conditioned on the deployment density and make an almost spontaneous detection in parallel with the scanning. The feasibility of the proposed approach is verified by both simulations and real-world implementations. For a density of $\leq 5\; beacons/m^{2}$, AS+sDE achieves a superior performance with a high accuracy rate, i.e., on average $< 1s$ is spent to guarantee at least 90% accuracy.

Our daily life services are quickly becoming smarter with intelligence and information through artificial intelligence (AI) and Big Data technologies. Parking services are one of the most frequently used in our daily life-cycle. This parking application could be classified into several features according to demands and properties, such as parking capacity balancing on a city-level view, parking fee maximization for achieving the service provider demand, empty parking spot notification within a parking lot, etc. This paper concentrates on parking space detection and alert to users. Most smart services rely on smart mobile derives of users such as smartphones and smartwatches. The proposed novel mechanism for smart parking is based on a smart device to gather mobile sensing data such as users’ activity and position data. Acquired mobile data are analyzed via machine learning technologies to provide dedicated parking services per user. Based on real testbed setups on campus and the proof-of-concept implementation, the proposed localization can achieve accuracy of a parking spot scale (2m-second guess 95%); moreover, it shows a much lower service operation period of 6.8 times (34s) than the legacy approach (230s).

Geographic routing has been considered as an efficient, simple, and scalable routing protocol for wireless sensor networks since it exploits pure location information instead of global topology information to route data packets. Geographic routing requires the sources nodes to be aware of the location of the sinks. How can source nodes get sinks locations with low overhead is a difficult issue in wireless sensor networks. In this paper, we propose a Sink Location Service for geographic routing in wireless Sensor Networks. In this scheme, a source node and a sink node send sink location announcement and query messages along two paths respectively by geographic routing. The node located on the crossing point of the two paths informs the source about the sink location. Then the source can send data packet to the sink by geographic routing. How to guarantee that these two paths have at least one crossing point in any irregular profile of sensor network is the challenge of this paper. Simulation results show that our protocol is significantly superior to other protocols in terms of energy consumption and control overhead.

In this paper, user log data of mobile notifications are collected from a real mobile news application, and notification opening rate and reaction time are identified as key parameters that characterize user behavior. The results indicate that notification opening patterns depend on the content of notifications, the time at which they are sent, the activeness of users, and the reaction time. This paper also demonstrates that there exists an inverse relationship between the notification opening rate of users and their notification reaction time, and between their notification opening rate and notification opening volume. Then, a novel method of quantifying notification usefulness for users is developed, where 3 models are presented to describe usefulness of notifications against the volume of notifications received, each relevant in different contexts. Utilizing this usefulness model, a notification optimization framework is developed for sending notifications from different news categories. The optimized notification allocation improved the average notification opening rate by by 65.24% from 4.92% to 8.13%, and the average reaction time improved by 13.13% from 20.4 hours to 17.68 hours.

Based on distributed sensing via energy-constrained sensor devices, the key of large-scale and shape-dynamic object monitoring is how to reduce communication costs which is the highest related factor to energy consumption. For achieving this goal, we propose a novel detection mechanism based on the convex hull algorithm that has a strong aspect for reducing the number of (geographic) sensing points to be transmitted to base stations for figuring out a large-scale object. As the convex hull only could recognize convex shapes, we develop methods to not only detect shape loss being a convex but recovery original boundary. That is, our mechanism could take both low communication cost and high detection reliability of large-scale objects.