We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates.

With the growing adoption of electric vehicles (EVs) and advent of bidirectional chargers, EV aggregators, such as charging stations, will become a major player in electricity markets, providing voltage regulation (VR) or other services. We present a novel and practical VR scheme that takes advantage of the charging flexibility of EVs in charging stations that are connected to buses in a distribution grid. This VR scheme relies on real-time measurements, as well as estimates of the distribution system state and regulation capacity of each charging station. We then propose a novel false data injection attack (FDIA) against the VR capacity estimation process that exploits the uncertainty in EV mobility and network conditions. We show the attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem, subject to a constraint that ensures it bypasses bad data detection. We determine this attack vector by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a co-simulation platform, based on two standard test feeders, reveal the vulnerability of the VR capacity estimation process.

Effective positioning and tracking of critical construction resources is crucial to fulfilling project objectives in terms of time, cost, and safety. Received signal strength (RSS) based localization methods increasingly become a popular choice in construction applications, especially for indoor scenarios, potentially automating field data acquisition and construction process control. However, the fluctuating nature of collected RSS data due to multipath propagation effects, along with the unavailability of radio signals at certain site locations, hampers effective application of such localization techniques in challenging and dynamic indoor applications. This research evaluates the feasibilities of two RSS-based location tracking techniques, namely, the ranging-based method and the profiling-based method. To verify the positioning accuracy of the two methods, a wireless sensor network system was prototyped for conducting positioning experiments in an indoor carpark, which mimics an enclosed construction site. Experimental results reveal that the ranging-based method performed poorly in the test setting, while on the other hand, the positioning error achieved with the profiling-based method was less than 2.14 m with 95% likelihood. Further, to evaluate the achievable accuracy of this method in a real-world indoor environment, we coupled the profiling-based method with commonly used noise filtering algorithms and conducted field testing in a pipe fabrication shop. With 95% likelihood, consistent positioning accuracy of 1–2 m away from the actual position of a tracked tag was obtained in the fabrication shop, which is deemed sufficient for materials and labor hours tracking in support of productivity analysis. In conclusion, the RSS profiling-based method is found to be an effective method for deployment in real-world indoor settings of construction-related applications.

Users today can easily and intuitively record their real-world experiences through mobile devices, and commodity virtual worlds enable users from around the world to socialize in the context of realistic environments where they simulate real-world activities. This synergy of technological advances makes the design and implementation of trans-reality games, blending the boundaries of the real and virtual worlds, a compelling software-engineering problem. In this paper, we describe fAARS, a platform for developing and deploying trans-reality games that cut across the real and parallel virtual worlds, offering users a range of game-play modalities. We place fAARS in the context of recent related work, and we demonstrate its capabilities by discussing two different games developed on it, one with three different variants.