Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass, bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5m, 1m, and 8m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5m for localization and 0.9m for tracking in the office environment. When using only the magnetism, Magicol consumes 9× less energy in tracking compared to WiFibased tracking.

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bicycles caused by the ever-changing usage and (available) supply. This bicycle imbalance issue necessitates efficient bike re-balancing strategies, which depends highly on bicycle mobility modeling and prediction. In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour granularity. We extensively evaluated the performance of our design through a oneyear dataset from the world's largest public bike-sharing system (BSS) with more than 2800 stations and over 103 million check in/out records. Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction. We believe this new mobility modeling and prediction approach can advance the bike re-balancing algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.

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Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FOLLOWME, a "last-mile" navigation system to enable plugand-play navigation in indoor and semi-outdoor environments. FOLLOWME exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FOLLOWME is infrastructure-free, energyefficient and cost-saving. We implemented FOLLOWME on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m 2 . Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

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Abstract-Limited energy in each node is the major design constraint in wireless sensor networks (WSNs). To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. In a typical WRSN, batteries in sensor nodes can be replenished by a mobile charger that periodically travels along a certain trajectory in the sensing area. To maximize the charged energy in sensor nodes, one fundamental question is how to control the traveling velocity of the charger. In this paper, we first identify the optimal velocity control as a key design objective of mobile wireless charging in WRSNs. We then formulate the optimal charger velocity control problem on arbitrarily-shaped irregular trajectories in a 2D space. The problem is proved to be NP-hard, and hence a heuristic solution with a provable upper bound is developed using novel spatial and temporal discretization. We also derive the optimal velocity control for moving the charger along a linear (1D) trajectory commonly seen in many WSN applications. Extensive simulations show that the network lifetime can be extended by 2.5× with the proposed velocity control mechanisms.

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