Where to go? Predicting next location in IoT environment
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Where to go? Predicting next location in IoT environment Hao LIN, Guannan LIU
, Fengzhi LI, Yuan ZUO
School of Economics and Management, Beihang University, Beijing 100191, China c Higher Education Press 2020
Abstract Next location prediction has aroused great interests in the era of internet of things (IoT). With the ubiquitous deployment of sensor devices, e.g., GPS and Wi-Fi, IoT environment offers new opportunities for proactively analyzing human mobility patterns and predicting user’s future visit in low cost, no matter outdoor and indoor. In this paper, we consider the problem of next location prediction in IoT environment via a session-based manner. We suggest that user’s future intention in each session can be better inferred for more accurate prediction if patterns hidden inside both trajectory and signal strength sequences collected from IoT devices can be jointly modeled, which however existing state-of-the-art methods have rarely addressed. To this end, we propose a trajectory and sIgnal sequence (TSIS) model, where the trajectory transition regularities and signal temporal dynamics are jointly embedded in a neural network based model. Specifically, we employ gated recurrent unit (GRU) for capturing the temporal dynamics in the multivariate signal strength sequence. Moreover, we adapt gated graph neural networks (gated GNNs) on location transition graphs to explicitly model the transition patterns of trajectories. Finally, both the low-dimensional representations learned from trajectory and signal sequence are jointly optimized to construct a session embedding, which is further employed to predict the next location. Extensive experiments on two real-world Wi-Fi based mobility datasets demonstrate that TSIS is effective and robust for next location prediction compared with other competitive baselines. Keywords internet of things, next location prediction, neural networks, trajectory, signal
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Introduction
Enabled by internet of things (IoT), human movement, no matter outdoor and indoor, can be well tracked by GPS, Wi-Fi access points, and other IoT devices in a ubiquitous way. These online footprints in IoT environment thus offer new opportunities for more accurately predicting user’s next location without human intervention, which in turn helps to enhance system design [1] or improve location based services [2] in low cost. For example, with the indoor moving trajectories tracked by Wi-Fi facilities in a shopping mall, people’s mobility patterns can be inferred and the next location they tend to visit can also be predicted, which can help manage the store locations by rearReceived April 4, 2019; accepted September 16, 2019 E-mail: [email protected]
ranging the frequently co-visited stores close to each other, and meanwhile design proactive personalized advertisements to increase the exposure of stores and products. Also, with human mobility patterns analyzed, smart energy scheduling can be better achieved [3]. Besides indoor movements, outdoor trajectories, e.g., traces collected from wireless PDA
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