Where to go?Predicting next location in IoT environment

Next location prediction has aroused great inter-ests in the era of internet of things(IoT).With the ubiquitous deployment of sensor devices,e.g..GPS and Wi-Fi,loT en-vironment 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 con-sider the problem of next location prediction in loT environ-ment via a session-based manner.We suggest that user's future intention in each session can be better inferred for more ac-curate prediction if patterns hidden inside both trajectory and signal strength sequences ollected from IoT devices can be jointly modeled,which however existing state-of the-art meth-ods have rarely addressed.To this end,we propose a trajectory and sIgnal sequence(TSIS)model,where the trajectory transi-tion 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 dy-namics in the mutivariate signal strength sequence.Moreover,we adapt gated graph neural networks(gated GNNs)on loca-tion transition graphs to explicitly model the transition patterns of trajectories.Finally,both the low-dimensional representa-tions 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 pompared with other competitive baselines.

The structure of WbnH in a near active state

Dear Editor, Gram-negative bacteria possess a complicated membrane system that plays an essential role in interactions between bacteria and the envJronment （Reeves and Wang, 2002）. The inner leaflet of the membrane is composed of various glycerophospholipids, and the outer leaflet consists primarily of lipopolysaccharide （LPS） molecules.