Evaluating location predictors with extensive Wi-Fi mobility data

Song, Libo; Kotz, David; Jain, Ravi; He, Xin

doi:10.1109/infcom.2004.1357026

Cited by 191 publications

(41 citation statements)

References 12 publications

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“…Fortunately, large residence times before transiting into an OFF state is a dominant feature in the collected data rendering 30-minute inaccuracy insignificant. More details on the data collection method and the network can be found in [6,13].…”

Section: System Model Assumptions and Methodologymentioning

confidence: 99%

Model T++

Lelescu¹,

Kozat²,

Jain³

et al. 2006

Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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We present an empirical registration model derived from the WLAN registration patterns of the mobile users. There exist models that accurately describe individually the spatial and temporal aspects of user registration, and demonstrate the importance of this modeling. The main distinction of the new model from the previous empirical models is that we are able to formulate the inter-dependence of space and time explicitly by a set of few equations. Our extensive studies of the WLAN traces indicate that a simple but proper notion of popularity gradient suffices to capture the correlation across space and time. Indeed, when locations (i.e., AP coverage area) are differentiated with respect to the number of visits they are receiving (i.e., AP popularity), the time spent at each location i before user moves from i to k turns out to be closely related to the difference of popularity between locations i and k. This observation led to the design of a joint time-space registration model (referred to as Model T++) that builds upon the Model T, which itself models only the space aspect of the registration, but is derived from the same campus WiFi network. As part of the process of generating a joint space-time model, we further extend spatial aspects of the Model T. We evaluate our model using various metrics against a random walk model as well as the Model T by superimposing location independent time series on these space-only registration models. Our results suggest that with a slight increase in the model complexity, our joint time-space registration model is able to better capture the real network registration than the independent time models. Model T++ can be easily integrated into both WLAN and multi-hop wireless mesh network simulations that require realistic registration models.

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Section: System Model Assumptions and Methodologymentioning

confidence: 99%

Model T++

Lelescu¹,

Kozat²,

Jain³

et al. 2006

Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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show abstract

“…Previous work [9] has shown that the trace length for different users shows very wide variability, with a median of 494 and a maximum of 188,479 APs visited. However observe that, on average, users visit only a few distinct APs per day that they are active; see Fig.…”

Section: Locality and Hierarchymentioning

confidence: 99%

“…A limitation of the data is that the OFF state is not always accurately captured. Further details of the data and collection process are described in [8,9].…”

Section: Empirical Datamentioning

confidence: 99%

Model T: A model for user registration patterns based on campus WLAN data

Jain¹,

Lelescu²,

Balakrishnan³

2006

Wireless Netw

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We discuss the derivation of an empirical model for spatial registration patterns of mobile users in a campus wireless local area network (WLAN). Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set.The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns. In this context, we also introduce and discuss the modeling of the disconnected state as an integral part of real traffic characteristics.We generate synthetic traffic traces based on the model we derive. We then compare these traces against the real traces from the test set using a set of metrics we define. We find that the synthetic traces agree very well with the test set in terms of the metrics. We compare the derived model to a simple modified random waypoint model, and show that the latter is not at all representative of the real data. We also show how the model parameters can be varied to allow designers to consider 'what-if' scenarios easily. Finally we develop an extended version of Model T that uses an alternative modeling of relative popularity of APs and clusters, with certain generalization advantages, and evaluate its fidelity to the real data also, with positive results.

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“…It is trained by constructing a transition matrix based on history trajectories, which indicates the transition probability between locations. The next location will be inferred depending on the current status and the transition matrix [13]. Pattern-based techniques aim to mine the mobility patterns existing in history trajectories by utilizing association rule methods.…”

Section: Introductionmentioning

confidence: 99%

Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network

Chen

et al. 2020

IJGI

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Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. In recent years, ubiquitous location acquisition technologies have provided the opportunity for mining knowledge from trajectories, making location prediction and real-time decisions more feasible. Previous location prediction methods have mostly developed on the basis of shallow models whereas shallow models are not competent for some tricky challenges such as multi-time-step location coordinates prediction. Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. The method of this work consists of three successive parts: trajectory preprocessing, prediction model construction, and post-processing. In this work, a prediction model named the bidirectional recurrent mixture density network (BiRMDN) was constructed by integrating the long short-term memory (LSTM) and mixture density network (MDN) together. This model has the ability to learn long-term contextual information from recent trajectory and model real-valued location coordinates. We employed a vessel trajectory dataset for the implementation of this approach and determined the optimal model configuration after several parameter analysis experiments. Experimental results involving a performance comparison with other widely used methods demonstrate the superiority of the BiRMDN model.

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Evaluating location predictors with extensive Wi-Fi mobility data

Cited by 191 publications

References 12 publications

Model T++

Model T++

Model T: A model for user registration patterns based on campus WLAN data

Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network

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