zCap: a zero configuration adaptive paging and mobility management mechanism

Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards

Autonomous Accident Monitoring Using Cellular Network Data

Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions

Zero configuration adaptive paging (zCap)

Today, cellular networks rely on fixed collections of cells (tracking areas) for handset localisation. This management parameter is manually configured and maintained and is not regularly adapted to changes in use patterns. We present a decentralised approach to localisation, based on a self-adaptive probabilistic mobility model. Estimates of model parameters are built from observations of mobility patterns collected on- line using a distributed algorithm. Based on these estimates, dynamic local neighbourhoods of cells are formed and maintained by the mobility management entities of the network. These neighbourhoods replace the static tracking areas used in current implementations by using the tracking area list facility of LTE. The model is also used to derive a multi phase paging scheme, where the division of cells into consecutive phases is optimal with respect to a set balance between response times and paging cost. The approach requires no manual tracking area configuration, and performs localisation efficiently in terms of number of location updates, page messages per localisation request and response times