The design of a Bayesian Network for mobility management in Wireless Sensor Networks

Mobility management functions in WSNs are mainly being developed from a ommunicational point of view, since the focus has been on maintaining the network connectivity. However, from a sensing point of view, sensor mobility has also an impact on the network spatial coverage. In mobile WSNs, the extension of the spatial coverage is often changing, and as a result, the region of interest might be inaccurately sensed by the mobile sensors. Therefore, the representation of a movement context is important to avoid making interpretations and decisions outside of the situation in which the WSN is capturing information; and make possible to decide where, when and how the sensing is performed with the most suitable spatial coverage of a region of interest. This paper proposes a Bayesian Network (BN) approach for (a) making explicit the structural and parametric components of a movement context using WSN metadata, and (b) probabilistically inferring the mobility management requirements when a WSN spatial coverage is insufficiently covering a region of interest. A controlled experiment was carried out and the results show that the BN has successfully inferred different mobility management requirements according to a given movement context. Two movement contexts have been used to illustrate this approach. They are related to whether the environmental sensing is being carried out in an emergency situation or not