# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 3 BBN Design Considerations
### Mobility Management for BBN
\begin{table}
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**Category** & **Advantages** & **Disadvantages** & **BBN Application** \\ \hline
**Movement History-Based Mobility Prediction** & & & \\ \hline Exploent the regularity in human movement behavior within a defined period of time. & Unpredictable changes in user’s behavior within a defined period of time. & & \\ \hline
**Physical Topology-Based Mobility Prediction** & & & \\ \hline Link expiration time estimation & Estimate the expiration time of the wireless link. Routes are reconfigured before disconnecting. & Support simple mobility pattern, no sudden changes in velocity and moving directions & U-health in indoor environments (hospital, rest home) \\ \hline Link availability estimation & Immediate rerouting in link failure case. Select more reliable neighbors to form more stable clusters. & Difficulties in learning the changes in link status due to nodes movements. In highly volatile environments, increase of the control overhead. & U-health in accidents and emergencies. (Patient transportation, sharing emergency information, and vital signs monitoring.) \\ \hline Group mobility and network partition & Prevent disruptions caused by the network partitioning. Low-complexity clustering algorithm accurately predicting the mobility groups and their mobility switching. & Assume that the velocity of the mobile group of nodes is invariant over time. Non realistic assumption. & U-Health for a rescue team in a disaster area \\ \hline Cluster change prediction & The mobile node position and the direction of its movement in the cluster are used to predict future cluster switching. & This prediction process needs an accurate location. The use of GPS is needed to locate the mobile nodes. & U-health monitoring of a group of soldiers. The position and mobility of each soldier are function of those of his neighbors. \\ \hline
**Logical Topology-Based Mobility Prediction** & & & \\ \hline Neighboring Nodes Relative Mobility & Based on past measurements. A linear model is implemented by mobile nodes to estimate the distance from their next cluster head (CH). & Do not consider node mobility during CH election. & U-health in indoor environments (hospitals), or u-health monitoring of a sport team or a rescue team in outdoor/indoor environment. \\ \hline Information theory based mobility prediction & Assume virtual clusters, no fixed geographical area. Online learning to predict the next cluster. & Recurrent CH switching due to node mobility. & U-health in indoor environments, or U-health monitoring of a sport team or a rescue team in a limited geographical area. \\ \hline \hline \end{tabular}
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**Information theory based mobility prediction** & Assume virtual clusters, no fixed geographical area. Online learning to predict the next cluster. & Noe’s mobility is deduced from the frequency of its neighborhood changeability over time. & U-health in indoor environments, or U-health monitoring of a sport team or a rescue team in a limited geographical area. \\ \hline Evidence based mobility prediction & No GPS use is required. Accurate prediction of the user trajectory. The distance between mobile nodes is estimated by the signal strength. & Only applied by the border nodes to predict the next cluster. & Feasible in outdoor environments, for U-health monitoring of freely moving patients or any group of persons in an outdoor/indoor environment. \\ \hline \end{tabular}
\end{table} 
Table 2: Comparative study of mobility prediction methods for BBNs. © [2019] IEEE. Reprinted, with permission, from [4]. The study in [70] analyzes two prerequisites of mobility support in WBANs. First, the location independence, which consists of decoupling the different TDMA transmission phases.