## 4 Network Communication
### Channel Characterization
#### 4.1.6 Cross Layer  
In WSNs, cross layer design is an effective way to improve system performance by compressing or adopting the redundant information across layers and modules. Cross-layer optimization combines cognitive science, artificial intelligence, and convex optimization to allocate resources, so as to efficiently improve the utilization of these resources in wireless networks. Meanwhile, it can also offer better QoS for nodes in wireless networks [127]. Reference [128] proposes a cross-layer Dynamic Source Routing (DSR) algorithm which extends DSR by exploiting cross-layer optimization techniques to minimize the frequency of recomputed routes. DSR initiates a route discovery only when a link failure occurs, thus leading to a 50% reduction of recomputed routes compared with the traditional DSR protocol, and finally improves the routing energy efficiency of WSNs. Such a study also exists in BSN research. Reference [129] studies the real-time data streaming applications in BSNs. Firstly, researchers formulate routing and residual energy of nodes during the establishment of the  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol** & **Content** & **Resolved Issues** \\ \hline \multirow{2}{*}{FPSS} & Choosing path intelligently among nodes based on heuristic self-adaptive algorithm in energy constrained on-body network. & Energy balance \\ \hline \multirow{2}{*}{PRPLC} & Forwarding packets to proper neighbors by prediction of postural trend based on link likelihood fact. & Topological partition \\ \hline TARA & Establishing route to detour around hotspots area using a withdrawal strategy. & Minimizing the thermal effects of Implant biosensor \\ \hline \multirow{2}{*}{ALTR} & Always choosing neighboring node with the lowest temperature as next stop. & Implant biosensor \\ \hline \multirow{2}{*}{ALTR} & Choosing next stop by both the lowest temperature node and the shortest hop count. & Implant biosensor \\ \hline \multirow{2}{*}{LTRT} & Choosing the shortest path based on a Dijkstra algorithm with the weight of temperature. & Implant biosensor \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of existing BSN routing protocols.  
routing paths. And then a cross-layer optimization is applied for improving the real-time performance of sensorial feedback for users, maximizing the lifetime of system at the same time. Furthermore, they also study the ARBA protocol, which monitors the complex conditions in data streaming from various sensors in a real-time mode, adaptively allocating resources to cope with changes in context and application requirements. Reference [130] presents a cross-layer communication protocol, called Cascading Information retrieval by Controlling Access with Distributed slot Assignment (CICADA), designed for wireless multi-hop mobile BSNs. It uses the same packets to take care of both medium access as well as routing, which makes low delays and energy efficiency possible while preserving network flexibility. BSN-MAC [101], another cross layer protocol, can make dynamic adjustments based on the feedback provided by sensors to achieve a better energy efficiency performance by the interaction of network coordinators and sensors. Reference [33] presents MOFBAN, a lightweight Modular Framework for Body Area Networks, which is a special cross layer design. The framework supports many features, such as MAC control, routing and robustness, realized by modules, unlike a normal layered architecture, so it is adaptable and expandable.