## 5 WBAN and the Communications Protocol Stack
### Application Layer  
The application layer for a WBAN includes the application running on the node that may be specific to the sensor type as well as management, security, synchronization, and query type functions. Any application specific QoS constraints, e.g., latency, are handled at this level together with any data compression and signal processing. Although extensive research has been undertaken into protocols operating within the lower layers for WSNs/WBANs, there is very little existing research for protocols operating at the application layer.  
At the application layer system administrators can interact with the nodes by using a Sensor Management Protocol (SMP) [63]. A SMP enables the lower layers to transparently interface with the application layer to undertake key management tasks such as management of rules relating to attribute naming and clustering, time synchronization and authentication. Sensor Query and Data Dissemination Protocol (SQDDP) [63] provides interfaces to issue attribute based queries. Sensor Query and Tasking Language (SQTL) [64] provides a mechanism to interface with events generated by a sensor node.  
Compression of data at the application layer is a vital process that consolidates the data contained within each packet therefore, requiring fewer transmissions resulting in energy savings. Distributed Source Coding (DSC) [65] is a widely referenced compression technique used in WSNs. Nodes located close to each other may sense values that do not differ significantly. Each node would therefore, encode its data using fewer bits than it ordinarily would, making it impossible to retrieve the measured value with information from only one node. However, when all measurements reach the sink node, the DSC technique can combine the sensors encoded data to retrieve the values measured by all the sensors with minimal coding error. This process effectively moves the computational complexity from node to sink, thus fewer bits are transmitted and energy is saved. Other techniques such as Data Fusion and Data Aggregation [66] are further examples of methods used to reduce transmission data and save energy in low power networks.  
Compressed Sensing (CS) is a relatively new signal processing technique that holds great promise for leveraging greater efficiency in the compression and recovery of signal data [67]. CS theory asserts that sufficient signal data can be recovered from far fewer samples or measurements than traditional methods where the sensed data adheres to a number of key criteria. CS relies on two principles: _sparsity_--which relates to the signal of interest and _incoherence_--which relates to the sensing modality [68]. Efficient sampling and sensing protocols can be designed based on CS techniques to capture useful data content embedded in a sparse signal and then condense the raw data into a reduced data set. What is most remarkable about CS is that it will allow a sensor node to very efficiently capture data in a sparse signal without needing to comprehend that signal, then by using numerical optimization techniques, CS can reconstruct the full length signal from the small amount of collected data. CS is a very efficient and effective signal acquisition method that in the context of a WBAN, could sample the data from a sensor node at a low rate and later reconstruction from what appears to be an incomplete set of measurements at the sink. The complexity of CS in comparison to standard Nyquist rate based signal processing techniques is moved to the sink side. This process alleviates complexity and therefore, battery draining processing from the sensing node. CS is an active research area and could hold great potential for WBANs, but careful analysis of the recovered signal must form a vital part of any research to ensure no QoS thresholds are breached and the accuracy of patient life signs data is not compromised.