## 4 The WBANs Communication Stack
### Medium Access Control Layer
#### 4.2.3 Existing State-of-the-Art on the MAC Protocols
Further, the protocol was implemented on HBC platform and the results are compared with IEEE802.15.4 (CSMA/CA), TDMA and polling protocols. Statistical MAC appeared to be the more energy-efficient, having the lowest data transfer latencies, and seems suitable to apply in heterogeneous traffic network. In [60] the authors studied the behavior of slotted and unslotted CSMA/CA protocols and concluded that the unslotted mechanism performs better than the slotted one in terms of throughput and latency, but with a high power consumption cost. _Hybrid and Cross-Layer Protocols:_ Several hybrid mechanisms for medium access also exist in the literature. Works such as [61] have proposed a mixture of MAC techniques based on contention-based access and polling-based access and is analyzed under temporal WBAN channel models. By using hybrid techniques at different proportions, authors have evaluated how performance and energy consumption varies. The results show design trade-offs in the packet delivery versus latency versus consumed energy space. It is concluded that for the MAC design of WBANs polling-based channel access offers significant energy gains compared to contention-based access. Regarding the latency (end-to-end delay), the combination of short contention periods with long polling periods provides the most stable performance with respect to packet transmissions. The Human Energy Harvesting (MAC) protocol (HEH-BMAC) was proposed in [62]. In this protocol hybrid polling mechanism is used which is powered by human energy harvesting for WBANs. The protocol combines two different medium access methods, namely polling (ID-polling) and probabilistic contention (PC) access, to adapt its operation to the different energy and state (active/inactive) changes that the network nodes may experience due to their random nature and the time variation of the energy harvesting sources. HEH-BMAC exploits the packet inter-arrival time and the energy harvesting rate information of each node to implement an efficient access scheme with different priority levels. In addition, the proposed protocol can be applied dynamically in realistic networks, since it is adaptive to the topology changes, allowing the insertion/removal of wireless sensor nodes. Extensive simulations have been conducted in order to evaluate the performance of the protocol and study the performance and energy tradeoffs. A wireless EEG monitoring system is considered in the Energy-Delay-Distortion cross-layer design which is presented in [63]. In the proposed approach, transmission energy, encoding energy, application quality of service (QoS) constraints, and scheduling are jointly integrated into a cross-layer design framework. This framework is used to dynamically perform radio resource allocation for multiple users, and to effectively choose the optimal system parameters to adapt to the varying channel conditions. Furthermore, the relationship between bandwidth and energy consumption is exploited to optimally allocate time-frequency slots to the sensor nodes while minimizing the total energy consumption in the network. Further, authors have extended the traditional Rate-Distortion analysis by considering another dimension, the energy consumption, and defined the E-R-D analysis framework for EEG signal encoding and transmission under energy constraints. Using the E-R-D model, under the delay and distortion constraints, the proposed cross-layer optimization framework is able to find the best configuration of system parameters to minimize the total energy consumption. This framework jointly minimizes the total energy consumption and determines the optimal transmitted rate at physical layer, assigned time slots length, and bandwidth at MAC layer, wavelet filter length and compression ratio at application layer. _Traffic-Aware Adaptive MAC for WBAN:_ Flexible duty cycling techniques through Traffic-adaptive MAC protocol (TaMAC) is introduced in [64]. It takes into account the traffic information of the sensor nodes.