# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 2 Related Work
A transmission rate adaption policy (TRAP) was proposed to adjust the transmission rate at each sensor when the link quality is poor to assist the QoS optimization problem. Rate adaptation can be tuned for a trade-off between energy consumption and the packet loss rate requirements. [PERSON] et al. [31] proposed a resource allocation scheme to optimize the allocation of the transmission power, the source rate, and the time slots to improve the QoS performances of a WBAN with a single wireless channel for all sensors. They formulated a throughput maximization problem whose optimal numerical solution was found efficiently by applying geometric programming. The source rates are allocated based on the optimal throughput results. [PERSON] et al. [32] proposed the GABAT-TRAP algorithm to achieve QoS metrics in a WBAN with a star topology, which applies an optimized hybrid technique of the genetic algorithm (GA) with the nature-inspired metaheuristic optimization algorithm, called the bat algorithm (BAT). The transmission rates are initially optimized in TRAP, proposed in [30]. Then, the transmission rates of the sensors are further optimized using GABAT to optimize the energy efficiency of the network. [PERSON] and [PERSON] [33] proposed the network status aware congestion control (NSACC) algorithm to predict the congestion level and regulate the packet sending rate. A fuzzy controller estimates the severity of the congestion. The rate regulation (RR) module appliesrules for each congestion level to change packet sending rates. It controls the congestion by adopting the logarithmic growth for the transmission rate to better utilize the bandwidth available, enhances the throughput, and reduces the number of retransmissions. [PERSON] and [PERSON] [34] proposed the following two approaches to improve QoS in WBANs: the reliable packet transmission with dynamic QoS (RPT-DQ) and the lion cooperative hunt optimization (LCHO) techniques for the optimization of multi-objective QoS. RPT-DQ consists of a series of steps from the priority assignment to the smart delay mitigation. The intelligent rate assignment step applies fast start and stabilized rate adjustment strategies. The fast start strategy initializes the packet transmission with a high data rate to utilize maximum bandwidth. The stabilized rate adjustment strategy increases or decreases the transmission rate of a sensor node by a stable amount when congestion occurs in the system to increase the packet transmission rate and throughput while reducing loss and delays. Table 1 compares the problem models applied in the above papers with our proposed model. Our model is most similar to the model in [25]. Like in our proposed model, the multi-hop network and utility optimization are used in a centralized manner, but the optimum data rate is searched for in a continuous domain. The models in [26; 27; 28] also use multi-hop network topology but throughput is maximized instead of utility. Furthermore, they follow a distributed approach in rate adaptation. The method in [29] maximizes the utility similarly to our model, but it applies the simple star topology. Like in our proposed model [30; 31; 32], search for the data rate in the discrete domain. However, all nodes are directly connected to the sink in a star topology in these models. The methods in [23; 33; 34] adapt the data rate in the continuous domain in a WBAN with a star topology, which differs from our approach. \begin{table}
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