# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 1 Introduction
the data can still be useful in reduced quality for monitoring and evaluation purposes. Data rate adaptation can be considered a complex multicriteria optimization problem where the aim is to maximize the overall utility of the delivered data streams. The utility  
Figure 1: An example of a WBAN. is defined as a metric expressing the overall satisfaction of the user consuming the data, who is typically a member of the medical staff in the case of a WBAN. The utility function defines the relationship between the usefulness of the delivered data and their parameters (e.g., data rate). The main reason for optimizing utility instead of throughput is that the same amount of data may have different contributions to the correct evaluation of the streamed information. The utility is typically a monotone concave function of the data rate, i.e., increasing the data rate results in a smaller or equal utility increase if the data rate is larger. For example, if the sampling rate of an ECG sensor is doubled, the utility of the stream increases by less than double. Various utility models have been developed, especially in multimedia [13; 14; 15]. The utility functions are used for rate control and resource allocation in communication networks [16]. Network utility maximization (NUM) deals with allocating traffic rates to maximize the users' total utility subject to network resource constraints [17; 18; 19]. Utility models can be used to combine multiple optimization targets into one metric by applying weights. They are widely used in resource allocation in WSNs to ensure the effectiveness and fairness of the system [20]. The utility function can model the trade-off between QoS and energy efficiency [21]. Game theoretic approaches use the concept of utility to quantify the degree of a player's preference across a set of available alternatives [22]. The rate adaptation can be performed in a distributed or centralized manner. In the distributed case, each node locally determines a proper transmission rate to avoid network congestion. In the centralized case, the rate adjustment algorithm running on the sink node tries to optimize the data rate assignment using global network information and the sink node sends the calculated data rates to all sensor nodes. The main advantages of the centralized approach include that it can find near-optimal rate assignments and the complexity of software on sensor nodes except the sink node can be reduced. The principal contributions of our work are defined as follows:  
* A utility-based formal optimization model for discrete data rate adaptation in WBANs with a tree topology. * Optimization algorithms to solve the discrete data rate adaptation based on the multiple-choice multidimensional knapsack problem. * Evaluation of the proposed algorithms, where the greedy method closely approximated the optimum in a short running time and proved to be suitable for real-time usage. * Further results of the evaluation show that solving the proposed model has much higher accuracy than adapting the solutions of more common models. The remainder of this paper is organized as follows. In Section 2, related work is discussed. Section 3 introduces the proposed content adaptation module. Section 4 provides a formal model for delivering data streams in WBANs. In Section 4, a detailed description of the proposed algorithms is provided. In Section 5, the presented algorithms are evaluated and compared to each other, while Section 6 concludes the paper.