## 2 Related Works  
Major challenges and open research issues on QoS provisioning in BSNs have been presented in [6] and a cross-layer QoS framework (that spans over three layers) for biomedical sensor networks has been proposed in [7]. In EDDD [8], an energy-efficient differentiated directed diffusion mechanism has been developed that provides with service differentiation between real time and best effort traffic. However, their designs are neither scalable nor adaptive to dynamic environment. In recent years, QoS routing in location-aware wireless sensor networks has received much research interests due to its inherent characteristics of (i) being scalable to large networks, (ii) making routing decisions based on local neighborhood information, and (iii) being very adaptive under dynamic changes and mobility as only a node's neighborhood is affected. In [9], a reinforcement learning-based routing model for BSNs is proposed that selects a QoS route via computing neighborhood node's Q-values and position information, but does not consider energy at all. Directional Geographic Routing (DGR) [10] constructs an application-specific number of multiple disjointed paths for routing real time video communications data in wireless sensor networks. MCMP [11] uses link delay and reliability as routing decision parameters, where data packets are duplicated at source nodes by solving optimization problem. But, this approach considers neither residual energy nor progress speed. Hence, packets may get routed to a node which is highly congested and/or energy critical. MMSPEED [12] also sends duplicate packets (probabilistically) toward multiple paths and multiple reliability- and delay-bound packets are considered for QoS provisioning. However, routing in MMSPEED fully avoids energy consideration, reducing its applicability for BSNs. A hybrid geographic routing (HGR) protocol has been designed in [13] to achieve an efficient tradeoff between energy efficiency and delay performance. A reliable and energy-efficient routing protocol (REER) has been developed in [14] exploiting geographic information and cooperative communications.  
The proposals in DARA [15] and LOCALMOR[16] have some similarities with our DMQoS protocol. In DARA [15], a weighted aggregate routing metric consisting of geographic progress, delay and energy is considered for supporting critical and non-critical data packets through defining long- and short-range forwarding zones, respectively. However, the use of same routing function for both the packet types deteriorates the QoS performance. In LOCALMOR [16], the routing functions have been separated for multiple packet types; however, it uses fixed number of sinks (primary and secondary sinks) and all packets are blindly duplicated toward both the sinks, making it unscalable. Also, it increases the overhead of sending too many duplicate packets.  
The distinguished features of our DMQoS protocol design from those of the above mentioned works are as follows. Its routing functions are distinct for different packet types based on their QoS requirements. Its architecture is modular and the routing modules cooperate to render enhanced QoS services to different traffic classes. It uses Lexicographic Optimization (LO) for trading-off energy and service.