## 2 Related Work  
A vast literature could be found on energy efficiency and lifetime improvement of WSN. Although WSNs significantly differ from WBANs in many aspects including size, scope, application, coverage etc., understanding of research efforts in traditional WSNs is important to gain deeper insight and full context. Hence, the first part of this section discusses energy efficiency in traditional WSNs for completeness. Grouping nodes to form clusters is one of the well-known mechanisms for energy-efficient communications in WSN. A clustering-based protocol LEACH (Low-Energy Adaptive Clustering Hierarchy) was proposed by [PERSON] et al. [19] that exploited randomized rotation of cluster heads to evenly distribute the energy load among the sensors in the network. In-LEACH scalability and robustness for dynamic networks are addressed by localized coordination, and data fusion is found to be incorporated into the routing protocol to reduce the amount of information to be transmitted to the base station. Another distributed clustering approach was proposed by [PERSON] et al. [20] for long-lived sensor networks. This approach does not make any assumptions for factors like the presence of infrastructure, or the availability of multiple power levels in sensor nodes. Instead, a Hybrid Energy-Efficient Distributed clustering (HEED) was presented by the authors. In this protocol cluster heads are selected periodically based on the residual energy of a node and node proximity to its neighbors or node degree. The advantages and objectives of clustering for WSN are analyzed by [PERSON] [21] along with a comparative study of these protocols. There are works reported in literature that focus on designing transmission strategies for WSN to achieve energy efficiency. In [11] a generic mathematical framework was proposed to characterize the policies for single-hop transmission over a replenishable sensor network. Here different modes of energy renewal were presented with Markov Chain Process and accordingly, optimal transmission policy was derived for sensors with different energy budgets. The energy status and the reward for successfully transmitting a message were given as input to the formulations to maximize the average reward rate and justify the existence of optimal threshold as well. In [22] optimal selective forwarding policies was proposed to save energy and extend the lifetime of WSN based on the available energy resources of the nodes, the energy cost of retransmitting a message or the importance of the message. Forwarding schemes included three different scenarios: first, when sensors maximized the importance of their own transmitted message; second, when the sensors maximized the importance of messages that were successfully retransmitted by at least one of its neighbors; and finally, when sensors maximized the importance of messages that successfully arrived at the sink. Performance was measured in terms of gain of selective forwarding policies under exponential importance distribution when energy costs were stochastic. Thus, the current energy level of nodes is found to be the key element in modeling transmission strategy in the works reported in [11; 12; 13]. However, data importance is obtained as another input parameter for optimal transmission strategy formulation in the works reported in [13; 22; 23]. In [13] policies were developed whether to transmit the data packet or not, based on the current energy level and data importance, to maximize the long-term average transmitted data importance. Whereas, the work in [24] aimed at maximizing the long-term average transmission rate considering energy-harvesting device with packet data queue. However, the architecture, deployment area, and operating conditions of WSNs differ from WBANs. WBANs are deployed on a human body thus having limited coverage area but sensitive to transmission energy (as radiation may cause tissue damage).