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Energy efficiency is a key concern for wireless sensor nodes, especially for wireless body area network (WBAN) in which sensors operate in close vicinity to, on or even inside a human body. In this paper, we first present a system-level energy consumption model associated with transmission distance d and transmission data rate over on-body wireless communication link. Then, based on the analysis of tradeoff between circuit energy and transmission energy on distance, a threshold distance d_{\rm th} which is responsible for the proportion of transmission energy and circuit energy is derived for energy saving in WBAN. With the case of d\leq d_{\rm th} , since circuit energy is comparable with transmission energy consumption, the total energy consumption can be saved by optimizing the transmission data rate R . Simulation results show that a 59.77% or even more energy saving is achievable using the optimized scheme, compared with baseline scheme. With d>d_{\rm th} , since the total energy consumption is monotonically decreasing with respect to time t$ , an offline algorithm is applied to energy saving by prolonging transmission time within the deadline time. In addition, on the basis of the offline algorithm, a battery-aware transmission approach is presented for WBAN using battery electrochemical property. Experimental results show that, using the presented battery-aware approach, 71.05% and 60.81% energy saving can be obtained, in comparison with the baseline and offline schemes, respectively. [ABSTRACT FROM PUBLISHER]

The core problem with nodes of wireless sensor networks is the limited charge capacity for the power-operated battery. The main concern of this paper is how to extend the battery's lifetime. According to the characteristics and functionality of sensor nodes, the sensor applications are categorized into three distinct types, i.e., the hard real-time applications, the soft real-time applications, and the periodic applications. For the hard real-time applications, a battery-friendly lazy packet scheme is designed to draw smoother and lower current for minimizing battery charge consumption. A battery-friendly local optimization scheme with slack time is exploited and evaluated for the soft real-time applications. For the periodic applications, a battery-aware task–scheduling algorithm is developed, which reschedules tasks of sensor nodes to achieve the battery friendly discharge profile. The simulation results show that the three battery-friendly algorithms perform better in extending lifetime of battery-operated sensor nodes and minimizing battery charge consumption. [ABSTRACT FROM PUBLISHER]