## 1 Introduction  
In wireless sensor networks (WSNs), the energy constraint is a very crucial issue, as sensor nodes are usually operated on limited and irreplaceable battery power. Thus, most research works have focused on how to prolong network lifetime by reducing the energy consumption of sensor nodes [1; 2; 3]. For the point of network management, a predictable, as well as certain guaranteed network lifetime is preferred over an unpredictable long lifetime. For example, if the network lifetime is guaranteed by the pre-determined time, many deployment problems can be easily solved [4]. However, research works for guaranteeing network lifetime have rarely been studied despite their impact on network management. Furthermore, since these existing schemes did not concern specific applications, they therefore suffer from adaptability problems in real deployment. Most WSN applications are required to meet specific requirements. In emergency WSN applications, such as radiation monitoring, security surveillance and fire detection, real-time data delivery is essential. In such applications, it is more desirable to continually accomplish a given task of the sensor nodes until the given network lifetime is expired. If an unpredictable lifetime is assumed, it is very hard to bound a delay deadline while considering energy constraints in WSNs. For this reason, a guaranteed lifetime for real-time applications in WSNs is a more appropriate deployment model than prolonging network lifetime. In our previous work [5], we balanced the active and sleep times of each node to meet real-time requirements in a guaranteed network lifetime. However, due to the conflicting situations and imprecisions in the data, the problem of balanced energy consumption among sensor nodes remains unsolved. To design a guaranteed WSN lifetime protocol to provide support for real-time applications, the communication protocols must adjust their routing performance based on the packet deadline. However, it is not an easy task to simultaneously achieve guaranteed lifetime and real-time service in WSNs, because it is not feasible to describe them accurately by mathematical models. To address this problem, fuzzy logic can be an alternative model that has the potential for dealing with conflicting situations and imprecision in data using heuristic human reasoning without the need of complex mathematical models [6]. Furthermore, general fuzzy logic-based solutions are computationally quick, require modest resources and are able to drive effective and efficient results on a real-time basis [7]. Based on the aforementioned research motivations, we extend our previous work [5] in this paper by adding fuzzy controller algorithms to guarantee a pre-configured lifetime \(\alpha\), while achieving the real-time data delivery requirement. A fuzzy system is the combination of both systems and uncertainty models to configure the active/sleep mode of each sensor node. Through a fuzzy controller, an active/sleep scheduling is defined as an energy-efficient way to set a sensor's mode in each round. In more detail, the proposed scheme is achieving its objective by enabling each sensor node to consume energy approximately at the optimal energy consumption rate in order to guarantee a pre-configured lifetime. Consequently, since the number of active sensors may not be enough to satisfy two requirements at once in the network for the whole period of operations, we adopt a new three-tier node selection mechanism to further enhance our previous scheme. In the first tier, the sensing nodes are selected, followed by the selection of normal transmission range (NTR) nodes in the second tier and the selection of extended transmission range (ETR) nodes in the third tier. We consider real-time constraints by forcing a power-aware transmission in ETR nodes selected in the third tier. To forward real-time datapackets through third-tier ETR relay nodes, the fuzzy controller accepts energy and velocity as input descriptors for the third-tier ETR relay node selection.