## 1 Introduction  
Recent developments in smart sensor technology have opened new perspectives for advanced applications of wireless sensor networks (WSNs) in many domains including healthcare, automation, infrastructure, and environment. The smart sensor nodes in WSN enable complex processing and analysis of sensed data with increased computational power [1; 2]. WSNs have become an important part of 5G mobile technology that open new perspectives for advanced applications of smart sensors in future Internet of Things (IoT) applications [3; 4]. Typical WSNs are composed of nodes that sense their environment by using built-in sensors and transmit the sensor readings to a sink node [5]. In contrast, smart sensor nodes have an additional ability to process the collected data, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes [6]. Instead of transmitting raw data readings, the smart sensor nodes report the detected events. Moreover, to recognize the important events in larger regions, many smart nodes must cooperate. It means that the sensor nodes from a given region of interest are grouped into cluster and exchange sensed information to make a common decision regarding occurrence of an event [7; 8]. In this case, the individual sensor nodes assess partial detection results and report them to one selected node, which acts as a cluster head (CH). One CH node is selected for each cluster. The CH node combines the collected partial results to recognize events in the region of interest. Finally, the information about events is reported by CH node to the sink. The key issue in applications of wireless sensor networks is the limited lifetime of battery-powered sensor nodes. Thus, network management methods are necessary to enable effective use of the energy resources of sensor nodes [9; 10]. In this study, the term lifetime refers to the time interval of the WSN operation before death of the first node. It should be noted that the uninterrupted operation of all sensor nodes in WSN is crucial for many applications where the feedback from the network must be reliable [11]. This paper introduces a method for extending lifetime of the above-mentioned wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the CH role among sensor nodes with suppression of unnecessary data transmissions. Data transmission is the most energy expensive operation for energy constrained wireless sensor nodes. According to the suppression approach, consumption of battery power in sensor nodes is reduced by transmitting the data only if it is necessary [12]. In the considered scenario, a smart sensor node, which did not detect any relevant event, can skip transmission to CH node to save the energy. In general, the transmissions of partial results to CH can be suppressed if the event detection results that would be obtained by the CH with and without these partial data are the same. To prolong the lifetime of wireless sensor network, a balance must be achieved between energy consumption of all sensor nodes. CH node consumes more battery energy than the remaining sensor nodes. The reason behind unequal energy consumption is that CH node must remain active most of the time, while other nodes are active only when processing and transmitting data. During inactive time, the sensor nodes are switched to sleep mode for saving energy. Thus, the CH node depletes its energy resources faster than the other nodes, which leads to reduced network lifetime. The balance of energy consumption can be restored by periodically reassigning (rotating) the CH role to different sensor nodes [13]. It should be also noted here that the energy balance is significantly disrupted by the mechanism of transmission suppression. The smart sensor nodes that detect a higher number of important events must perform the data transmissions more frequently and consume a larger amount of energy.