## 2 Related Works  
The concept of _Smart City_ was created in the context of an increasing demand for resource management in modern cities. In parallel, the constant monitoring of places and objects by interconnected devices originated the Internet of Things (IoT) concept, with objects sharing information autonomously (machine-to-machine communication) [15]. Actually, Smart Cities and IoT have different origins, but they are converging to a common goal of \"smart living\" [4]. A Smart City is typically comprised of one or more heterogeneous wireless sensor networks, composed of different types of sensors (scalar, video, image and audio). When combined, those sensors provide information about the status of a place or resource. In other words, wireless sensor networks are a relevant component of the backbone of Smart City systems [16]. Independent of the amount of data flowing on the network, WSN should maintain minimum levels of Quality of Service (QoS) to support the city systems. One way to achieve that is giving priority indexes to sensors (sensing relevance) and changing them according to some external or internal parameter (e.g., sensors with better coverage have higher priority). Such relevances can be exploited for a series of network optimizations, providing services as error recovery, congestion control, routing,  
Figure 1: Abstract view of the proposed approach. multipath selection, among others [9]. For example, the work in [17] proposed a generic multiple sinks positioning algorithm based on the sensing relevances of sensors. The relevance level of each sensor node (priority) is considered to calculate the positions of every sink in WVSNs. In a different perspective, the work in [8] exploits the nature of transmitted data to differentiate how corrupted packets will be recovered. Whatever is the employed optimization approach, prioritization levels may be exploited to enhance the performance of the network, delivering high quality data while reducing resource expenditure. In general terms, we can expect that there are at least two priority-based optimization approaches in evidence on academic and industrial scenario: based on sensor's properties and based on events of interest. In the first approach, the relevance of each sensor is assigned according to its current properties (e.g., remaining power, location, sensing coverage, and communication characteristics) [9; 13; 18]. In the second approach, an event (e.g., explosion) can trigger a critical level of monitoring over a region [14; 19]. In both cases, events or sensor properties are processed and delivered by the network. However, depending on the number of idle sensors in the network and their processing load, event-based strategies can jeopardize the performance or compromise the network precision while the new priorities are computed and delivered. Despite this costly task, optimizations based on sensing relevance are an effective way to optimize the network without compromising the overall monitoring quality [20]. The presented issues and the trade-off between energy consumption, data quality and network availability may foster the development of new approaches in this research field. Priorities definition in Smart Cities may then be centered on the detection of events, which is consistent with the particularities of such environment. Actually, automatic event detection has been pointed as one of the main target uses for wireless sensor networks. As sensor nodes normally comprise of computation capabilities, rudimentary event detection is possible by applying some predefined thresholding over the input sensor signal (which includes \"on-off\"approaches) [21; 22], or more developed (still low-complex) local signal processing or decision taking algorithm [23; 24]. Note that the last case includes multimedia sensor networks [25], which work with more significant amounts of data, such as in images and sound (see, for example, works in [26; 27; 28; 29]).