## 1 Introduction  
A Wireless Sensor Network (WSN) [1; 2] is a special type of ad hoc network that has become popular due to its wide applicability, which includes environmental, medical, industrial, and military applications [3]. A WSN comprises a large number of low-cost sensor nodes that can perceive the environment and perform collaborative tasks to monitor an area of interest. Because WSNs are resource-constrained networks--regarding energy, bandwidth, and computational capacity--algorithms and solutions for WSNs need to be resource efficient. In addition, WSNs demand self-organizing features, i.e., the ability of autonomously adapt to changes resulting from external interventions, such as topological changes (cause by failures, mobility, or node inclusion) or reaction to an event detection. An important issue in WSNs is collecting and processing data perceived from the environment and sending that data to be processed and evaluated by an external entity connected to a sink node (or gateway). As a consequence, routing data towards the sink node is a fundamental task in WSNs. Due to the resource constraints, different algorithms have been proposed considering specific application and scenario characteristics [4]. Thus, given a specific scenario, a WSN may be designed to operate with the most suitable routing algorithm, which can be defined _a priori_. Regarding the application characteristics, WSNs can demand different data collecting strategies, such as periodic, eventual, or on-demand strategies [5]. In particular, we expect that several applications should adopt the event-driven model. These applications are concerned with special events that should not be so frequent (e.g., fire or intrusion detection), so that the network must work in a low activity mode until an event occurs. In such scenarios, the network behavior may frequently change in an unpredictable way. For example, the network may present a low traffic profile for a long time, but after a while a few events occur, which increase the traffic load and the routing workload as well. Such a feature favors different algorithms in different situations. However, it might be unfeasible or undesirable to have an external entity dynamically acting on the network to change its behavior. In this case, the network must adapt itself based on autonomic principles. Hybrid adaptive approaches for routing are natural choices to deal with dynamic scenarios by choosing the best routing strategy in response to current network conditions. In this context, some protocols have been proposed for ad hoc networks (MANETs), such as ZRP [6] and SHARP [7]. However, they are not suitable for WSNs due to fundamental differences in the network, applications, and traffic profiles. The first effort in the WSN domain was made by us in [8], which initially proposed a hybrid solution composed of traditional reactive and proactive routing strategies for WSNs by using a simple traffic limiar, and in [9], which improves the adaptation rule by considering the event occurrence characteristic. In fact, this article is an extended version of these work. In this work, we evolve this approach by considering the temporal characteristic of event occurrence and detection through an estimation model. Thus, the proposed solution, called Multi-MAF, captures the seasonal and dynamic characteristics of events for predicting and choosing which routing strategy should be used: a reactive or proactive strategy. The Multi-MAF algorithm is evaluated through a set of simulations that compares the hybrid approach to their reactive and proactive components, and other well-known proposals. The remainder of this paper is organized as follows. Section 2 presents a broader discussion about data routing in WSNs. Section 3 presents the Multi-MAF algorithm by describing its reactive and proactive components and the adopted adaptation model. Section 4 evaluates Multi-MAF by comparing the hybrid solution to its individual reactive and proactive components. Finally, Section 5 presents our final considerations and outlook.