The Internet-of-Things revolution is coming to reality and most of real-life scenarios are experiencing the pervasive presence of network-enabled devices, in most cases sensors. The deployment of heterogeneous sensors at several locations enables collection of different kinds of (big amount of) data about the surrounding scenario. A wireless sensor network is the typical solution for data collection, data processing, and inference in most of Internet-of-Things applications. Wireless sensor networks have been studied extensively in various research contexts with results explored many applications, such as surveillance, security, traffic control, health care, environmental monitoring, and industrial monitoring. These networks are often tailored for a specific application, thus their design and deployment account for specific purposes. However, severe bandwidth and energy limitations on the network make resource allocation a crucial issue for the effective system design. A wireless sensor network consists of a number of small and inexpensive nodes dispersed over a geographic area to estimate/detect one (or more) parameter(s) of interest. Each node has sensing capabilities, limited computational and storage capabilities, limited energy availability, and is connected through low-power wireless communications to similar neighbor nodes and/or to a more sophisticated node, namely, the fusion center. Coordination of the phases in which the sensors collect the information, share the information, and process the information to achieve a common understanding of the surrounding environment is the key aspect of a wireless sensor network. This edited book deals with data fusion in wireless sensor networks from a statistical signal-processing perspective. It develops through four parts, each made of three chapters. The first part focuses on problems related to the sensing phase (i.e., when the wireless sensor network collects the information), while the second and third parts concentrate on problems related to the reporting phase (i.e., when the wireless sensor network shares the information), in centralized and decentralized architectures, respectively. Finally, the last part emphasizes potential benefits from cross-layer approaches related to energy harvesting and security requirements.