## 1 Introduction  
Recent advances in micro-electro-mechanical systems technology, wireless communications and digital electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in short distances. These tiny sensor nodes, which consist of sensing, data processing and communicating components, leverage the idea of sensor networks based on collaborative effort of a large number of nodes [1]. Hence, the wireless sensors networks (WSNs) are used in many scenarios [2] and for a lot of applications, such as home automation [3], health care [4], remote control [5], industrial control [6], environmental monitoring [7], intelligent transportation systems [8], _etc_. WSNs also represent a key component of the Internet of Things (IoT), which is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications. The basic idea of this concept is the pervasive presence around us of a variety of things or objects, tags, sensors, actuators, mobile phones, _etc._, which are able to interact with each other and cooperate with their neighbors to reach common goals [9]. Indeed, sensor networks will also play a crucial role in the IoT because they can cooperate with RFID systems to better track the status of things, _i.e._, their location, temperature, movements, _etc_. As such, they can augment the awareness of a certain environment and thus act as a further bridge between the physical and the digital worlds. Today, most of commercial wireless sensor network solutions are based on the IEEE 802.15.4 standard, which defines the physical and MAC layers for low-power, low bit rate communications in wireless personal area networks (WPANs) [10]. If WSNs are sufficiently mature to be used in several applications, as previously discussed, still great efforts are needed towards the definition of methodologies and the design of tools to support their deployment and management. Indeed, deployment of wireless networks in realistic scenarios, often represented by a combination of outdoor and indoor areas, requires the analysis and the modeling of the wireless channels to optimally place the nodes so as to maximize the network lifetime while achieving the WSN application goals (e.g., sensing the status of the environment); it also requires the identification of the best roles of each node (e.g., how many routing nodes to run and which configuration to be set up). After deployment, network management requires methodologies and tools to support the monitoring and troubleshooting during the whole network life. Key to all these activities is the definition of the performance indicators, which should be able to provide a general view of the network status and to easily highlight the major issues, and the techniques to perform the relevant measures, which should be accurate and not intrusive at the same time. On the basis of the availability of the information related to the network channel links, it is possible to improve the network throughput [11], lifetime [12], packet reception rate [13], fault tolerance [14]. Many are the works focused on performance monitoring for WSNs [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. In [15], the [PERSON] _et al_. focus on the in-band monitoring approach so that diagnosis packets are transmitted on the same band used for the WSN communications, whereas, in [20], [PERSON] _et al_. propose an out-band monitoring framework so that the diagnosis packets are sent in a separate band with respect to one used by the WSN. In [25], [PERSON] _et al_. focus on a passive analysis and propose a set of tools to analyse the packets captured by a sniffer. As it will be clear from the analysis of the state of the art in next section, one of the major problems in the proposed methodologies is their intrusiveness. It is due to the need for the nodes to measure the received signal power and to generate and transmit packets conveying the measurement results to the network.