The GB-InSAR is composed of a microwave transceiver mounted on a linear rail (Tarchi et al., 1997; Rudolf et al., 1999; Tarchi et al., 1999). The system used is based on a continuous-wave–stepped-frequency radar, which moves along the rail at millimeter steps in order to perform the synthetic aperture: the longer the rail, the higher the cross-range resolution. The microwave transmitter produces, step by step, continuous waves around a central frequency, which influences the cross-range resolution and determines the interferometric sensitivity, i.e., the minimum measurable displacement, usually largely smaller than the corresponding wavelength.

The radar produces complex radar images containing the information relative to both phase and amplitude of the microwave signal backscattered by the target (Bamler and Hartl, 1998; Antonello et al., 2004). The amplitude of a single image provides the radar reflectivity of the scenario at a given time, while the phase of a single image is not usable. The technique that enables to retrieve displacement information is called interferometry and requires the phase from two images. In this way, it is possible to elaborate a displacement map relative to the elapsed time between the two acquisitions.

The main added value of GB-InSAR is its capability to blend the boundary between mapping and monitoring, by computing 2-D displacement maps in near-real time. The use of this tool to monitor structures, landslides, volcanoes and sinkholes is widely documented (Calvari et al., 2016; Di Traglia, 2014; Intrieri et al., 2015; Bardi et al., 2016, 2017; Martino and Mazzanti, 2014; Severin et al., 2014; Tapete et al., 2013), as well as for early warning and forecasting (Intrieri et al., 2012; Carlà et al., 2016a, b; Lombardi et al., 2016).

GB-InSAR systems probably reveal their full potential in emergency conditions. They are transportable and only require from a few tens of minutes to a few hours to be installed (depending on the logistics of the site). Moreover, they can detect “near-real-time” area displacements, without accessing the unstable area, 24 h a day and in all weather conditions (Del Ventisette et al., 2011; Luzi, 2010; Monserrat et al., 2014). On the other hand, some limitations reduce the GB-InSAR technique applicability: first of all, the scenario must present specific characteristics in order to reflect microwave radiations, maintaining high coherence values (Luzi, 2010; Monserrat et al., 2014); only a component of the real displacement vector can be identified (i.e., the component parallel to the sensor's line of sight); and maximum detectable velocities are connected to the time that the system needs to obtain two subsequent acquisitions. Sensors need power supply that, for long-term monitoring, cannot be replaced by batteries, generators or solar panels.

With the specific aim of performing the function of an early warning system, data acquired in situ must be sent automatically to a “control center” where they are integrated into a complete early warning system procedure (Intrieri et al., 2013). In this sense, another main limitation is represented by the necessity to transfer a high quantity of data, whose weight has to be reduced to the minimum, in order to reduce the load on transmission network.

The employed system is a portable device designed and implemented by the Joint Research Center (JRC) of the European Commission and its spin-off company Ellegi-LiSALab (Tarchi et al., 2003; Antonello et al., 2004).

Morphological features, hydrogeological factors and sudden rainfall can cause diverse types of movements or fall of earthy and rock materials. The unpredictability and diversity of these events make structural interventions often inappropriate to reduce the related risk and a real-time monitoring network difficult to implement.

In the last decade, wireless sensor networks (WSNs) have been used extensively in various fields. A significant increase in the use of WSNs – due to their simplicity and the low cost of installation, manufacturing and maintenance – has been recorded in the framework of environmental monitoring applications (Intrieri et al., 2012; Liu et al., 2007; Yoo et al., 2007). Distinct types of sensor nodes of these networks, distributed with high density in the monitored areas, send environmental information to the concentrators nodes, generating a considerable amount and a wide variety of collected data. Due to the significant growth of data volumes to be transferred, the WSNs require flexible ad hoc protocols able to respect constraints related to energy consumption management (Hadadian and Kavian, 2016; Khaday et al., 2015; Parthasarathy et al., 2015). In particular, many protocols have been developed that offer data aggregation patterns to optimize the sensor nodes' battery life (Kim et al., 2015) or sleep–measurement–data transfer cycles to minimize the energy consumption (Fei et al., 2013; Venkateswaran and Kennedy, 2013).

LEWIS (Costanzo et al., 2016) uses heterogeneous sensors, distributed in the risk areas, to monitor the several physical quantities related to landslides. The measured data, through a telecommunications network, flow into the data collecting and processing center (DCPC), where, using suitable mathematical models for the monitored site, the risk is evaluated and eventually the state of alert for mitigation action is released (Fig. 1).

The system, through a modular architecture exploiting a telecommunication network (called LEWARnet) based on an ad hoc communication protocol and an adaptive middleware, has a high flexibility, which allows for the use of different interchangeable technological solutions to monitor the parameters of interest.

The network has been equipped with both single-point sensors and area sensors. The present paper addresses a sub-network comprising an area sensor, the GB-InSAR.

The different sensors types generate asynchronous traffic, thus imposing the adoption of an ad hoc transmission protocol. This can support an asynchronous transmission mode to the DCPC, and it is equipped with message queue management capacity to reconstruct historical data series, between two connection sessions, in case of null or partial transmission. This operation mode requires the presence of a software architecture that operates as a buffer, acting as an intermediary or as middleware (LEWARnet) between the data consumer (DCPC) and the data producers (sensors and sub-networks of sensors).

The developed middleware also monitors the processes of transmission and data acquisition, recognizing the activity status of the sensors and that of the DCPC, and integrating encryption and data compression functions.

A detailed description of LEWIS can be found in Costanzo et al. (2015, 2016).

The management of information flows, the telematic architecture and the services for data management are entrusted to the DCPC.

The DCPC has been designed and performed according to a complex hardware and software system able to ensure the reliability and continuity of the service, providing advance information of possible dangerous situations that may occur.

In the research project, the DCPC has to ensure the continuous exchange of information among monitoring networks, mathematical models and the command and control center (CCC) that is responsible for emergency management and decision making.

Data flow from the monitoring network was managed according to a communication protocol implemented by the DCPC and named AqSERV. AqSERV was designed considering the heterogeneity of devices of monitoring and transmission networks (single-point and area sensors) and the available hardware resources (microcontrollers and/or industrial computers). AqSERV was devised to link the DCPC database (named LEWISDB) to the monitoring networks, after validation of the authenticity of the node that connects to the center. Data acquisition, before the storage in the database, is validated both syntactically and according to the information content. The procedures for extraction of the information content and validation have been realized differently for single-point and area sensors: the latter require a more complex validation, as they work in a 2-D domain.

The complete management of the monitoring networks by DCPC has been realized through specific remote commands, sent to individual devices via AqSERV, to reconfigure the acquisition intervals or to activate any sensor, depending on the natural phenomena occurring in real time.

The configuration of monitoring networks – composed of devices and sensors, of communication protocol used by each network, and of rules for extraction and validation of information content – is carried out through a Web application that allows for the management of the entire system by the users.

The real-time search for acquisitions is carried out through a WebGIS that has been specifically designed for WSNs but that can be easily extended to classic monitoring networks.

The WebGIS was designed according to the traditional Web architecture, client–server, by using network services which are Web mapping oriented:

Web server for static data,

The GB-InSAR employed produced a single radar image, consisting of a 1001×1001 complex matrix, every 5 min. Each one is around 8 megabytes large, resulting in more than 2 gigabytes of data produced every day.

This amount of data represented an issue for both store capacity and data transmission.

After being acquired, data were then transferred through LAN connection to the external PC implementing a dedicated Matlab script locally performing the actions described as follows.

The interferometric data generated by GB-InSAR, after the pre-processing and proper correction previously described, are ready for transfer to the DCPC. The transmission of these data to the DCPC is mediated by the middleware, which interrogates the GB-InSAR in order to track the state, detects the newest data, and reorders and marks them to properly build data time series to be transferred to DCPC.

Subsequently, the middleware manages communications with the DCPC, according to the implemented ad hoc protocol. This ensures the security of data providers through encrypted authentication mechanisms; it allows for recovering missing or partially transmitted data, thus avoiding information loss; and it provides data acquired by the sensors to the DCPC in a standardized format, JSON, able to guarantee uniformity between the various information provided by the various sensors types. All these particular features fully justify the adoption of an ad hoc protocol for data transfer, instead of using a standard protocol such as FTP.

The data files produced by the GB-InSAR have already been locally pre-processed and result in a matrix expressed in ASCII code; the dimensions of the matrix are known and range from 1×1 (for the displacement of single control points) to 1001×1001 (for uncropped displacement maps). Before encapsulating these data in the message to be transferred to DCPC, the middleware converts them from ASCII code to character strings, using the standard coding ISO/IEC 8859-1, and so is able to obtain a data compression with a factor equal to ≈8.

Eventually the DCPC is entrusted with cumulating the displacements relative to the control points, which are compared with the respective thresholds, and with visualizing the displacement maps as WebGIS layers, thus enabling data validation and the evaluation of the extension of moving surface.