This paper presents an approach for explosive-landmine detection by designing, developing and integrating a Ground Penetrating Radar (GPR) onboard an autonomous aerial drone. Our goal is twofold: firstly, we have tackled the development of a custom-designed lightweight GPR by approaching an interplay between hardware and software radio. This technology is called Software-Defined Radio (SDR). Our SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real time. This means our system is capable of detecting landmines under terrains with different dielectric characteristics. Secondly, we introduce the integration of the aforementioned SDR-based GPR into an autonomous drone. To this purpose, we have developed an accurate navigation flight control system based on optical laser technology that enables the drone to operate steady at the corresponding altitude over the ground (50 cm). To this purpose, we propose a novel nonlinear model-dependent control strategy for regulating the altitude (z) and the attitude of the drone (roll , pitch , yaw ) named backstepping+DAF. Experiments have been conducted aimed at characterizing how our SDR-based GPR system performs depending on different landmine morphology. Also, how steady flight is achieved by means of the proposed flight control system that consequently enhances the GPR performance. Results have shown that our SDR-based GPR system is able to detect buried landmines up to cm of depth for semi-wet terrains (humidity about 70%), however, the artifact must cover an area with at least of the material made of metal. By calculating ROC data, the accuracy of the GPR is above 80% under the tested scenarios.

This paper presents P-Sense (Pollution-Sense), a PS system for air pollution monitoring and control. The ultimate goal of this system is to allow government officials, international organizations, communities, and individuals access to the pollution data to address their particular problems and needs. P-Sense should provide large amounts of pollution data in time and space with different granularities. Government officials will have data to monitor and control the Air Quality Index (AQI) [1] of a city, state, or country; doctors will be able to correlate respiratory problems of their patients to the AQI they are exposed to during their daily activities, in the places they work and live; county officials, community developers, and realtors will have data to determine the best place where to build a new school or community and advertise properties according to the AQI where they are located.

This paper presents the design and implementation of an Internet of Thing (IoT)-based system for indoor localization using Bluetooth Low Energy (BLE) technology. Our solution consists of two main systems: an acquisition system and a central server, under the Client-Server paradigm and the IoT philosophy. We report the development of different modules: measurement (Bluetooth beacons), data aggregation and transmission, storage, web-interface and cloud services for data, and results visualization. The localization mechanism of the proposed system is based on a simple location algorithm stemming from the Received Signal Strength (RSS) footprinting method, which allow us to detect reference zones inside closed environments. We performed real experiments in order to assess the proposed system. This paper reports the design, implementation and evaluation of an IoT-based system for indoor location using Bluetooth Low Energy (BLE) technology.

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

Participatory Sensing (PS) is a new data collection paradigm based on the voluntary participation of many cellular users equipped with smart applications, a large diversity of sensors, and Internet connectivity at all times. Although many PS-based applications can be foreseen to solve interesting and useful problems, many of them have not been fully implemented and used in practice because of privacy concerns. Compounding the problem, privacy-preserving mechanisms introduce additional issues. For example, one of the most important problems is that of the quality of the information provided by the PS system to the final users. The problem is that, in order to protect the privacy of the users, most privacy-preserving mechanisms modify their real locations, which makes the reported data as if it had been measured from a different location, introducing noise or false information in the system and to the final users. Another important problem is that of the energy consumption. Privacy-preserving mechanisms consume extra energy and users are not very willing to use PS applications if they drain their batteries considerably faster. This paper proposes a hybrid privacy-preserving mechanism that combines anonymization, data obfuscation, and encryption techniques to increase the quality of information and privacy protection without increasing the energy consumption in a significant manner. A new algorithm is proposed that dynamically changes the cell sizes of the grid of the area of interest according to the variability of the variable of interest being measured and chooses different privacy-preserving mechanisms depending on the size of the cell. In small cells, where users can be identified easier, the algorithm uses encryption techniques to protect the privacy of the users and increase the quality of the information, as the reported location is the real location. On the other hand, anonymization and data obfuscation techniques are used in bigger cells where the variability of the variable of interest is low and therefore it is more important to protect the real location (privacy) of the user. We evaluated our hybrid approach and other privacy-preserving mechanisms using a real PS system for air pollution monitoring. Our experiments show the better performance of the proposed hybrid mechanism and the existing trade-offs in terms of privacy, quality of information to the final user, and energy consumption.