A fusion architecture to deliver multipurpose mobile health services.

Mobile Health (mHealth) services typically make use of customized software architectures, leading to development-dependent fragmentation. Nevertheless, irrespective of their specific purpose, most mHealth services share common functionalities, where standard pieces could be reused or adapted to expedite service deployment and even extend the follow-up of appearing conditions under the same service. To harness compatibility and reuse, this article presents a data fusion architecture proposing a common design framework for mHealth services.

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance.

This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications.

Modelling and Simulation of Collaborative Surveillance for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems rely on collaborative position reporting to track unmanned aerial system (UAS) operations over wide unsurveilled (with counter-UAS systems) areas. Many different technologies, such as Remote-ID, ADS-B, FLARM, or MLAT might be used for this purpose, in addition to the direct exploitation of C2 telemetry, relayed though cellular networks. This paper provides an overview of the most used collaborative sensors and surveillance systems in this context, analyzing their main technical parameters and performance effects.

Sensors and Communication Simulation for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, and poorly repeatable. As a solution, in this paper we propose an agent-based simulation platform, implemented through a micro service architecture, which may simulate UTM information sources, such as flight plans, telemetry messages, or tracks from a surveillance network.

Drone Mission Definition and Implementation for Automated Infrastructure Inspection Using Airborne Sensors.

This paper describes a Mission Definition System and the automated flight process it enables to implement measurement plans for discrete infrastructure inspections using aerial platforms, and specifically multi-rotor drones. The mission definition aims at improving planning efficiency with respect to state-of-the-art waypoint-based techniques, using high-level mission definition primitives and linking them with realistic flight models to simulate the inspection in advance. It also provides flight scripts and measurement plans which can be executed by commercial drones.

A lightweight Web of Things Open Platform to facilitate context data management and personalized healthcare services creation.

In the last few years, many health monitoring systems have been designed to fullfil the needs of a large range of scenarios. Although many of those systems provide good ad hoc solutions, most of them lack of mechanisms that allow them to be easily reused. This paper is focused on describing an open platform, the micro Web of Things Open Platform (µWoTOP), which has been conceived to improve the connectivity and reusability of context data to deliver different kinds of health, wellness and ambient home care services.

An energy-efficient strategy for accurate distance estimation in wireless sensor networks.

In line with recent research efforts made to conceive energy saving protocols and algorithms and power sensitive network architectures, in this paper we propose a transmission strategy to minimize the energy consumption in a sensor network when using a localization technique based on the measurement of the strength (RSS) or the time of arrival (TOA) of the received signal. In particular, we find the transmission power and the packet transmission rate that jointly minimize the total consumed energy, while ensuring at the same time a desired accuracy in the RSS or TOA measurements. We also propose some corrections to these theoretical results to take into account the effects of shadowing and packet loss in the propagation channel.

Model-driven methodology for rapid deployment of smart spaces based on resource-oriented architectures.

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies.

Weighted least squares techniques for improved received signal strength based localization.

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location.