The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any "thing" we may be interested in. Wireless sensor networks (WSN) are well suited for longterm environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring applications.

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.

A compilation-based software estimation scheme for hardware/software co-simulation / Lajolo, M.; Lazarescu, MIHAI TEODOR; Sangiovanni Vincentelli, A.. -ELETTRONICO. -(1999), pp. 85-89. ((Intervento presentato al convegno Hardware/Software Codesign, (CODES '99) tenutosi a Rome, Italy nel 1999. Original A compilation-based software estimation scheme for hardware/software co-simulation ieee Publisher: Published

Indoor human detection and localization sensors are at the base of many automation and monitoring systems. This work presents an indoor tagless passive human body identification method. It uses a load-mode capacitive sensor to detect the differences in the conductive and dielectric properties of the human body due to differences in body constituency. The experimental results show that four male individuals with similar height but different body mass index (BMI) standing at 70 cm in front of a chest-level 16 cm x 16 cm sensor plate determine different capacitance-frequency characteristics over a 5 kHz-160 kHz range, which can be used to identify the person.

Although useful for many applications, the practical use of tagless remote human identification is often hampered by privacy, usability, reliability or cost concerns. In this article, we explore the use of capacitive sensors, which appear to address most of these concerns, to identify different persons based on the unique electric and dielectric properties of their bodies given by their specific tissue composition. We present experimental results obtained by measuring the capacitance of a 16 cm×16 cm transducer plate 70 cm in front of different human bodies at different frequencies in the 5 kHz-160 kHz range. The measurements show clearly distinct signatures of capacitance variation with frequency for each person in the experiment, even after accounting for capacitance variations due to different body mass or physical dimensions. This work focuses on the contactless identification of human body using capacitive sensors in smart home environments.