The majority of security systems for wireless sensor networks are based on symmetric encryption. The main open issue for these approaches concerns the establishment of symmetric keys. A promising key distribution technique is the random predistribution of secret keys. Despite its effectiveness, this approach presents considerable memory overheads, in contrast with the limited resources of wireless sensor networks. In this paper, an in-depth analytical study is conducted on the state-of-the-art key distribution schemes based on random predistribution. A new protocol, called q-s-composite, is proposed in order to exploit the best features of random predistribution and to improve it with lower requirements. The main novelties of q-s-composite are represented by the organization of the secret material that allows a storing reduction, by the proposed technique for pairwise key generation, and by the limited number of predistributed keys used in the generation of a pairwise key. A comparative analysis demonstrates that the proposed approach provides a higher level of security than the state-of-the-art schemes.

Dense passive radio frequency identification (RFID) systems are particularly susceptible to reader collision problems, categorized by reader-to-tag and reader-to-reader collisions. Both may degrade the system performance decreasing the number of identified tags per time unit. Although many proposals have been suggested to avoid or handle these collisions, most of them are not compatible with current standards and regulations, require extra hardware and do not make an efficient use of the network resources. This paper proposes the Geometric Distribution Reader Anti-collision (GDRA), a new centralized scheduler that exploits the Sift geometric probability distribution function to minimize reader collision problems. GDRA provides higher throughput than the state-of-the-art proposals for dense reader environments and, unlike the majority of previous works, GDRA is compliant with the EPCglobal standard and ETSI EN 302 208 regulation, and can be implemented in real RFID systems without extra hardware.

Northern Italy has one of the highest air pollution levels in the European Union. This paper describes a mobile wireless sensor network system intended to complement the already existing official air quality monitoring systems of the metropolitan town of Torino. The system is characterized by a high portability and low cost, in both acquisition and maintenance. The high portability of the system aims to improve the spatial distribution and resolution of the measurements from the official static monitoring stations. Commercial PM ${}_{10}$ and O ${}_3$ sensors were incorporated into the system and were subsequently tested in a controlled environment and in the field. The test in the field, performed in collaboration with the local environmental agency, revealed that the sensors can provide accurate data if properly calibrated and maintained. Further tests were carried out by mounting the system on bicycles in order to increase their mobility.

Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors reliability made by comparing our results with more accurate and expensive standard - radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in [1].

Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.