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Arterial pulse waves contain a wealth of parameters indicative of cardiovascular disease. As such, monitoring them continuously and unobtrusively can provide health professionals with a steady stream of cardiovascular health indices, allowing for the development of efficient, individualized treatments and early cardiovascular disease diagnosis solutions. Blood pulsations in superficial arteries cause skin surface deformations, typically undetectable to the human eye; therefore, Microelectromechanical systems (MEMS) can be used to measure these deformations and thus create unobtrusive pulse wave monitoring devices. Miniaturized ultrathin and flexible Aluminium Nitride (AlN) piezoelectric MEMS are highly sensitive to minute mechanical deformations, making them suitable for detecting the skin deformations caused by cardiac events and consequently providing multiple biomarkers useful for monitoring cardiovascular health and assessing cardiovascular disease risk. Conventional wearable continuous pulse wave monitoring solutions are typically large and based on technologies limiting their versatility. Therefore, we propose the adoption of $29.5 ~\mu \text{m}$ -thick biocompatible, skin-conforming devices on piezoelectric AlN to create versatile, multipurpose arterial pulse wave monitoring devices. In our initial trials, the devices are placed over arteries along the wrist (radial artery), neck (carotid artery), and suprasternal notch (on the chest wall and close to the ascending aorta). We also leverage the mechano-acoustic properties of the device to detect heart muscle vibrations corresponding to heart sounds S1 and S2 from the suprasternal notch measurement site. Finally, we characterize the piezoelectric device outputs observed with the cardiac cycle events using synchronized electrocardiogram (ECG) reference signals and provide information on heart rate, breathing rate, and heart sounds. The extracted parameters strongly agree with reference values as illustrated by minimum Pearson correlation coefficients (r) of 0.81 for pulse rate and 0.95 for breathing rate.

The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary (“Normal”/”Pathologic”) and multiclass (“Normal”, “CAD” (coronary artery disease), “MVP” (mitral valve prolapse), and “Benign” (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers’ performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.

Technological innovations have resulted in a digital transformation in a variety of fields, including culture and tourism. We propose an innovative and personalized solution to benefit art and cultural heritage in indoor and outdoor environments by combining Internet of Things-enabled technologies and deep learning-based approaches. A recent Convolutional Neural Network (CNN) architecture to jointly perform local feature detection and description has been adapted and exploited for the first time for image matching in the cultural heritage application context. The performance validation of the proposed system shows that the proposed modular architecture ensures a very low error rate and excellent response time up to 2000 user visits in 700 seconds. The validation of the computer vision module shows as the proposed CNN based feature extraction approach improves image matching performance, especially in poorly textured object areas reaching a F1-Score of 0.9907 (against the 0.9679 obtained by traditional gradient based approaches) on the challenging dataset of images taken from 4 different historical sites and a F1-Score of 0.9807 (against the 0.9798 obtained by traditional approaches) on a public benchmark dataset of artworks.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

The Internet of Things has contributed to make smarter houses and buildings in the last decades. Different existing works already integrate IoT technologies in homes, but end-user needs continuously change and researchers must face this challenge in identifying platforms to fast prototype solutions satisfying these new needs. This paper presents a solution that demonstrates how well-known fast-prototyping technologies like Node-RED, IBM Watson, Telegram, Raspberry Pi 4, and secured MQTT can contribute to develop complex systems facing the challenge. The selected tools are used within a smart home context to support features inspired by people needs and allow users to: a) consult real time conditions (i.e., temperature, humidity, gas), b) remotely manage lights, c) save energy through a light management system based on user movements, d) remotely monitor the house through dedicated webcams, e) generate warning notifications in case of danger. Users can interact with the systems through a web Node-RED dashboard and a Telegram bot. Differently from existing works, the feasibility of the implemented system and the efficacy of the exploited platforms are demonstrated through a running scenario extracted from a consolidated study on user needs in smart homes. The performed experiment can facilitate the fast prototyping of new solutions.

One of the most important factors that influence people’s daily lives and their well-being at work is the so-called “worker safety”. Different literature works demonstrated the positive effects on worker mood and well-being brought by the awareness of being in a safe environment and, consequently, less prone to accidents. Every working environment should guarantee safety protection to employees and operators both in normal operations and extraordinary duties (e.g., maintenance operations), however, the industrial domain is the one that is more exposed to risks for workers. Different technologies already accomplished such requirements in “normal” operations, nonetheless, the literature still lacks solutions to also monitor and guide operators during exceptional and dangerous operations (e.g., maintenance). The combination of IoT and Industry 4.0 can guide the research toward the resolution of the maintenance-related exposed problems. This paper proposes an IoT-aware architecture for the industrial domain to support maintenance operators. It was designed to guide them step by step while real-time monitoring plant, machinery, and other employees working in the same area. During the maintenance procedure, the operator is guided in the proper execution of every single step required by maintenance and an autonomous IoT system monitors the status of the different parts of the plants and machinery to, then, authorize and show, the next steps foreseen in the maintenance process. To test the feasibility and usefulness of the proposed system, a prototype was developed and functionally tested through the exploitation of a machinery simulator and a real lathe machine.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

Emotions are the base of human evolution. They help us to survive and to face up all problems of our life. Without emotions human evolution was not possible and we would be in caves. Nowadays, emotions are a very important aspect in different field not only in psychology. They are very important to understand human mind and decision-making process.Emotional tourism is an example of a new way to use emotions analysis. In this field emotions are used to create a most deep experience from the begin of a travel to each steps of the journey. They help tourism to make traveler the protagonist of his travel and not just a spectator.In this paper, we are going to show an app which predicts a travel destination based on user’s mood and facial expressions to specifics visual and auditory trigger to encounter his reactions. This app uses different technology linked together to make this solution versatile and dynamic. It implements different technology modules to perform facial and mood analysis, capturing the image, store image and show all trigger to the user. By adopting this solution is possible to easily upgrade the app and each module can be changed with no large problem adapting it to the current version of the app.

The Internet of Things (IoT) is characterized by many technologies, standards, tools and devices for a wide range of application fields and often, for the end-users (makers and developers), is hard to orientate in an equally wide range of offers from various manufacturers. In recent years, the Bluetooth Low Energy (BLE) communication protocol is achieving a large portion of the market, thanks to its low-power and low-cost orientation and its pervasiveness in mobile devices, like smartphones. For these reasons, BLE is increasingly used in IoT-oriented Wireless Personal Area Networks (WPAN), where a small set of devices arranged in star topology network and connected to a smartphone and a Wi-Fi gateway, can cover a large number of monitoring and controlling use case scenarios. This work presents the ST’s STM32 Open Development Environment (ODE), a complete suite of hardware and software tools representing a reference point for end-users willing to create BLE-based star topology networks for a wide range of applications. Through a simple use case in a smart home context, it is shown how all provided tools can be used to fast prototype applications addressing all user requirements.

One of the main concerns of the last century is regarding the air pollution and its effects caused on human health. Its impact is particularly evident in cities and urban areas where governments are trying to mitigate its effects. Although different solutions have been already proposed, citizens continue to report bad conditions in the areas in which they live. This paper proposes a solution to support governments in monitoring the city pollution through the combination of user feedbacks/reports and real-time data acquired through dedicated mobile IoT sensors dynamically re-located by government officials to verify the reported conditions of specific areas. The mobile devices leverage on dedicated sensors to monitor the air quality and capture main roads traffic conditions through machine learning techniques. The system exposes a mobile application and a website to support the collection of citizens’ reports and show gathered data to both institutions and end-users. A proof-of-concept of the proposed solution has been prototyped in a medium-sized university campus. Both the performance and functional validation have demonstrated the feasibility and the effectiveness of the system and allowed the definition of some lessons learned, as well as future works.

The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person.

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects.

The introduction of Internet of Things enabling technologies into the sport and recreational activities domain provide an interesting research challenge. Their adoption could significantly improve the sport experience and also the safety level of team sports. Despite this, only few attempts have been done to demonstrate the benefits provided by use of IoT technologies in sport environments. To fill this gap, this paper propose an IoT-aware Sport System based on the jointly use of different innovative technologies and standards. By exploiting the potentialities offered by an ultra-low-power Hybrid Sensing Network (HSN), composed of 6LoWPAN nodes integrating UHF RFID functionalities, the system is able to collect, in real time, both environmental parameters and players’ physiological data. Sensed data are then delivered to a Cloud platform where a monitoring application makes them easily accessible via REST Web Services. A simple proof of concept has demonstrated the appropriateness of the proposed solution.

Recently, one of the hottest topics in the logistics sector has been the traceability of goods and the monitoring of their condition during transportation. Perishable goods, such as fresh goods, have specifically attracted attention of the researchers that have already proposed different solutions to guarantee quality and freshness of food through the whole cold chain. In this regard, the use of Internet of Things (IoT)-enabling technologies and its specific branch called edge computing is bringing different enhancements thereby achieving easy remote and real-time monitoring of transported goods. Due to the fast changes of the requirements and the difficulties that researchers can encounter in proposing new solutions, the fast prototype approach could contribute to rapidly enhance both the research and the commercial sector. In order to make easy the fast prototyping of solutions, different platforms and tools have been proposed in the last years, however it is difficult to guarantee end-to-end security at all the levels through such platforms. For this reason, based on the experiments reported in literature and aiming at providing support for fast-prototyping, end-to-end security in the logistics sector, the current work presents a solution that demonstrates how the advantages offered by the Azure Sphere platform, a dedicated hardware (i.e., microcontroller unit, the MT3620) device and Azure Sphere Security Service can be used to realize a fast prototype to trace fresh food conditions through its transportation. The proposed solution guarantees end-to-end security and can be exploited by future similar works also in other sectors.

The Web of Things paradigm has represented a shift in the conjunction of the Internet of Things (IoT) with people, as it allows treating a smart object as a Web resource. While in a first phase the challenge was the physically management of smart objects, the current demand is to help users in profitably introducing IoT in their own daily life.The paper presents a software architecture for IoT systems able to manage the behaviour of involved IoT entities basing on knowledge processing tools. The main goal is informing the user of the occurrence of events of interest semantically determined starting from actual state of the environment. The architecture exploits the potentialities of the Web of Topics (WoX) approach, a conceptual model that simplifies the designing of IoT applications. Leveraging the WoX approach, the architecture introduces an innovative way to mine knowledge from IoT devices aside from any technological background, so that facing the intrinsic heterogeneity affecting IoT entities. The discussed architecture is composed by different modules integrated into an Enterprise Service Bus (ESB), strongly decoupled and provided with RESTful-compliant web interfaces to communicate each other and with the external environment, according to a SOA structure. The paper shows how the system is able to receive data coming from sensors and to semantically interpret them by means of a series of business rules that act as knowledge processor.

The Internet of Things (IoT), with its plethora of smart objects and technologies, allows to realize smart environments in several scenarios. However, the existing solutions are strictly intended for specific applications and their customization is often limited to what developers have considered at the design and implementation time. So, the integration of new functionalities requires significant changes by developers, while common users cannot make personalizations by themselves. For these reasons, this work deals with the definition of a novel rule-based semantic architecture for the easy implementation of building automation applications in an IoT context. Applications are structured as an Event-Condition-Action (ECA) rule and the layered architecture separates high-level semantic reasoning aspects from low-level execution details. The proposed architecture is also compared with main state-of-the-art solutions and a standard-based implementation framework is suggested. The last aspect is treated by referring to standardized guidelines and widely-accepted platforms, in order to make the proposal more attractive and robust.

The analysis of locomotion in laboratory animals plays a crucial role in many scientific research areas. In fact, important information on animals’ behavior and their reaction to a particular stimulus is deduced from a careful analysis of their movements. The techniques commonly adopted to support such analysis have many limitations, which make the related systems particularly ineffective. On the one hand, the human observation and annotation process is strongly observer-dependent and expensive in terms of time and efforts. On the other hand, the use of more sophisticated systems based on video recordings and recognition algorithms is very expensive and complex. In order to face this challenge, this paper presents a tracking solution based on passive Radio Frequency Identification (RFID) technology in Ultra High Frequency (UHF) band, allowing the tracking of laboratory animals with a high accuracy. The overall solution consists of a hybrid system including hardware and software components. In particular, in this paper, the attention is focused on the software component as the hardware has already been described in previous works. The software component is a Web-oriented solution that offers a complete 2D and 3D information tool including reports, dashboards, and tracking graphs. The proposed solution was widely tested using twelve laboratory mice and compared with an automated video-tracking software (i.e., EthoVision) in order to demonstrate its effectiveness and reliability. The obtained results have demonstrated that the proposed solution is able to correctly detect and reconstruct the events occurring in the animals’ cage, and to offer a complete and user-friendly tool to support researchers in behavioral analysis of small laboratory animals.

The Internet of Things has focused on new systems, the so-called smart things, to integrate the physical world with the virtual world by exploiting the network architecture of the Internet. However, defining applications on top of smart things is mainly reserved to system experts, since it requires a thorough knowledge of hardware platforms and some specific programming languages. Furthermore, a common infrastructure to publish and share resource information is also needed. In this paper, we propose a software architecture that simplifies the visual development and execution of mash-up applications based on smart things, exploiting Internet Web protocols and their ubiquitous availability even on constrained devices. We have developed a distributed architecture that allows to create and control mash-up applications in an easy and scalable way, without specific knowledge on both hardware and programming languages. In addition, we have also defined a centralized public database deployed on the Internet, to manage and share physical resource information. The effectiveness of the proposed framework has been tested through a real use case and experimental results have demonstrated the validity of the whole system.

Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) have received an ever-increasing attention in recent years, mainly because they represent two of the most important technologies enabling the Internet of Things vision. Although designed originally with different objectives, WSN and RFID represent two complementary technologies whose integration might increase their functionalities and extend their range of applications. However, important technological issues must still be solved in order to fully exploit the potentialities offered by such integration. In this work, an innovative RFID-WSN integration approach is presented and validated. It relies on the interconnection of a new-generation, long-range, EPCglobal Class-1 Generation-2 Ultra-High-Frequency (UHF) RFID tag with a commercial WSN node via the I2C interface. Experimental results have demonstrated the effectiveness of the proposed approach compared to existing solution in the literature. Interesting application scenarios enabled by the proposed RFID-WSN integration approach are briefly summarized at the end of the paper.

The worldwide spreading of Internet, in combination with the development of new low power and low cost embedded devices, has enabled the so-called Internet of Things vision. Wireless Sensor Networks represent an invaluable resource for realizing such scenario, inside which new and innovative applications could be developed. However, the low availability of resources and the reduced processing capacity of the target embedded platforms make the development of the next-generation applications very challenging. This paper proposes an innovative system architecture, called STarch, able to simplify the development of new applications and protocols for resource-constrained objects. It is meant to follow the software engineering principles and to support a wide range of applications, making both the programming easier and the code portable over multiple hardware platforms. STarch simplifies the network configuration process, through the use of an automatic mechanism based on the XML language and it runs properly on different operating systems, including FreeRTOS and Contiki. The feasibility of the proposed architecture has been proved by using a test bed approach, while an extensive performance analysis have been carried out in order to demonstrate its effectiveness in terms of memory requirements and processing delays.

Wireless Sensor Networks (WSNs) are receiving an ever increasing attention because they are one of the most important technologies enabling the Internet of Things vision. Since nodes of these networks are battery-powered, energy efficiency represents one of the main design objectives. This goal can be primarily achieved through an optimization of the communication phase, which is the most power consuming operation for a WSN node. However, the limited computational and storage resources of physical devices make the design of complex communication protocols particularly hard, suggesting, on the contrary, to integrate more simple communication protocols with hardware solutions aimed at energy saving. In this work, a new MAC protocol, compatible with the IEEE 802.15.4 standard, and a reconfigurable beam-steering antenna are presented and validated. They significantly reduce the nodes’ power consumption by exploiting scheduling techniques and directional communications. Specifically, both during transmission and receiving phases, the node activates exclusively the antenna sector needed to communicate with the intended neighbour. The designed antenna and the proposed protocol have been thoroughly evaluated by means of simulations and test-beds, which have highlighted their good performance. In particular, the MAC protocol has been implemented on the Contiki Operating System and it was compared with the IEEE 802.15.4 standard solution.

The adoption of solutions based on Radio Frequency IDentification technology in a wide range of contexts is a matter of fact. In many situations, such as the tracking of small-size living animals, the straightforward use of commercial systems does not ensure adequate performance. Consequently, both the RFID hardware and the software control platform should be tailored for the particular application. In this work, the specific requirements of Near Field Ultra High Frequency RFID reader antennas suitable for small-size animal localization and tracking are identified and a control system in a LabVIEW environment is designed. Afterwards, both hardware and software solutions have been implemented and validated. In particular, an algorithm based on the measured Received Signal Strength Indication, in order to obtain precise localization data, was developed and validated. Finally, the set-up of a first working prototype involving built-in-lab reader antennas has been completed and tested. The achieved results prove the effectiveness of the proposed tracking system.

A Wireless Sensor Network consists of a large number of sensor nodes that are usually battery powered and deployed in large areas in which changing or recharging batteries may be impractical or completely unfeasible. Therefore, energy efficiency represents one of the main design objectives for these networks. Since most of the energy is consumed by the radio communication, the development of Medium Access Control protocols able to minimize the radio energy consumption is a very attractive research area. This paper presents an energy efficient communication protocol and its implementation in the Contiki Operating System. The performances and the portability of the proposed solution are thoroughly evaluated by means of both simulations, carried out using the Contiki simulation tools (i.e., Cooja and MPSim), and test beds based on two different platforms. Obtained results show that the proposed scheme significantly reduces the sensor nodes power consumption compared to the IEEE 802.15.4 standard solution already implemented in Contiki.

The evolution of modern mobile devices towards novel Radio Frequency (RF) capabilities, such as Near Field Communication, leads to a potential for delivering innovative mobile services, which is still partially unexplored. Mobile proximity payment systems are going to enhance the daily shopping experience, but the access to payment security resources of a mobile device (e.g. the “Secure Element”) by third party applications is still blocked by smartphone and Operating System manufacturers. In this paper, the IDA-Pay system is presented, an innovative and secure NFC micro-payment system based on Peer-to-Peer NFC operating mode for Android mobile phones. It allows to deliver mobile-to-POS micro-payment services, bypassing the need for special hardware. A validation scenario and a system evaluation are also reported to demonstrate the system effectiveness and performance.

Radio Frequency Identification is going to play a veryimportant role as auto-identification solution for manyapplication scenarios, where item-level tagging and highperformance are crucial. In such a context, the use of passive Ultra High Frequency (UHF) tags is strongly suggested but, unfortunately, general-purpose commercial tags could not meet all the requirements in presence of critical operating conditions, including the presence of metals and liquids, the misalignment between tag and reader antennas, and the need of multiple reading of tags. In this paper, the main features that a UHF tag should own to work properly in the whole supply chain are presented. A tag, named below Enhanced tag, satisfying all theindividuated requirements has been also realized and validated in a controlled test environment simulating the pharmaceutical supply chain. Tests have been focused on the above-mentioned critical conditions. The performance of the Enhanced tag, in terms of successful read rate, has been compared with that of some commercial Far Field and Near Field UHF tags. The experimental results are impressive and clearly demonstrate that ad hoc Far Field UHF tags are able to effectively solve many of the performance degradation problems affecting generalpurpose tags. Finally, the proposed tag has been also tested in extreme conditions, applying it directly on Tetra Pak packages containing liquid, with interesting results in terms of platformtolerant features.

Wireless Sensor Networks are a key element of the Internet of Things since they are one of the most interesting pervasive systems. However, they are battery-powered, so energy efficiency represents one of the primary design challenges to address. This goal can be primarily achieved through an optimization of the communication procedure, which is the most power-consuming component of a WSN node. Nevertheless, the poor resources of the embedded devices usually limit the complexity of protocol solutions, which thus are not sufficient to reach satisfactory results. For this reason, the current trend aims at integrating both hardware and software solutions. In this work, a cross-layer solution, based on the combined use of a duty-cycling MAC protocol and a reconfigurable beam-steering antenna, is presented and validated. It significantly reduces the nodes' power consumption by exploiting scheduling techniques and directional communications. Specifically, a MAC scheduler manages the activation of the antenna sectors based on information coming from both MAC and network layers. This way, node awakenings occur only when an actual communication has to take place and only the interested antenna sector is activated. The effectiveness of the proposed cross-layer approach has been evaluated through OMNeT++ simulation tool.

In this paper a protocol, local to the satellite link, is defined in order to boost TCP performance in mobile integrated wired-satellite Internet. It has been conceived to help to overcome the well known retransmissions competition problem that arises when a satellite reliable link layer protocol is used to face satellite link errors. This protocol, called Local IPSec-aware Freezing proTocol (LIFT), has been designed to allow the satellite gateway, even in the presence of communications secured by IPSec, to freeze the TCP sender when it perceives a possible delay due to satellite channel conditions. The effectiveness of LIFT has been evaluated, using the ns-2 tool, in terms of Web page download mean time for a satellite mobile host. Simulation results have shown that the adoption of LIFT protocol provides substantial improvements in TCP performance.

Energy efficiency represents one of the primary challenges in the development of wireless sensor networks (WSNs). Since communication is the most power consuming operation for a node, many current energy-efficient protocols are based on duty cycling mechanisms. However, most of these solutions are expensive from both the computational and the memory resources point of view and; therefore, they result in being hardly implementable on resources constrained devices, such as sensor nodes. This suggests to combine new communication protocols with hardware solutions able to further reduce the nodes' power consumption. In this work, a cross-layer solution, based on the combined use of a duty-cycling protocol and a new kind of active wake-up circuit, is presented and validated by using a test bed approach. The resulting solution significantly reduces idle listening periods by awakening the node only when a communication is detected. Specifically, an MAC scheduler manages the awakenings of a commercial power detector connected to the sensor node, and, if an actual communication is detected, it enables the radio transceiver. The effectiveness of the proposed cross-layer protocol has been thoroughly evaluated by means of tests carried out in an outdoor environment.

Radio frequency identification (RFID) technology is more and more adopted in a wide range of applicative scenarios. In many cases, such as the tracking of small-size living animals for behaviour analysis purposes, the straightforward use of commercial solutions does not ensure adequate performance. Consequently, both RFID hardware and the control software should be tailored for the particular application. In this work, a novel RFID-based approach enabling an effective localization and tracking of small-sized laboratory animals is proposed. It is mainly based on a UHF Near Field RFID multiantenna system, to be placed under the animals’ cage, and able to rigorously identify the NF RFID tags implanted in laboratory animals (e.g., mice). Once the requirements of the reader antenna have been individuated, the antenna system has been designed and realized. Moreover, an algorithm based on the measured Received Signal Strength Indication (RSSI) aiming at removing potential ambiguities in data captured by the multiantenna system has been developed and integrated. The animal tracking system has been largely tested on phantom mice in order to verify its ability to precisely localize each subject and to reconstruct its path. The achieved and discussed results demonstrate the effectiveness of the proposed tracking system.

The paper presents an innovative gapless traceability system able to improve the main business processes of the fresh vegetables supply chain. The performed analysis highlighted some critical aspects in the management of the whole supply chain, from the land to the table of the end consumer, and allowed us to reengineer the most important processes. In particular, the first steps of the supply chain, which include cultivation in greenhouses and manufacturing of packaged vegetables, were analyzed. The re-engineered model was designed by exploiting the potentialities derived from the combined use of innovative Radio Frequency technologies, such as RFID and NFC, and important international standards, such as EPCglobal. The proposed tracing and tracking system allows the end consumer to know the complete history of the purchased product. Furthermore, in order to evaluate the potential benefits of the reengineered processes in a real supply chain, a pilot project was implemented in an Italian food company, which produces ready-to-eat vegetables, known as IV gamma products. Finally, some important metrics have been chosen to carry out the analysis of the potential benefits derived from the use of the re-engineered model.