Distribution of entanglement is an essential task in quantum information processing and the realization of quantum networks. In our work, we theoretically investigate the scenario where a central source prepares anN-partite entangled state and transmits each entangled subsystem to one ofNreceivers through noisy quantum channels. The receivers are then able to perform local operations assisted by unlimited classical communication to distill target entangled states from the noisy channel output. In this operational context, we define the EPR distribution capacity and the GHZ distribution capacity of a quantum channel as the largest rates at which Einstein-Podolsky-Rosen (EPR) states and Greenberger-Horne-Zeilinger (GHZ) states can be faithfully distributed through the channel, respectively. We establish lower and upper bounds on the EPR distribution capacity by connecting it with the task of assisted entanglement distillation. We also construct an explicit protocol consisting of a combination of a quantum communication code and a classical-post-processing-assisted entanglement generation code, which yields a simple achievable lower bound for generic channels. As applications of these results, we give an exact expression for the EPR distribution capacity over two erasure channels and bounds on the EPR distribution capacity over two generalized amplitude damping channels. We also bound the GHZ distribution capacity, which results in an exact characterization of the GHZ distribution capacity when the most noisy channel is a dephasing channel.

This paper explores how high-throughput phenotyping can be integrated with machine learning models to efficiently forecast the Sterility Mosaic Disease using a small amount of training data. This approach is generalized through the use of a Dynamic Bayesian Network (DBN). To predict the spread of the virus, the entire network is decomposed into several distributed and cooperative learning modules. The EM algorithm is used to learn the parameters for each module. Upon iterative convergence, the estimated hidden state vector of one module serves as input control for the next. The parameter estimates of the final module are used to formulate a predictor capable of forecasting q-days ahead.To demonstrate the effectiveness of the proposed DBN, its performance is evaluated using real-world data from ICRISAT, Patancheru, Hyderabad, Telangana, India. Physiological data was collected using 3D point cloud technology, while environmental data was recorded by a local weather station.

Our research proposes a novel methodology for the design of immersive learning environments that prioritize accessibility and inclusion of adults with various disabilities. In contrast to existing hyper-specialized approaches that lack generalizability, it integrates Universal Design for Learning and participatory design to balance scalability and personalization. The iterative process promotes adaptable and universally accessible solutions through multidisciplinary collaboration and empirical validation. Preliminary results confirm the potential of Extended Reality to overcome pedagogical, spatial and social barriers and underline the need for standardized, flexible design guidelines.

While access to quality health services is a basic human right, it is not guaranteed for everyone, especially for people with neurodevelopmental disorder such as Autism Spectrum Disorder (ASD). For people with ASD, emotions can play an important role in the success of therapy. Severe anxiety before and during visits to specialists often leads to behavioral problems and poor cooperation, which can make treatment impossible. Using digital tools to support people with ASD in their daily lives can help manage anxiety and stress when accessing healthcare paths. However, the adaptation of these tools to ASD is still very rare. In this context, within the framework of the ACCESS project, we aim at developing a technological solution for personalized access to healthcare for individuals with ASD (including users of any age), and to demonstrate the developed methodology in the use case of the specialised Ear, Nose, and Throat (ENT) treatments for people with ASD. The main challenges of this study is the development of a system that can support people with ASD through customizable features tailored to their needs. We will conduct two experimental studies to validate the effectiveness and usability of our solution.

In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.

The Internet of Things (IoT) is increasingly composed of numerous sensing devices with limited power availability, which rely on energy storage solutions such as batteries or supercapacitors to sustain their operation. To ensure a reliable power supply for continuous functioning and energy replenishment, energy harvesting systems have been proposed. However, energy harvesting is not without limitations, including high equipment costs and limited feasibility in certain environments. This paper investigates a star-connected IoT network, in which the central coordinator node is equipped with a solar energy harvesting system. The harvested energy is used not only to power the coordinator’s own operations but also to activate nearby end devices via Wireless Power Transmission (WPT). Based on the estimated amount of power each end device can receive, determined by its distance from the coordinator, we propose an algorithm to compute optimal task scheduling that maximizes overall quality while satisfying the energy neutrality condition. Simulation results demonstrate the feasibility of integrating energy harvesting with WPT to efficiently power IoT devices.

Structural Health Monitoring (SHM) is crucial for ensuring the safety and longevity of critical infrastructures. Traditional methods for crack detection and damage assessment are often labor intensive and time consuming, highlighting the need for advanced technologies to enhance efficiency and accuracy. This paper introduces a novel approach that integrates continual learning frameworks within multi-layer recurrent neural networks to improve parameter estimation in SHM applications. By deploying the Generalized Expectation Maximization algorithm, we address challenges associated with dynamic operational environments and inherent uncertainties in sensor data. Our methodology enables real-time monitoring and adaptive learning, allowing the model to continuously refine its predictions based on new data. We demonstrate its effectiveness in automating structural anomaly detection from accelerometer readings, significantly enhancing the reliability of damage assessment. First results indicate that our framework not only improves crack detection accuracy, but also facilitates timely interventions, contributing to a more sustainable infrastructure management.

People with Autism Spectrum Disorder (ASD) often face various barriers in accessing healthcare due to anxiety and fear. Our framework of the ACCESS project aims to develop a system that supports people with ASD during Ear, Nose, and Throat (ENT) examinations. By incorporating personalized serious games and information materials, we aim to reduce anxiety and improve cooperation. We have developed two applications, WithMe: Paziente and WithMe: Clinica. Compliance with the General Data Protection Regulation was a crucial aspect of the development process. In this paper, we explain the methods and design solutions that we used to develop our system, ensuring both ease of use and data protection.

Distribution of entanglement is an essential task in quantum information processing and the realization of quantum networks. In our work, we theoretically investigate the scenario where a central source prepares an N-partite entangled state and transmits each entangled subsystem to one of$N$receivers through noisy quantum channels. The receivers are then able to perform local operations assisted by unlimited classical communication to distill target entangled states from the noisy channel output. In this operational context, we define the EPR distribution capacity and the GHZ distribution capacity of a quantum channel as the largest rates at which Einstein-Podolsky-Rosen (EPR) states and Greenberger-Horne-Zeilinger (GHZ) states can be faithfully distributed through the channel, respectively. We establish lower and upper bounds on the EPR distribution capacity by connecting it with the task of assisted entanglement distillation. We also construct an explicit protocol consisting of a combination of a quantum communication code and a classical-post-processing-assisted entanglement generation code, which yields a simple achievable lower bound for generic channels. As applications of these results, we give an exact expression for the EPR distribution capacity over two erasure channels and bounds on the EPR distribution capacity over two generalized amplitude damping channels. We also bound the GHZ distribution capacity, which results in an exact characterization of the GHZ distribution capacity when the most noisy channel is a dephasing channel.

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space.Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities.We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy.To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker’s profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.

This study addresses the need for a rapid and accurate process monitoring by developing an innovative approach for wastewater quality assessment to enhance the Industry 4.0’s vision. By integrating Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning (ML), we focus on accurately determine key indicators such as Chemical Oxygen Demand, Total Suspended Solids (TSS), chlorides, and conductivity. Our findings demonstrate the efficacy of ML models in accurately predicting water quality from UV-Vis spectral data, underscoring their potential for real-time monitoring and analysis in industrial settings. The study also revealed the potential for both single and multitarget predictions. Additionally, the feature importance analysis provided valuable insights into the spectral regions most relevant for predicting each water quality indicator. This approach aligns with the goals of Industry 4.0, offering a smart, efficient solution for environmental monitoring and sustainable resource management.

In the Internet of Things/Fog interplay we define a novel problem, namely Max Sustainable Scheduling, and propose an algorithm for its solution. This problem arises when an IoT device and a Fog node stipulate a service-level agreement that limits the computational power that the Fog node devotes to serve the IoT device and that defines the corresponding costs, which are dependent on the actual amount of computational power required and on the associated energy costs for the Fog node. As the cost of energy is variable over time, this agreement is an incentive for the IoT device to schedule its activities so to modulate the implied load on the Fog node, in order to take advantage of the variable costs of the energy and to maximize the overall quality of its output. We discuss the pragmatics of the solver and we propose an efficient algorithm to solve the problem on the IoT device itself.

In many applications Internet of Things (IoT) supports decision taking on the base of continuous data acquisition. These data, usually streams of sensed data, are processed and analysed to produce high-level information. The latter task is usually achieved by means of artificial intelligence technologies. Among these, continual learning is emerging as a paradigm that combines well with IoT as it matches the ability of IoT to continuously produce new data. In this context, we address continual learning with Recurrent Neural Networks (RNN) under a stochastic perspective, in which we consider the RNN as a stationary state-space network. This led us to deploy the Generalized Expectation-Maximization algorithm, in a setting suitable for IoT. We demonstrate the effectiveness of our approach by considering a case study taken from digital agriculture, in which we adopt the continual learning model to assess the biomass prediction in the field of horticulture using IoT technology. Results demonstrate that RNNs embedded in the EM framework can learn on their own after a very short training phase covering a few time samples.

Progress in Information and Communication Technologies (ICT) can make a real difference in the quality-of-life of persons with Autism Spectrum Disorder (ASD) by acting on several aspects, from customized software for communication, to emotion recognition, to social behavior and also to provide systems for the observation of the wide spectrum of manifestations, to ease the diagnosis, to support the therapy, and to monitor the improvement and the growth of children with ASD. This has been achieved by the introduction of a large number of innovative technologies, spanning from Internet of Things, to robotics, virtual and augmented reality, etc. Differently from other surveys on the same research area, we focus this survey on innovative technologies used in this field, and we organize a classification of the papers based on three different but strictly crossed axis, namely the triad of impairment (either communication, social interaction, or social behaviors), research purpose (either diagnosis or therapy), and system activity (either monitoring or intervention).

Proximity detection is the process of estimating the closeness between a target and a point of interest, and it can be estimated with different technologies and techniques. In this paper we focus on how detecting proximity between people with a TinyML-based approach. We analyze RSS values (Received Signal Strength) estimated by a micro-controller and propagated by Bluetooth’s tags. To this purpose, we collect a dataset of Bluetooth RSS signals by considering different postures of the involved people. The dataset is adopted to train and test two neural networks: a fully-connected and an LSTM model that we compress to be executed directly on-board of the micro-controller. Experimental results conducted over the dataset show an average precision and recall metrics of 0.8 with both of the models, and with an inference time less than 1 ms.

In the context of a 5G and beyond network operating in a smart city, in which the fixed network infrastructure is supported by a flock of unmanned aerial vehicles (UAV) operating as carriers of Virtual Network Functions (VNF), we propose a Mixed Integer Linear Programming (MILP) model to place chains of VNFs on a hybrid UAV-terrestrial infrastructure so to maximize the UAV lifetime while considering resource constraints and by taking into account the network traffic originated by crowds of people assembling in the city at given hotpoints. We formalize the UAV deployment problem and we test our solution with a practical scenario based on DoS detection system. The experimental results assess the deployment in a practical scenario of a DoS detection system and show that the proposed solution can effectively enhance the capability of the system to process the input flows under a DoS attack.

The Mobile Edge Computing paradigm shifts the computation back to places where it is required. A traditional MEC architecture comprises a number of Edge Data Centers (EDC) in charge of seamlessly providing services to users with wireless network technologies. In this scenario, it becomes crucial to deploy the EDCs in strategic locations, such as highly visited places. In this paper we focus on the deployment phase of an EDC. In particular, we propose a probabilistic model designed to measure the location converge, namely the probability that a candidate location for an EDC is visited by users. Our model is based on the analysis of user’s trajectories and on the probability of detouring towards the target locations for the EDS. The information returned by our model offers the possibility of implementing mobility-aware deployment strategies in urban environments. We test the model with two real-world mobility data sets, evaluating its applicability of realistic settings.

Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different tasks characterized by different energy costs (and quality), depending on the current energy production of the energy harvesting subsystem and on the residual battery charge. In this context, we propose a novel scheduling problem that aims at keeping the energy neutrality of the scheduling while maximizing the overall quality of the executed tasks and minimizing the leaps of quality among consecutive tasks, so to improve the stability of the output of the device itself. We propose for this problem an algorithm based on a dynamic programming approach that can be executed even on low-power devices. By simulation we show that, with respect to the state of the art, the scheduling by our algorithm greatly improve the stability of the device with a minor penalty in terms of overall quality.

Internet of Things (IoT) has become omnipresent in many applications, such as healthcare, vehicles, and precision farming. They sense data from dozens of sensors scheduled periodically in a synchronous fashion on mobile CPUs that are forwarded to the cloud or other IoT devices via an essentially stochastic wireless channel. Hence, the task response time becomes stochastic, preventing optimization at compile time. On the other hand, knowing response time at compile time along with jitter, availability, and scalability is crucial to ensure a certain level of Quality of Service. This contribution presents a stochastic framework for performance analyses of multiapplications on a possible multiprocessor platform. When annotated with (stochastic) execution time, a traditional synchronous dataflow (SDF) graph can be transformed into a directed acyclic workflow graph, revealing the timing of individual actors. A generalized version of the rejection sampling Monte Carlo algorithm explores the properties of the workflow graph, to determine the distribution of the response time in a single application as well as a multiapplication multiple access scenario. Mean and jitter are the moments of the distribution. An IoT toy example with a number of distributed smart sensors was deployed in real environments to assess the performance of the proposed framework. Our analysis framework works at compile time of the code, scales with the number of things, and has low computational complexity.

We address the problem of energy neutrality in energy harvesting IoT devices by means of a model checking approach, aiming at analyzing the dynamics of the battery charge in energy-neutral IoT devices. Our approach allows to compute the best task schedule and to study the maximum utility when operating on other parameters such as the initial battery charge, the number and structure of the available tasks, the size of the photo-voltaic panel that recharges the device, the day of the year, and the variable weather conditions that affect the energy production. The simulations confirm the state space explosion typical of model checking, but also hint that a small number of alternative tasks can achieve an overall utility very close to a large number of tasks. This conjecture has a strong practical relevance since it can pave the way to the wider adoption of energy neutrality concept in low-power IoT devices.

Unmanned aerial vehicles (UAVs) are autonomous devices employed as data collectors in precision agriculture to support a large number of applications. These UAVs gather data from on-the-ground wireless sensor networks, especially in scenarios that lack any kind of fixed communication infrastructure or where the available infrastructure does not fit the application requirements. Sensors on the ground can store sensing data, and in scenarios that do not require a real-time observation and analysis of the data, like in smart farming, a drone can be used maybe once or two times each day to collect and report the data to a command-and-control center that use them directly, without any other infrastructure (cloud or edges). In this article, we study analytically how close the drone, that uses a LoRa radio, needs to fly over the sensors to collect data with a given quality of data collection. This can be used to properly spacing the sensors on the field at deployment time, to select among different type of drones, and to properly solve some tradeoff related to field size vs autonomy of the drones and the path used by the latter when collecting the data.

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.

Human-enabled edge computing (HEC) is a recent smart city technology designed to combine the advantages of massive mobile crowdsensing (MCS) techniques with the potential of multiaccess edge computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this article, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.

The Internet of Things (IoT) paradigm poses a great variety of application domains where million of devices work uninterruptedly to improve some aspect of our lives. To support the continuous execution of the applications working on the devices, energy harvesting systems enable to extract the energy found naturally in the environment (for instance from the sun or from the wind) and convert it into energy able to either sustain the device's operation and recharge its batteries which, in conjunction with an appropriate scheduling strategy, led to the device to an electrically sustainable state (i.e. an energy-neutral state). Most of the works found in literature oriented to achieve energy neutrality are however evaluated by means of simulation which means that, in spite of precisely modeling hardware features and energy productions, lack of the realism that we find in a real deployment. A minor part of the works are based on a real deployment but do not share the collected data that permit to replicate the analysis. With this purpose in mind, in this article we describe a testbed designed for outdoor monitoring purposes in the IoT context, equipped with several sensors for weather conditions monitoring and with a solar panel to provide application lifetimes potentially infinite. The testbed was deployed on the roof of a building and it executed uninterruptedly an application able to generate a dataset with the collected information over a period of more than two months. This dataset has been online published to be used for different researching purposes, as for instance, prediction models of the energy production.

The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.

To make sustainable large IoT deployments in smart cities, a promising approach is to develop a new generation of solar energy harvesting IoT devices based on the concept of energy neutrality. Key to this concept are the models for the forecast of energy production, which provide input to the energy-neutral schedulers governing the activities of the IoT devices. The development of such models however need to be validated against real-world conditions. To this purpose we propose a testbed aimed at the collection of real-world dataset about the energy parameters of energy harvesting IoT devices, and, on the base of such a dataset, we perform a comparative assessment of state of the art and novel energy production forecast models.

Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.

Mobile Crowd-Sensing and Fog Computing are fundamental Internet of Things technologies tailored for smart cities. The former enables user's devices to collect and share data in urban environments. The latter shifts the computation close to end users, lightening the work that their devices have to perform to communicate sensed data in the Cloud. In a fog-based MCS campaign a large number of devices with heterogeneous resources executes sensing tasks generally distributed by remote servers. A careful selection of some of these users' devices for sensing operations can bring benefits to the whole platform in terms of computational costs and energy saving. In this paper, we propose a novel users' recruitment model based on distance, computational capacity, and residual battery of devices. The selection process is carried out in a scenario where devices of the MCS campaign periodically share their battery and Central Processing Unit status to fog nodes through their short-range communication interfaces. Based on this information, fog nodes select devices suitable for performing specific tasks. To verify the effectiveness of the proposed model, we compare our solution with a selection model based only on distances, using an MCS simulator suitably modified for fog-based scenarios as testbed. Results show that our model is able to achieve a more accurate task resolution and a more effective recruitment selection, detecting those devices that can perform sensing operations better than others, thus, guaranteeing an overall average saving of computational and energy resources.

In an increasingly interconnected world, mobile and wearable devices, through short range communication interfaces and sensors, become needful tools for collecting and disseminating information in high population density environments. In this context Mobile Crowdsensing (MCS), leveraging people's roaming and their devices' resources, raised the citizen from mere walk-on parts to active participant in the knowledge building and data dissemination process. At the same time, Mobile Edge Computing (MEC) architecture has recently enhanced the two-layer cloud-device architectural model easing the exchange of information and shifting most computational cost from devices towards middle-layer proxies, namely, network edges. We introduce Human-driven Edge Computing, a new model which melts together the power of MEC platform and the large-scale sensing of MCS to realize a better data spreading and environmental coverage in smart cities. In addition, it will be briefly discussed the main sociological aspects related to human behavior and how they can influence the exchange of data in large-scale sensor networks.

The increasing availability of sensors and intelligent objects enables new functionalities and services. In the Ambient Assisted Living (AAL) domain, such technologies can be used for monitoring and reasoning about the older people behavior to detect possible anomalous situations, which could be a sign of the next onset of chronic illness or initial physical and cognitive decline. We propose an approach to detecting abnormal behavior by developing a profiling strategy (in which task models specify the normal behavior), which can also work in case of rare anomaly data. Events corresponding to the user behavior is detecting by a middleware software(Context Manager). Afterward, our algorithm compares the planned and actual behavior to identify if any deviation occurred and also defines to which category the anomaly belongs. The resulting environment should be able to generate multi-modal actions (i.e alarms, reminders) based on detected anomalous behavior, aiming to provide useful support to improve older people well-being.

Mobile Edge Computing (MEC) opens to the opportunity of moving high-volumes of data from the cloud to locations where the information is actually accessed. In turn, the combination of MEC with the Mobile Crowdsensing approach, using a restricted number of devices with respect the number of base stations, matches the performance of the conventional MEC middleware layer ensuring the same spatial coverage. In this work, we envision a MEC architecture composed by mobile and fixed edges. Their goal is to optimize the share of contents among users by exploiting their mobility and sociality. We first present an algorithm to identify a suitable set of mobile edges and we show how such selection increases the performance of a content-sharing scenario. Our experiments are based on the ParticipAct dataset, which captures the mobility of about 170 users for 10 months. The experiments show that the number of requests that can be served mobile edges is similar to that of requests served by fixed edges, and then that mobile edges can be considered a viable (and lowcost) alternative to fixed edges.

Scheduling tasks appropriately in an IoT device powered by multiple energy-harvesting sources is a challenging problem. In this paper, we model this problem, and we present a scheduling algorithm that optimally sets the overall node power consumption based on the utility, and on the energy required by tasks. The algorithm schedules high-level tasks, it uses the weather forecast informations available at the beginning of each scheduling period (typically a day), and the level of the battery, to define an optimal schedule. The main goal is to find a schedule that is energy neutral on average, over a period longer than the single scheduling window, for example a week. We test our scheduler on a simulated platform with the same specs of an Arduino node, equipped with a small (portable) solar panel, and attached to a small wind turbine. We see from the simulations that the scheduler performs as expected and that the utility of the scheduling improves as the error between the expected forecast and the real harvested energy is reduced.

Outdoor Internet of Things (IoT) applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the problem is NP-hard and that the algorithm finds the optimum solution in a pseudo-polynomial time. Furthermore, we show that the algorithm can be executed with a small overhead on three popular IoT platforms (namely TMote, Raspberry PI, and Arduino) and, by simulation, we show the behavior of the algorithm with different settings and at different conditions of energy production.

With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.

Some recent research projects, inspired by the widespread availability of sensor-provided smartphones, have built harvesting experiments to collect large quantities of data in urban areas. These efforts produced new real-world datasets, typically focusing on different technological aspects (GPS and Bluetooth mobility traces or WiFi indicators) and, more recently, also on user-related data, from low-level accelerometer samples to higher-level social networking data. At the same time, Mobile Crowd Sensing (MCS) blossomed with a few very recent project, with the goal to efficiently coordinate user participation, both to collect sensor data and to allow active collaboration in participatory tasks. This paper aims to shed some light and to propose new research directions on the MCS by employing the notable results already obtained in the Mobile Social Network area to the study of human dynamics. The reported results, comparing three MCS datasets available in the literature, lead to an in-depth discussion of some lessons we learned about sociotechnical management aspects of MCS. The results we present are valuable for the MCS community to design new MCS campaigns and to refine the whole MCS process to the purpose of better efficiency and scalability.

The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.

The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project.

Mobile Crowd Sensing (MCS) is an emerging paradigm that exploits the ubiquity of smartphones and cheap sensor devices to collect data and thus contribute to the provision of useful services, especially in the domains of urban life. While many MCS implementations have been proposed for different applications, the lack of common performance metrics means that their efficiency cannot be easily compared. In this paper, we formalize a generic coverage model for the class of MCS systems sampling spatial phenomena before introducing a way to produce one such a metric by exploiting a spatio-temporal estimator. We avail of a large-scale dataset of users' mobility traces to demonstrate the use of the newly introduced metric in informing the resolution of a typical problem in MCS system design.

The paper proposes a narrowband stochastic system model for auditory signal processing. The parameters are ipsilateral, contralateral and interaural hearing losses, false positive and false negative responses, and patient response time. The auditory model is then used to realize a patient simulator (artificial patient), comprising out of two microphones, a skull simulator, sound cards and a noiseless personal computer. A locally stored database contains the simulated and the recorded patient data. First field trials in an audiometric test room at the University Medical Center, Utrecht, The Netherlands, indicate that the artificial patient resembles the behavior of a real patient within a band of 10 dB-HL over the entire audiometric frequency range.

Underwater communications through acoustic modems rise several networking challenges for the Underwater Acoustic Sensor Networks (UASN). In particular, opportunistic routing is a novel but promising technique that can remarkably increase the reliability of the UASN, but its use in this context requires studies on the nature of mobility in UASN. Our goal is to study a real-world mobility dataset obtained from the Argo project. In particular, we observe the mobility of 51 free-drifting floats deployed on the Mediterranean Sea for approximately one year and we analyze some important properties of the underwater network we built. Specifically, we analyze the contact-time, inter-contact time as well density and network degree while varying the connectivity degree of the whole dataset. We then consider three known routing algorithms, namely Epidemic, PROPHET and Direct Delivery, with the goal of measuring their performance in real conditions for USAN. We finally discuss the opportunities arising from the adoption of opportunistic routing in UASN showing that, even in a very sparse and strongly disconnected network, it is still possible to build a limited but working networking framework.

Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.

Mobile Crowd Sensing (MCS) aims to coordinate and activate the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. One of the most important requirements in MCS is maximizing the effectiveness of the data gathering campaign. In fact, also due to the initial low penetration rate of MCS apps and to avoid making the MCS process cumbersome to users, only a small portion of the whole citizenship can be involved in such campaign, while most citizens are not part of the process. This paper proposes a novel approach that combines participatory and opportunistic techniques to amplify the amount of data harvested from the crowd. The core idea is that people with similar interests, such as employees of the same company, students or friends tend to meet more frequently with respect to people with different interests. Accordingly, it is possible to opportunistically involve in the crowd sensing loop people in the volunteers' neighborhood. Following that main design guideline, our work assesses the SOcial amplification FActor (SOFA) that allows to increase the number of samples retrievable during a crowd sensing campaign. We show the benefits of SOFA by using the ParticipAct MCS platform and we analyze three different application scenarios. Highly realistic simulation results, based on ParticipAct mobility traces, show the advantages of SOFA, with an amplification factor increasing up to 4.45.

CORDIAL is a collaborative service discovery strategy designed for mobile and opportunistic networks, which takes advantage of some features of human behavior, namely the periodicity of movements, the membership to a restricted number of communities and the sharing of interests among members of a community. CORDIAL exploits these features by adapting the strategies of query and service advertisements by exploiting the cooperation of nodes with higher social centrality in order to improve the chance of finding the desired service. The evaluation of CORDIAL by simulation shows that, as compared with similar approaches, it improves proactivity and accuracy levels, and it reaches performance comparable to the flooding technique but at a much lower cost.

The realization of smart cities relies on the availability of large amount of data about occurring phenomena/events that can be guaranteed by very large deployments of Wireless Sensor Networks (WSN). This poses a great challenge to the scalability of current routing protocols for WSN due to the size and density of the network and the presence of mobile sensors.We tackle this problem by proposing a solution based on virtual coordinate systems combined with mechanisms that renew the virtual coordinates and suitable routing schemes. The simulation results show that this approach is actually suited to this context and that it guarantees high delivery rate and low path length.

The problem of providing assistive services to elderly in smart cities is becoming important due to the aging of population in the developed countries. The possibility of using personal devices like smartphones to be assisted also outside the house is a key factor to guarantee the independency of elderly users still remaining connected to his caregivers network. We identified in the development of a suitable mobile middleware one of the main solution to the barrier in the deployment of distributed AAL services. In this scenario, we show the effectiveness of the mobile middleware solution proposed, called GiraffPlus-mobile (GP-m), in terms of integration with the existing pervasive environment, performances and energy saving.

This paper analyzes the behavior of the MAC layer in applications using stencil skeletons in Visual Sensor Networks (VSN) applications. Specifically, we analyze the communication pattern required by a specific skeleton (INS) and simulate its behavior using a selection current MAC layers available on VSN. We analyze both the energy required by different MACs and their ability to reach low latencies needed for real-time applications. The results show that, using energy efficient protocols like T-MAC, it is possible to reach a good tradeoff between performance and energy. To reach this result, however, T-MAC parameters (frame size, contention time and timeout) need to be carefully tuned exploiting the characteristics of the pattern.

We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membership to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossiping. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossiping-based solutions.

Solar cells combined with power management algorithms enable the dynamic scheduling of Wireless Sensor Networks applications in a reference period, where the objective of the scheduling is to maximize the application quality level while conserving an energy level sufficient to constantly maintain the sensor operation. In this paper we consider networked, solar cells powered wireless sensors and we propose an algorithm aims to find a global, (sub)optimal scheduling that maximizes the overall quality of service in the sensors and keeps the system energy neutral, thus ensuring that the system works uninterruptedly.

Due to limitations in sensors' hardware and communication, routing in Wireless Sensor Networks (WSN) has required different approaches as compared to more powerful and conventional networks. One successful approach to the routing problem in WSN is based on geographic protocols that, however, must rely on coordinates and that are limited to two-dimensional networks. We propose here an approach that guarantees packet delivery in networks of any dimensionality. This approach combines a recursive coordinate assignment protocol based on the network topology and a specific routing protocol. Both protocols are simple, and the path lengths produced by the routing protocol are slightly larger than the shortest paths.

Sensors equipped with solar cells and rechargeable batteries are useful in many outdoor, long-lasting applications. In these sensors the cycles of energy harvesting and battery recharge need to be managed appropriately in order to avoid sensor unavailability due to energy shortages. We suggest that adapting the sensor duty cycle (and specifically its environment sampling frequency) to the expected energy production and residual battery charge is very useful to avoid sensors unavailability. To this purpose we introduce a novel concept of QoS, which is measured in terms of sampling frequency, and we provide a mean to maximize the QoS, i.e. the extent of the period in which the sensor operates at the user's desired sampling frequency.

We consider a problem where a physical quantity is repeatedly measured by replicated devices, yielding a stream of numerical data. Data are stored within the measuring devices and sporadically retrieved by a user. To avoid data losses due to large data streams with insufficient memory, the data are split into fragments, each of which is a compressed encoding of a number in the stream, and different fragments are stored in different, replicated devices. The devices are not allowed to communicate with each other, and they produce the local streams of fragments from independent measurements. Given the independence of measurements, the fragments are corrupted by independent errors, which are likely to be small integers, although errors of unbounded magnitude may also occur due to failures or to interferences. As devices may fail, or communication may be unreliable, the user may be unable to download fragments from some of the replicated devices, leading to fragment erasures. Our approach to the problem is to encode the data in a Residue Number System with Nonpairwise-Prime Moduli, named D-RNS-NPM. Withnmoduli andnresidue digits, every replicated device is tied to a different modulus, with which it produces and stores a residue digit (i.e., a fragment) from the local measurement. Assuming an upper boundz, withzn, to the number of erasures, we show that the D-RNS-NPM guarantees the reconstruction of any number from a subset of at leastn-zfragments. If fragments bear errors, whose magnitude is unrestricted for at most one error and upper bounded by a small δ for the others, reconstruction is within an approximation of ±δ, and this property is retained when errors cannot be detected due to the unbounded error multiplicity. The time complexity of the decoding algorithm is polynomial. This problem appears to be relevant in wireless sensor networks, and an application in this area is envisioned.

ABSTRACT The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI‐based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd.

One of the most promising use of Wireless Sensor Networks is in the field of the event warning applications. However, depending on the communication scheme adopted, the amount of the exchanged data may be huge, with large energy consumption. In this paper we study the efficiency of two approaches to composite event detection in Wireless Sensor Networks. The first solution approaches the problem by collecting all the available information in a single sensor that performs the real event detection. If the area covered by the network is wide this approach may result inefficient. In the second solution, the sensors may exchange among themselves the sensed data and execute locally the event detection, with the advantage of propagating the sensed data only in a smaller area.

RSS-based indoor localization systems are widely accepted in the literature as one of the less invasive localization technique. In fact, this range-based approach does not require any special hardware and is available in most standard wireless devices. Furthermore, judicious use of RSS has not a significant impact on local power consumption, sensor size, and cost. In front of these interesting characteristics, the performance of the RSS approach is worst with respect to some more invasive ad hoc hardware range-based solutions (such as Angle of Arrival, Time of Arrival etc…). In this paper we propose a localization method that leveraging the context information, such as the knowledge of being in a given room, increases the localization accuracy of RSS-based methods. Performance evaluation is done via real measurements in an office environment composed of three adjacent rooms.

MAC protocols for Wireless Sensor Networks based on channel checking to detect incoming packets (such as Low Power Listening, LPL) require some form of synchronization among sensors, in order to schedule the times in which the sender and the receiver should turn on their radios, and to ensure correct packet delivery. However, while travelling along multihop paths, packets may accumulate delays that force the sensors either to incur packet losses and retransmissions or to readjust the planned scheduling. In both cases this fact impacts on the energy budget of the sensors. In this paper we propose a cross-layer optimization for MAC protocols that use LPL. This optimization takes into account high-level information of the application in order to compute adaptive delays in every sensor along a multihop path, with the goal of adjusting precisely the activity time window of the sensor along the path. We validate our delay-based model by evaluating different scenarios, and we compare it against the LPL model. The simulation results confirm the validity of our approach and demonstrate that a delay-based model can improve the synchronization achieved through the LPL strategy.

We present a novel video stream authentication scheme which combines signature amortization by means of hash chains and an advanced watermarking technique. We propose a new hash chain construction, the Duplex Hash Chain, which allows us to achieve bit-by-bit authentication that is robust to low bit error rates. This construction is well suited for wireless broadcast communications characterized by low packet losses such as in satellite networks. Moreover, neither hardware upgrades nor specific end-user equipment are needed to enjoy the authentication services. The computation overhead experienced on the receiver only sums to two hashes per block of pictures and one digital signature verification for the whole received stream. This overhead introduces a provably negligible decrease in video quality. A thorough analysis of the proposed solution is provided in conjunction with extensive simulations.

In-network storage of data in Wireless Sensor Networks (WSNs) is considered a promising alternative to external storage since it contributes to reduce the communication overhead inside the network. Recent approaches to data storage rely on Geographic Hash Tables (GHT) for efficient data storage and retrieval. These approaches, however, assume that sensors are uniformly distributed in the sensor field, which is seldom true in real applications. Also they do not allow tuning the redundancy level in the storage according to the importance of the data to be stored. To deal with these issues, we propose an approach based on two mechanisms. The first is aimed at estimating the real network distribution. The second exploits data dispersal method based on the estimated network distribution. Experiments through simulation show that our approach approximates quite closely the real distribution of sensors and that our dispersal protocol sensibly reduces data losses due to unbalanced data load.

Data Centric Storage is a recent paradigm that results more efficient than other storage techniques (such as local or external storage) in Wireless Sensor Networks (WSN). In this work we consider the use of this storage paradigm in WSN based on the ZigBee standard. In particular we propose a novel protocol (Z-DaSt) that exploits the routing and addressing features of ZigBee (that are based on routing trees) to distribute data to the sensors. The protocol also features Quality of Service in the storage of data by enabling the user to specify the amount of redundancy to be used in the storage of each datum. This work evaluates Z-DaSt by analysis and simulation, and compares its performance to DCS-GHT. The simulation results show that Z-DaSt is a viable alternative to DCS-GHT for practical cases, in particular for low to moderate network densities.

In a Wireless Sensor Network (WSN) where the sensors monitor the environment to the purpose of raising alarms about the occurrence of some events, we consider the problem of detecting false alarms (i.e. false positives). We adopt a system level diagnosys model, where all the sensors in the region where the alarms have been raised cooperate to execute mutual tests. The test outcomes are given as input to a diagnosis algorithm that identifies the faulty sensors and thus confirms or neglects the alarm. The diagnosis algorithm can succeed in this work if the tests among sensors are sufficient to achieve a given diagnosability of the system, that depends on topological properties of the WSN. This work considers the problem of determining a test strategy of the sensors in a WSN in order to ensure that the desired system diagnosability is met. In particular it is shown by simulation that appropriate testing strategies are successful to guarantee given levels of system diagnosabilities depending of the network topology or its density.

Current approaches to the query of wireless sensor networks address specific sources such as individual sensors or transducers. We believe that it is important to have a higher level mechanism of abstraction for querying a sensor network. In this work we aim at querying complex events, where such an event is modeled as a condition computed over a complex aggregate of sensed data. When the condition becomes true then the event is detected and tracked. In this paper we present a model for detecting and tracking such complex events in a WSN and we propose a declarative language for the event definition and for the detection and tracking specification and we also discuss its implementation guidelines.

Wireless sensor networks (WSN) are composed of several sensors having limited memory, processing power, communication bandwidth, and energy, which cooperate in performing a given task. The use of the database paradigm has emerged in the last few years as a viable solution to manage data in such a context. In this paper we present the MaD-WiSe system, a distributed query processing framework that moves the processing of the query into the network. MaD-WiSe reconsiders various aspects related to database system design and it reinterprets them according to the WSN constraints and requirements. In particular it considers the aspects related to the definition of a query language to formalize the queries, a stream model to manage data acquired by the sensors, a query algebra to define the operators that actually perform the query, and energy efficiency and query optimization strategies for saving energy.

Localization systems based on Received Signal Strength Indicator (RSSI) exploit fingerprinting (based on extensive signal strength measurements) to calibrate the system parameters. This procedure is very expensive in terms of time as it relies on human operators. In this paper we propose a virtual calibration procedure which only exploits the measurements of the RSSI between pairs of anchors. In particular, we propose two procedures for virtual calibration and we evaluate their performance with respect to an ad-hoc calibration campaign by performing measures in an indoor environment with an IEEE 802.15.4 sensor network.

In-network storage of data in wireless sensor networks contributes to reduce the communications inside the network and to favor data aggregation. In this paper, we consider the use of erasure codes in combination to in-network storage. We provide an abstract model of in-network storage to show how erasure codes can be used, and we discuss how this can be achieved in two cases of study. We also define a model aimed at evaluating the probability of correct data encoding and decoding, and we exploit this model and simulations to show how the parameters of the erasure code and the network should be configured in order to achieve correct data coding and decoding with high probability even in presence of sensors faults.

We propose a novel localization algorithm of mobile sensors based on wireless sensor networks providing RSSI measurements between the mobile and the fixed sensors (anchors) in the network. The algorithm selects and weights the RSSI measurements according to their strength, and it uses a propagation model to transform RSSI measurements into distances, in order to estimate the position of the mobile. The algorithm also uses a virtual calibration method of the propagation model that does not require human intervention. By an experimental setup we show that the localization algorithm increases the performance with respect to the commonly used least mean square algorithm showing also how to achieve a wished accuracy increasing the anchor density.

Information assurance in critical infrastructure is an issue that has been addressed generally focusing on real-time or quasi real-time monitoring of the critical infrastructure; so that action could be undertaken when anomalies arise, to avoid more severe consequences to the infrastructure. In this paper, we relax the hypothesis of intervening when anomalies are detected: we focus on sensed data survivability. Specifically, we study this problem in a specific critical infrastructure: pipelines. The problem we introduce is how to place sensors in such a way that the sensed data related to the monitoring of the pipeline will survive even in presence of a partial destruction of the infrastructure. The contributions of this paper are twofold. First, we introduce the problem of sensed data survivability in critical infrastructure. In this framework, the goal is to have the sensed data to survive to the infrastructure failure, so that the phenomena that lead to the failure could be better understood and possibly tackled in similar deployment. Second, we provide a model that allows to produce an optimal network topology with respect to the level of information assurance desired, while satisfying deployment constraints, such as available bandwidth and available energy of the sensors. We believe that the work addressed in this paper could foster further research in the field of information assurance in critical infrastructure.

Current trends in smart homes suggest that several multimedia services will soon converge towards common standards and platforms. However this rapid evolution gives rise to several issues related to the management of a large number of multimedia streams in the home communication infrastructure. An issue of particular relevance is how a context acquisition system can be used to support the management of such a large number of streams with respect to the Quality of Service (QoS), to their adaptation to the available bandwidth or to the capacity of the involved devices, and to their migration and adaptation driven by the users’ needs that are implicitly or explicitly notified to the system. Under this scenario this paper describes the experience of the INTERMEDIA project in the exploitation of context information to support QoS, migration, and adaptation of multimedia streams.

SMEPP Light is a middleware for Wireless Sensor Networks (WSNs) based on mote-class sensors. It is derived from the specification developed under the framework of the SMEPP project, to deal with the hardware and software constraints ofWSNs. SMEPP Light features group management, grouplevel security

We consider the problem of routing with guaranteed delivery in wireless sensor networks where physical locations of the sensors are not known. We propose a naming protocol which defines a 2-dimensional coordinate system and a routing protocol (Ibrid) which ensures guaranteed delivery of packets. We show by means of simulation that in realistic settings where the network includes voids and obstacles, Ibrid finds routess hortest than those obtained with existing geographic routing algorithms over physical coordinates.

Storage of sensed data in wireless sensor networks is essential when the sink node is unavailable due to failure and/or disconnections, but it can also provide efficient access to sensed data to multiple sink nodes. Recent approaches to data storage rely on Geographic Hash Tables for efficient data storage and retrieval. These approaches however do not support different QoS levels for different classes of data as the programmer has no control on the level of redundancy of data (and thus on data dependability). Moreover, they result in a great unbalance in the storage usage in each sensor, even when sensors are uniformly distributed. This may cause serious data losses, waste energy and shorten the overall lifetime of the sensornet. In this paper, we propose a novel protocol, Q-NiGHT, which (1) provides a direct control on the level of QoS in the data dependability, and (2) uses a strategy similar to the rejection method to build a hash function which scatters data approximately with the same distribution as sensors. The benefits of Q-NiGHT are assessed through a detailed simulation experiment, also discussed in the paper. Results show its good performance on different sensors distributions on terms of both protocol costs and load balance between sensors. 1

Satellite link presents peculiar characteristics like no packet reordering and low bit error rate. In this paper we leverage these characteristics combined with watermarking techniques to propose a novel authentication algorithm for multicast video streaming. This algorithm combines a single digital signature with a hash chain pre-computed on the transmitter side; the hash chain is embedded in the video stream by means of a watermarking technique. Our proposal shows several interesting features: authentication is enforced, as well as integrity of the received multicast stream; received blocks can be authenticated on the fly; no storage is required on the receiver side, except for the amount of memory needed to store a single hash; overhead computations required on the receiver sum up to single hash per block, while a digital signature verification is amortized over the whole received stream. Finally, note that the bandwidth overhead introduced is negligible, since the applied watermarking technique introduces virtually no modifications (at least, not recognizable by humans) on the original video stream pictures.

We consider the problem of source authentication of video streams in multicast environments. We proposed a novel algorithm which combines signature amortization based on hash chains and watermarking to embed hash chains in the video stream. The algorithm exploits a novel watermarking procedure using Reed-Solomon mark encoding which ensures error-free and fast convergence mark extraction. We show that the algorithm has a negligible impact on the video stream quality and we evaluate the configuration parameters of the algorithm which ensure proper verification of the source authenticity.

System-level diagnosis aims at the identification of faulty units in a system by the analysis of the system syndrome, that is, the outcomes of a set of interunit tests. For any given syndrome, it is possible to produce a correct (although possibly incomplete) diagnosis of the system if the number of faults is below a syndrome-dependent bound and the degree of diagnosis completeness, that is, the number of correctly diagnosed units, is also dependent on the actual syndrome sigma. The worst-case diagnosis completeness is a syndrome-independent bound that represents the minimum number of units that the diagnosis algorithm correctly diagnoses for any syndrome. This paper provides a lower bound to the worst-case diagnosis completeness for regular graphs for which vertex- isoperimetric inequalities are known and it shows how this bound can be applied to toroidal grids. These results prove a previous hypothesis about the influence of two topological parameters of the diagnostic graph, that is, the bisection width and the diameter, on the degree of diagnosis completeness.

Sensor network software is still in its youth. Due to sensor hardware limitations and the highly specific nature of application domains, existing software is generally poorly structured. We identify data collection, intra-sensor and inter-sensor communication as recurring activities in sensor network applications and propose a software module that abstracts these activities: the stream system. Applications running on the sensors rely on the stream system to disregard the actual implementation details of collecting transducer readings and passing such data to other local or remote computational entities.

In this paper we consider the problem of constructing a coordinate system in a sensor network where location information is not available. To this purpose we introduce the virtual coordinate assignment protocol (VCap) which defines a virtual coordinate system based on hop distances. As compared to other approaches, VCap is simple and have very little requirements in terms of communication and memory overheads. We compare by simulations the performances of greedy routing using our virtual coordinate system with the one using the physical coordinates. Results show that the virtual coordinate system can be used to efficiently support geographic routing.

In this paper we address the problem of Denial of Service (DoS) mitigation in multicast environment. The contribution of the paper is twofold: first, we introduce an optimization (PMT) on the Merkle tree distillation codes by leveraging the implicit redundancy of a Merkle tree representation. Second, we devise a new algorithm(CECInA) for encoding/decoding that mitigates DoS attacks on the end user device and reduces the buffer size in case of DoS. In particular, according to the type of DoS attack, CECInA achieves either complexity or buffering savings. This attack mitigation capability is not a feature offered by state of the art algorithms. Furthermore CECInA is particularly efficient when used in conjunction with PMT. We derive and plot analytical results that indicates that the proposed solutions are effective. Hence, CECInA can be a viable solution to mitigate DoS in multicast, particularly suited for contexts in which end-user devices are resource constrained. As for PMT, note that it is a general technique that can be adopted independently from CECInA.

This paper addresses the problem of the identification of faulty units in symmetric systems under the diagnostic model proposed by Barsi, Grandoni and Maestrini (hence called the BGM model). The paper introduces and evaluates an algorithm named Diagnosis Algorithm for Symmetric Systems under the BGM model (DABS). It is shown that DABS provides a diagnosis unconditionally correct although possibly incomplete. A measure of diagnosis incompleteness IDeg(t) has been defined as the quotient between the number of suspect units (i.e. the units that DABS is unable to identify as either good or faulty) and the number of system units. IDeg(t) is evaluated over the set of syndromes deriving from at most t faults under the BGM model. A general approach to the evaluation of IDeg(t) in symmetric systems is introduced, and tight bounds to IDeg(t) are derived for square toroidal grids and hypercubes. This bound is O(t/n) in the case of square toroidal grids of n units.

We use a vertex-isoperimetric inequality to show that the number of test and repair iterations needed to perform sequential diagnosis of d-dimensional hypercubes is upper bounded by d-r, where r/spl epsi//spl Theta/(d). This result improves the best bound of d test and repair iterations previously known. Numerical evaluation has shown that the actual value of r ranges from 0.16d to 0.31d.

This paper introduces a distributed data storage for mobile, wireless networks based on a peer-to-peer paradigm. The distributed storage provides support to create and share files under a write-once model, and ensures at the same time data confidentiality and dependability by encoding files in a Redundant Residue Number System. More specifically files are partitioned into records and each record in encoded separately as (h+r)-tuples of data residues using h+r moduli. In turn, the residues are distributed among the mobiles in the network. Dependability is ensured since data can be reconstructed in the presence of up to s≤r residue erasures, combined with up to ¨ o 2 s r − corrupted residues, and data confidentiality is ensured since recovering the original information requires knowledge of the entire set of moduli.

The problem of identifying the faulty units in regularly interconnected systems is addressed. The diagnosis is based on mutual tests of units, which are adjacent in the "system graph" describing the interconnection structure. This paper evaluates an algorithm named EDARS (Efficient Diagnosis Algorithm for Regular Structures). The diagnosis provided by this algorithm is provably correct and almost complete with high probability. Diagnosis correctness is guaranteed if the cardinality of the actual fault set is below a "syndrome-dependent bound," asserted by the algorithm itself along with the diagnosis. Evaluation of EDARS relies upon extensive simulation which covered grids, hypercubes, and cube-connected cycles (CCC). Simulation experiments showed that the degree of the system graph has a strong impact over diagnosis completeness and affects the "syndrome-dependent bound," ensuring correctness. Furthermore, a comparative analysis of the performance of EDARS, with hypercubes and CCCs on one side and grids of the same size and degree on the other side, showed that diameter and bisection width of the system graph also influence the diagnosis correctness and completeness.

The problem of identifying faulty mobiles in ad-hoc networks is considered. Current diagnostic models were designed for wired networks, thus they do not take advantage of the shared nature of communication typical of ad-hoc networks. In this paper we introduce a new comparison-based diagnostic model based on the one-to-many communication paradigm. Two implementations of the model are presented. In the first implementation, we assume that the network topology does not change during diagnosis, and we show that both hard and soft faults can be easily, detected Based on this implementation, a diagnosis protocol is presented The evaluation of the communication and time complexity of the protocol indicates that efficient diagnosis protocols for ad-hoc networks based on our model can be designed In the second implementation we allow the system topology to change during diagnosis. As expected, the ability of diagnosing faults under this scenario is significantly reduced with respect to the stationary case.

A new diagnosis algorithm for square grids is introduced. The algorithm always provides correct diagnosis if the number of faulty processors is below T, a bound with T /spl epsi//spl Theta/(n/sup 2/3/), which was derived by worst-case analysis. A more effective tool to validate the diagnosis correctness is the syndrome dependent bound T/sub /spl sigma// with T/sub /spl sigma///spl ges/T, asserted by the diagnosis algorithm itself for every given diagnosis experiment. Simulation studies provided evidence that the diagnosis is complete or almost complete if the number of faults is below T. The fraction of units which cannot be identified as either faulty or nonfaulty remains relatively small as long as the number of faults is below n/3 and, as long as the number of faults is below n/2, the diagnosis is correct with high probability.

The problem of multiple faults diagnosis in safety-critical systems is considered. Error propagation between system components is modeled as a directed graph, where the errors propagate instantaneously along the edges. Some of the system components are equipped with alarms, which ring when abnormal conditions are detected. A diagnosis algorithm identifies the set of potential failure sources based on the set of ringing alarms. The paper introduces the D-FAULTS algorithm, which diagnoses the system when at most two nodes can be failure sources at any time. The concept of sequential diagnosis is also introduced, to deal with an unknown number of faults. Sequential diagnosis is aimed at locating the smallest set of nodes containing at least one fault. Using this approach, a faulty system can be restored to normal condition by executing repeatedly the diagnosis and repair phases. To this purpose, we introduce the sequential diagnosis algorithm S-DIAG with optimal time complexity.

Introduces EDARS (Efficient Diagnosis Algorithm for Regular Structures). The algorithm provides a diagnosis which is correct, but possibly incomplete, if the cardinality of the actual fault set is below a "syndrome-dependent bound" asserted by the algorithm itself. The time complexity of EDARS is O(nt) when executed on t-regular structures of size n. The correctness and the completeness degree of EDARS were evaluated by means of simulation. Grids, hypercubes and cube-connected cycle (CCC) structures were considered. Simulation results with grid structures showed a strong influence of structure degree over diagnosis performance. Furthermore, comparisons of simulation results obtained with hypercubes, CCCs and grids of the same size and degree showed that diameter and bisection width also appear to influence the performance of EDARS, particularly with respect to diagnosis completeness.

An important issue in the design of future personal communication services (PCS) networks is the efficient management of location information. In this paper, we consider a distributed database architecture for location management in which update and query loads of the individual databases are balanced. An important issue to consider in load balanced location management algorithms is the number of databases a mobile host's location information is updated in. To have the same replication for all mobiles is not optimal. In this paper we present a dynamic load balanced algorithm which replicates mobile hosts according to their level of activity. We analyze the algorithms and derive expressions for the cost of the algorithm. We compare the algorithm with an existing algorithm and show the effectiveness of the proposed algorithm.

An important issue in the design of future personal communication services (PCS) networks is the efficient management of location information. The current IS-41 standard PCS architecture uses a centralized database, the home location register (HLR), to store service and location information of each mobile registered in the PCS network. If the HLR fails, all incoming calls to a mobile from hosts which are not in the same location area as the mobile are lost. Location updates from mobiles to the HLR are also lost. Once the HLR is functional it can not direct calls to mobiles immediately as mobiles could have changed their location during the HLR's failure. Fast recovery from a failure of the HLR is hence important. A link failure in the network could partition the network resulting in a loss of location updates from mobiles affected by the failed link. We present a new protocol for fast recovery of the HLR after a HLR failure or an intermediate link failure. The protocol does not require use of wireless bandwidth during the recovery process, has a bounded recovery period and is simple to implement making it an appealing choice in the design of future mobile networks. We analyze the protocol in order to find a medium between protocol cost and the recovery interval.