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This papers proposes an refinement of the taxonomy of mobile apps in tourism roposed by Kennedy-Eden and Gretzel (2012) from two perspectives: a taxonomy on what services travel-related apps provide to the user and a taxonomy based on the functionalities. The taxonomies provide insights into app development trends as well as gaps in the mobile app landscape. A few emblematic case studies from European cities are analysed. Managerial and policy making implications are provided.

It is our great pleasure to welcome you to the Fourth IEEE International Workshop on Emerging COgnitive Radio Applications and aLgorithms (CORAL 2016), held in Coimbra, Portugal on June 21, 2016, in conjunction with the IEEE WoWMoM 2016 Conference.

Background:There is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families.Methods:This is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community.Results:The societal burden of AD, composed of public, patient, and informal care costs, was about €20,000/yr. Out of this, the cost borne by the public sector was €4,534/yr. The main driver of public cost was the national cash-for-care allowance (€2,324/yr), followed by drug prescriptions (€1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at €13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure.Conclusion:The burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556).

The availability of family caregivers of older people is decreasing in Italy as the number of migrant care workers (MCWs) hired by families increases. There is little evidence on the influence of socioeconomic factors in the employment of MCWs.We analyzed baseline data from 438 older people with moderate Alzheimer's disease (AD), and their family caregivers enrolled in the Up-Tech trial. We used bivariate analysis and multilevel regressions to investigate the association between independent variables-education, social class, and the availability of a care allowance-and three outcomes-employment of a MCW, hours of care provided by the primary family caregiver, and by the family network (primary and other family caregivers).The availability of a care allowance and the educational level were independently associated with employing MCWs. A significant interaction between education and care allowance was found, suggesting that more educated families are more likely to spend the care allowance to hire a MCW.Socioeconomic inequalities negatively influenced access both to private care and to care allowance, leading disadvantaged families to directly provide more assistance to AD patients. Care allowance entitlement needs to be reformed in Italy and in countries with similar long-term care and migration systems.

The heterogeneity of current telecommunication access technologies is dramatically increasing the complexity and costs required for the set-up and maintenance of the network by service providers, and it is also posing important limitations in terms of network interoperability from the end-users' perspectives. For these reasons, research on wireless systems has investigated the possibility to deploy wireless devices with self-organizing and self-managing capabilities. However, most of these approaches limit the re-configuration capabilities to a specific layer (e.g. routing), and do not investigate the property of reconfiguration of the network as a whole. In this paper, we propose an alternative architecture (called STEM-Net) for network infrastructure deployment, extension, and management. In STEM-Net self-organization is managed through fully-reconfigurable wireless devices (called stem-nodes), that can undergo mutations to fulfill a specific tasks, like their biological counterpart. In this work we present the main characteristics of stem-nodes, and we discuss how their evolutionary behaviour can result in an extreme flexibility of the whole network segment, transforming the technological heterogeneity from a limitation to a richness.

Plasma alpha-tocopherol and retinol, both assayed by an HPLC method, have been evaluated in a group of 60 patients affected by insulin-dependent (type 1) diabetes mellitus, stratified according to the presence of retinopathy and nephropathy diagnosed by an urinary albumin excretion rate ranging between 20 and 200 micrograms/min (microalbuminaria) or200 micrograms/min (macroalbuminuria), all of whom were compared with 26 healthy controls strictly matched for age and sex. Plasma lipids and age were positively correlated with plasma retinol and alpha-tocopherol in both diabetic and control subjects. Either plasma retinol or its ratio to cholesterol were significantly and independently reduced in the younger subset of diabetics, as compared to controls, independently from other confounding variables, while plasma alpha-tocopherol was unchanged in diabetic subjects and in healthy controls. Retinopathy was not associated with altered levels of both plasma alpha-tocopherol or retinol. The presence of increased urinary albumin excretion was associated with higher plasma levels of alpha-tocopherol and, only for macroalbuminuria, of retinol. However, after processing the data by a multivariate model, nephropathy was characterized by an increase only in plasma alpha-tocopherol. In conclusion, according to our findings, plasma retinol is significantly decreased in younger insulin-dependent diabetic patients while alpha-tocopherol is significantly altered in diabetic patients with nephropathy.

To report our experience on implementation and preliminary results of a decision-making model based on the recommendations of an Interdisciplinary Oncological Care Group developed for the management of colorectal cancer.The multidisciplinary team identified a reference guideline using appraisal of guidelines for research and evaluation (AGREE) tool based on a sequential assessment of the guideline quality. Thereafter, internal guidelines with diagnostic and therapeutic management for early, locally advanced and metastatic colonic and rectal cancer were drafted; organizational aspects, responsibility matrices, protocol actions for each area of specialty involved and indicators for performing audits were also defined.The National Institute for Health and Care Excellence (NICE) UK guideline was the reference for drafting the internal guideline document; from February to November 2013, 125 patients with colorectal cancer were discussed by and taken under the care of the Interdisciplinary Oncological Care Group. The first audit performed in December 2013 revealed optimal adherence to the internal guideline, mainly in terms of uniformity and accuracy of perioperative staging, coordination and timing of multi-modal therapies. To date, all patients under observation are within the diagnostic and therapeutic course, no patient came out from the multidisciplinary "path" and only in 14% of cases have the first recommendations proposed been changed. The selected indicators appear effective and reliable, while at the moment, it is not yet possible to assess the impact of the multidisciplinary team on clinical outcome.Although having a short observation period, our model seems capable of determining optimal uniformity of diagnostic and therapeutic management, to a high degree of patient satisfaction. A longer observation period is necessary in order to confirm these observations and for assessing the impact on clinical outcome.

The deployment of Structural Health Monitoring (SHM) systems is a natively interdisciplinary task that involves joint research contributions from sensing technologies, data science and civil engineering. The capability to assess, also from remote stations, the working conditions of industrial plants or the structural integrity of civil buildings is widely requested in many application fields. The technological development aims to continuously provide innovative tools and approaches to satisfy these demands. As a first instance, reliable monitoring strategies are needed to detect structural damages while filtering out environmental noise. Ongoing solutions to tackle these topics are based on the exploitation of highly customized sensing technologies, such as shaped transducers for Acoustic Emission (AE) testing or Micro-Electro-Mechanical System (MEMS) accelerometers for Operational Modal Analysis (OMA) [1]. On the other hand, effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in civil engineering. Layered SHM architectures [2] represent straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few real-world implementations have been presented so far in the literature. In this paper, we overcome these limitations by presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists of accelerometer-based sensor networks, a data acquisition layer, built on the recent W3C Web of Things standard [3], and a data storage and analytics layer, which leverages distributed database and Machine Learning tools. We extensively discuss the hardware/software components of the proposed SHM architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally, the effectiveness of the system is validated on a real-world use-case, i.e., the monitoring of a metallic frame structure located at the SHM research labs of the University of Bologna, Italy, within the MAC4PRO project [4].

The Web of Things (WoT) standard recently promoted by the W3C constitutes a promising approach to devise interoperable IoT systems able to cope with the heterogeneity of software platforms and devices. The WoT architecture envisages interconnected IoT scenarios characterized by a multitude of Web Things (WTs) that interact according to well-defined software interfaces; at the same time, it assumes static allocations of WTs to hosting devices, and it does not cope with the intrinsic dynamicity of IoT environments in terms of time-varying network and computational loads. In this paper, we extend the WoT paradigm for cloud-edge continuum deployments, hence supporting dynamic orchestration and mobility of WTs among the available computational resources. Differently from state-of-art Mobile Edge Computing (MEC) approaches, we heavily exploit the W3C WoT, and specifically its capability to standardize the software interfaces of the WTs, in order to propose the concept of a Migratable WoT (M-WoT), in which WTs are seamlessly allocated to hosts according to their dynamic interactions. Three main contributions are proposed in this paper. First, we describe the architecture of the M-WoT framework, by focusing on the stateful migration of WTs and on the management of the WT handoff process. Second, we rigorously formulate the WT allocation as a multi-objective optimization problem, and propose a graph-based heuristic. Third, we describe a container-based implementation of M-WoT and a twofold evaluation, through which we assess the performance of the proposed migration policy in a distributed edge computing setup and in a real-world IoT monitoring scenario.

In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose mathsf {ELAPSE} (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. mathsf {ELAPSE} integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the mathsf {ELAPSE} framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.

Bluetooth Mesh is a recent SIG standard enabling the deployment of multi-hop Wireless Sensor Networks (WSNs) over Bluetooth Low Energy (BLE) communication links. The standard introduces many novel and interesting features in the Internet of Things (IoT) domain, such as the seamless integration among sensors and mobile and wearable devices, and the support for a wide range of different IoT application profiles. At the same time, fine-grained assessments of the performance are still needed to understand the potential of the technology. In this paper, we investigate the usage of Bluetooth Mesh solutions for the joint monitoring of indoor spaces and humans. Through the deployment of a test-bed, we evaluate the performance of Bluetooth Mesh WSNs under varying traffic loads and network sizes. In addition, by exploiting the short-range, multi-hop communications, we propose a procedure for the indoor localization of mobile devices and evaluate its accuracy. The results demonstrate that the technology supports reasonable delivery ratio under high traffic loads, however the network and localization performance sharply decreases when increasing the number of hops between the source and destination nodes.

Digital Twins are crucial in Industry 4.0 IoT scenarios, as they replicate physical assets and enable important tasks such as predictive analytics, what-if scenarios and real time monitoring. The heterogeneity of IoT use cases usually makes the development of digital twins extremely application-specific as well as prone to interoperability issues. To overcome these two challenges, we propose WoTwins, a framework that, on one side, leverages the W3C Web of Things (WoT) standard to model data and entities, and, on the other side, generates automatically Digital Twins of existing Web Things by modeling their state space through a Markov Decision Process (MDP) graph and by predicting its behavior though Machine Learning techniques. We conduct experiments on a simulated use cases related to IoT robotics to evaluate our proposal

The integration of legacy IoT ecosystems in Industry 4.0 scenarios requires human effort to adapt single devices. This process would highly benefit from features like device lookup, loose coupling and late binding. In this paper, we tackle the issue of integrating legacy monitoring systems and actuation systems in an industrial scenario, by looking into the Web of Things (WoT) as a communication standard and the Eclipse Arrowhead Framework (AHF) as a service orchestrator. More specifically, we propose a general architectural approach to enable closed-loop automation between the above mentioned legacy systems by leveraging the adaptation of the WoT to the AHF. Then, we develop a rule-based engine that enables the control of the actuation based on sensor values. Finally, we present a proof-of-concept use case where we integrate a Structural Health Monitoring (SHM) scenario with a power control actuation subsystem using the developed components