Mobile and Pervasive Computing

A noise map facilitates the monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (for months or even years), as they rely on population and traffic models rather than on real data. Smart phone based urban sensing can be leveraged to create an open and inexpensive platform for rendering up-todate noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end, context-aware, noise mapping system called Ear-Phone. Ear-Phone investigates the use of different interpolation and regularization methods to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95, N97 and HP iPAQ, HTC One mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. A major challenge of using smart phones as sensors is that even at the same location, the sensor reading may vary depending on the phone orientation and user context (for example, whether the user is carrying the phone in a bag or holding it in her palm). To address this problem, Ear-Phone leverages context-aware sensing. We develop classifiers to accurately determine the phone sensing context. Upon context discovery, Ear-Phone automatically decides whether to sense or not. Ear-phone also implements in-situ calibration which performs simple calibration that can be carried out without any technical skills whatsoever required on the user's part. Extensive simulations and outdoor * Corresponding author.

Η ψηφιακή τεχνολογία έχει μετασχηματίσει ριζικά τον τομέα της υγείας, από τους ηλεκτρονικούς φακέλους και την τηλεϊατρική έως τη χρήση τεχνητής νοημοσύνης στη διάγνωση και τη θεραπεία. Παρά τα σημαντικά οφέλη, οι καινοτομίες αυτές εγείρουν κρίσιμα ζητήματα βιοηθικής, βιοεξουσίας και κοινωνικής δικαιοσύνης. Η εργασία εξετάζει τις προκλήσεις που προκύπτουν για το κράτος δικαίου, τη δημοκρατία και τα θεμελιώδη δικαιώματα των πολιτών, αναλύοντας μοντέλα διαχείρισης δεδομένων υγείας και τις συνέπειες της αλγοριθμικής λήψης αποφάσεων στην ιατρική πράξη. Μέσα από παραδείγματα καλών και κακών πρακτικών, αναδεικνύεται η ανάγκη για θεσμικές εγγυήσεις, διαφάνεια και ενεργή συμμετοχή των πολιτών, ώστε η τεχνολογία να υπηρετεί την υγεία χωρίς να υπονομεύει την ελευθερία και την αξιοπρέπεια του ανθρώπου.
Digital technology has radically transformed the field of healthcare, from electronic health records and telemedicine to the use of artificial intelligence in diagnosis and treatment. Despite its significant benefits, these innovations raise critical issues of bioethics, biopower, and social justice. This paper examines the challenges posed to the rule of law, democracy, and fundamental rights, analyzing models of health data governance and the implications of algorithmic decision-making in medical practice. Through examples of best and worst practices, it highlights the need for institutional safeguards, transparency, and active citizen participation, so that technology can serve health without undermining human freedom and dignity.
Digital Health, Artificial Intelligence, Bioethics, Biopower, Health Data, Rule of Law, Democracy

Home and Building Automation Systems (HBAS) are becoming of widespread adoption. When distinct users interact with such systems, their intentions are likely to be different, often resulting in conflicting situations, which the systems ought to recognize and resolve automatically. This work aims at investigating conflict in HBAS and creating a solution to detect and resolve them. Herein, we review the literature concerning conflict detection and resolution, and propose a formal framework based on constraint solving that enables detecting and solving conflict situations automatically.

Το περιεχόμενο του συγγράμματος αντιστοιχεί στο εργαστηριακό μέρος των προπτυχιακών μαθημάτων «Συστήματα Ψηφιακής Επεξεργασίας Σήματος σε Πραγματικό Χρόνο» και των μεταπτυχιακών μαθημάτων «Συστήματα Πραγματικού Χρόνου», «Διασύνδεση Η/Υ με τον Αναλογικό Κόσμο» και «Ανάπτυξη Εφαρμογών Βασισμένων σε Αισθητήρες». Πρόκειται για ένα Εγχειρίδιο Εκμάθησης του προγραμματιστικού περιβάλλοντος για επιστήμονες και μηχανικούς: LabVIEW – Laboratory Virtual Instruments Engineering Environment. Το συγκεκριμένο περιβάλλον (στα Ελληνικά Περιβάλλον Κατασκευής Εικονικών Εργαστηριακών Οργάνων) ειδικεύεται στην πρωτοτυποποίηση, τον σχεδιασμό, την ανάπτυξη, τον προγραμματισμό και τον έλεγχο συστημάτων μετρήσεων και αυτομάτου ελέγχου. Τα επτά κεφάλαια του Συγγράμματος ασχολούνται με την εκμάθηση των εξής θεμάτων: «Ανάπτυξη Εικονικών Οργάνων», «Βρόγχοι και Διαγράμματα», «Πίνακες και Γραφικές Απεικονίσεις», «Δομές Περίπτωσης και Ακολουθίας», «Αλφαριθμητικές Σειρές», «Είσοδος/Έξοδος Αρχείων», «Συλλογή Δεδομένων σε Πραγματικό Χρόνο», «Αναλογική και Ψηφιακή Είσοδος/Έξοδος», «Φασματική Ανάλυση» και «Ψηφιακά Φίλτρα».

Communication protocols need to be more reliable and efficient since the Internet of Things (IoT) is growing exponentially, changing how devices interact. IoT ecosystems often comprise many devices with varying degrees of processing power, low energy storage, and high network requirements. These elements provide serious obstacles to current communication methods, including latency issues, wasteful bandwidth use, and security flaws. The main goal of this research is to improve communication protocols specifically designed for IoT situations. The suggested improvements seek to solve the shortcomings of conventional protocols by putting adaptive processes, lightweight structures, and cutting-edge security measures into place. The project looks for methods to optimize bandwidth usage, lower latency, and encourage smooth device interoperability. It also emphasizes how crucial data transmission security is to defending against intrusions on IoT. The research evaluates how well the enhanced protocols work in various IoT contexts using simulations, theoretical analysis, and hands-on testing. The results show how these streamlined protocols could allow for faster information sharing, less consumption of energy, as well as scalable IoT implementations.

This paper presents a model for building context-based systems in pervasive computing environments from high level specifications. A pervasive computing environment is viewed as a collaboration space consisting of mobile users, system services, and sensors and resources embedded in the physical environment. The approach presented here is developed for building collaborative activities in which users and environment services cooperate towards some shared objectives and tasks. The specification model allows expression of policies related to context-based discovery and secure access of resources, and role-based interactions among users and environmental services. Using several examples we illustrate the capabilities of our specification model for expressing various kinds of context-based requirements for resource access and user interactions.

Understanding human mobility is essential for many fields, including transportation planning. Currently, surveys are the primary source for such analysis. However, in the recent past, many researchers have focused on Call Detail Records (CDR) for identifying travel patterns. CDRs have shown correlation to human mobility behavior. However, one of the main issues in using CDR data is that it is difficult to identify the precise location of the user due to the low spacial resolution of the data and other artifacts such as the load sharing effect. Existing approaches have certain limitations. Previous studies using CDRs do not consider the transmit power of cell towers when localizing the users and use an oversimplified approach to identify load sharing effects. Furthermore, they consider the entire population of users as one group neglecting the differences in mobility patterns of different segments of users. This research introduces a novel methodology to user position localization from CDRs through improved detection of load sharing effects, by taking the transmit power into account, and segmenting the users into distinct groups for the purpose of learning any parameters of the model. Moreover, this research uses several methods to address the existing limitations and validate the generated results using nearly 4 billion CDR data points with travel survey data and voluntarily collected mobile data.

Understanding human mobility is essential for many fields, including transportation planning. Currently, surveys are the primary source for such analysis. However, in the recent past, many researchers have focused on Call Detail Records (CDR) for identifying travel patterns. CDRs have shown correlation to human mobility behavior. However, one of the main issues in using CDR data is that it is difficult to identify the precise location of the user due to the low spacial resolution of the data and other artifacts such as the load sharing effect. Existing approaches have certain limitations. Previous studies using CDRs do not consider the transmit power of cell towers when localizing the users and use an oversimplified approach to identify load sharing effects. Furthermore, they consider the entire population of users as one group neglecting the differences in mobility patterns of different segments of users. This research introduces a novel methodology to user position localization fr...

The purpose of this study was to investigate the effects of the interaction mode on users' wayfinding performance. Both the dynamic peephole (DP) and static peephole (SP) interaction modes were examined. Sixty users were divided into two groups, where users in one group had previous experiences of mobile wayfinding (i.e., experienced users) while those in the other group did not (i.e., inexperienced users). The experiment was a 2 Â 2 withinsubjects design. All of the participants were invited to complete four wayfinding tasks, and the system usability scale (SUS) questionnaire was also adopted. The results show that adopting a dynamic peephole is superior to a static peephole for perceptual wayfinding tasks. Interaction mode and user experience did not significantly affect wayfinding performance in general. Moreover, dynamic peephole gained a higher SUS score.

Initiatives like Linked Open Data have resulted in a rapid growth of the Web of data, and this growth is expected to continue. While impressive progress has been made in recent years in scalable storing, querying, and reasoning with languages like RDFS and OWL, existing reasoning techniques fail to perform when applied at Web-scale, due to the quantities of instance data, expressiveness of the ontologies, or the inherent inconsistency and incompleteness of data on the Web. These problems of scale are increasingly compounded by the appearance of highly dynamic data streams. Data streams occur in modern applications such as traffic engineering, applications of RFID tags, telecom call recording, medical record management, financial applications, and clickstreams. On the Web, many sites distribute and present information in real-time streams of semi-structured text. In many of these application areas, the ability to perform complex reasoning tasks that combine streaming information (both data and text) with background knowledge would be of great benefit. Stream reasoning is a new multidisciplinary approach for semantically processing high-frequency high-volume streams of information in combination with rich background knowledge. The NeFoRS2010 workshop is a joint continuation of earlier successful workshop on scalable and dynamic reasoning for the Semantic Web, NeFoRS'07, NeFoRS'08, and SR'09. The workshop organizers would like to thank the following researchers for their involvement and contribution to the work of the programme committee:

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences.

Low-power wireless communication is a central building block of Cyber-physical Systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude the paper with a discussion on open research questions in this field.

Consider a house cleaning robot planning its activities for the day. Assume that the robot expects the human inhabitant to first dress, then have breakfast, and finally go out. Then, it should plan not to clean the bedroom while the human is dressing, and to clean the kitchen after the human has had breakfast. In general, robots operating in inhabited environments, like households and future factory floors, should plan their behavior taking into account the actions that will be performed by the humans sharing the same environment. This would improve human-robot cohabitation, e.g., by avoiding undesired situations for the human. Unfortunately, current task planners only consider the robot's actions and unexpected external events in the planning process, and cannot accommodate expectations about the actions of the humans. In this paper, we present a human-aware planner able to address this problem. Our planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. Our planner has been tested both as a standalone component and within a full framework for human-robot interaction in a real environment.

This article presents a new representation of semantic virtual environments. We propose to use the ontology as a tool for implementation. Our model, called SVHsIEVs 1 provides a consistent representation of the following aspects: the simulated environment, its structure, and the knowledge items using ontology, interactions and tasks that virtual humans can perform in the environment. In SVHsIEVs, we find two type of ontology: the global ontology and the local ontology for Virtual Human. Our architecture has been successfully tested in 3D dynamic environments.

The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov mode...

This paper presents a model for building context-based systems in pervasive computing environments from high level specifications. A pervasive computing environment is viewed as a collaboration space consisting of mobile users, system services, and sensors and resources embedded in the physical environment. The approach presented here is developed for building collaborative activities in which users and environment services cooperate towards some shared objectives and tasks. The specification model allows expression of policies related to context-based discovery and secure access of resources, and role-based interactions among users and environmental services. Using several examples we illustrate the capabilities of our specification model for expressing various kinds of context-based requirements for resource access and user interactions.

The cloud computing is a technology which connects multiple nodes to each other in network. Cloud computing facilitate sharing of computational task and resources with clients and servers. It also operates in heterogeneous environment but the main issue is privacy and security of data. The data generated by IoT is also very large in size. If all the data is passed to cloud, the latency will increase. Fog computing was introduced to decrease the problems of cloud computing. Cloud computing is not replaced by fog computing but it is a service which enhances the cloud services by separating data from users which is need to live on a node in the outskirts. Fog computing provides the service of cloud computing data centers which collaborate on processing, storage, and networking to end users.

Home energy management is becoming increasingly important and, though there are a plethora of tools for accessing energy consumption data, few provide concrete insights that can directly help users manage demand. Mechanisms that enable a user to draw connections between activities and energy consumption by attaching contextual labels to energy events are a promising step; however solutions for collecting annotations from users can be error prone or intrusive. This work presents a system for collecting in situ annotations using a smartphone application coupled with an off-the-shelf home energy measurement infrastructure. We use a novel power profiling approach to identify important energy consumption events and solicit contextual annotations from the user via a push notification sent to a smartphone. Using a five-week study performed in five homes, we show that our power profiling approach can identify a significant percentage of important energy consumption events using a very small number of monitored devices. We were able to collect an average of over 2 annotations per day and while users provided a wide range of annotations, the motivation to provide annotations varied across subjects.

Home energy management is becoming increasingly important and, though there are a plethora of tools for accessing energy consumption data, few provide concrete insights that can directly help users manage demand. Mechanisms that enable a user to draw connections between activities and energy consumption by attaching contextual labels to energy events are a promising step; however solutions for collecting annotations from users can be error prone or intrusive. This work presents a system for collecting in situ annotations using a smartphone application coupled with an off-the-shelf home energy measurement infrastructure. We use a novel power profiling approach to identify important energy consumption events and solicit contextual annotations from the user via a push notification sent to a smartphone. Using a five-week study performed in five homes, we show that our power profiling approach can identify a significant percentage of important energy consumption events using a very small number of monitored devices. We were able to collect an average of over 2 annotations per day and while users provided a wide range of annotations, the motivation to provide annotations varied across subjects.

This work presents a system for collecting user activity annotations using in-home distributed energy monitoring combined with a novel algorithm that generates device-specific profiles used to identify potentially important changes in user context. In a five-week study of five homes, the system was able to generate profiles for 80% of the devices studied. Moreover, between four and five important changes in user context were identified when background loads were accurately distinguished.

Rainbow trout (Oncorhynchus mykiss, Walbaum, 1792) was grown in cages (water reservoirs "Bočac", Bosnia and Herzegovina) in two separate experiments for a period of 90 days (autumn -winter and springsummer). Trout were fed six different feed formulations and studied their influence on rainbow trout intestine histology. Normal histological structure of this part of the digestive system was found. The results showed that with water temperature and feed quantity decrease, the height of enterocytes increases; while with increase of water temperature and amount of food given to the fish, the average height of enterocytes decreased.

Currently it is very difficult for connection oriented applications to use a mobile environment. One reason is that Mobile IP requires intermediate software agents to be deployed in the Internet. This infrastructure based mobility scheme offers connectivity to itinerant hosts but incurs significant handoff and tunneling delays along with deployment costs. These delays are particularly harmful for connection oriented applications. In this paper we investigate an alternate mobility scheme which does not require any such infrastructure but only uses an end-point technique and interestingly provides much faster loss-free handoff for connection oriented applications. This End-to-End scheme named Interactive Protocol for Mobile Networks (IPMN) intelligently performs handoff based on information provided by MAC Layer. The network address change is handled by renewing the existing connections by manipulating the TCP/IP stack at the end-points. Also, unlike several other recently proposed end-to-end techniques which require extensive modification of end-protocols, the proposed scheme does not require any functional change in the TCP/IP protocol software. Besides the difference in deployment scenarios, the IPMN offers blazingly fast event based handoff and much faster and simplified transport (no tunneling delay) than MIP. We have implemented IPMN over FreeBSD. In this paper we show the performance advantage of IPMN over MIP with real deployment for three interesting real-time traffic types -www, voice streaming and, steerable/interactive time critical video.

efficiency, privacy, and transparency by integrating public and private blockchains. The system guarantees secure transaction validation through adaptive consensus protocols and multi-signature authentication, greatly enhancing interoperability. Compared to traditional models, performance assessments show a 36% increase in transaction speed, a 42% decrease in cross-chain transaction latency, and a 25% improvement in network scalability. According to security studies, blockchain security is strengthened by a 51% reduction in double-spending vulnerabilities and a 40% reduction in transaction failure rates. These findings demonstrate the model's potential to provide a decentralized, trustless marketplace, promoting safe and effective blockchain use across the IoT, healthcare, and financial sectors. The suggested method improves blockchain interoperability by facilitating smooth, scalable, and safe interactions in diverse blockchain ecosystems.

Demographics prediction is an important component of user profile modeling. The accurate prediction of users' demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users' demographics, including ''gender'', ''job type'', ''marital status'', ''age'' and ''number of family members'', based on mobile data, such as users' usage logs, physical activities and environmental contexts. The core idea is to build a supervised learning framework, where each user is represented as a feature vector and users' demographics are considered as prediction targets. The most important component is to construct features from raw data and then supervised learning models can be applied. We propose a feature construction framework, CFC (contextual feature construction), where each feature is defined as the conditional probability of one user activity under the given contexts. Consequently, besides employing standard supervised learning models, we propose a regularized multi-task learning framework to model different kinds of demographics predictions collectively. We also propose a cost-sensitive classification framework for regression tasks, in order to benefit from the existing dimension reduction methods. Finally, due to the limited training instances, we employ ensemble to avoid overfitting. The experimental results show that the framework achieves classification accuracies on ''gender'', ''job'' and ''marital status'' as high as 96%, 83% and 86%, respectively, and achieves Root Mean Square Error (RMSE) on ''age'' and ''number of family members'' as low as 0.69 and 0.66 respectively, under the leave-one-out evaluation.

Smartphones are increasingly becoming popular. All Smartphones have rich set of sensors, which can be used to provide driver assistant services. Services such as road traffic information and road condition to provide such services, there is need of sensors such as GPS, Gyroscope, Accelerometer. Now day's smartphones are coming with this sensor. Smartphone camera sensor, one of the most powerful and neglected Sensor can be used to analyses traffic scene. In this paper we have provided surveys on different approaches to provide collaborative driver assistant service and analyze road surface disruption.

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Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other (V2V) as well as with roadside infrastructure units (V2I). These units provide different services such as driver information systems and Internet access. The high speed and high mobility of vehicles make it very challenging to establish and maintain a connection to these units. We introduce a new protocol which uses the characteristics of vehicle movements to predict the future behavior of vehicles, and to select a route with the longest lifetime to connect to the wired network. The proposed protocol aims at spreading the advertisement messages through multi-hops without flooding the network and performing seamless handovers. The proposed protocol is simulated by considering mobility scenarios, generated by the MOVE tool, in the ns-2 simulator tool. The simulation results show that the proposed scheme increases the packet delivery ratio and decreases the packet delay and overhead.

Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other (V2V) as well as with roadside infrastructure units (V2I). These units provide different services such as driver information systems and Internet access. The high speed and high mobility of vehicles make it very challenging to establish and maintain a connection to these units. We introduce a new protocol which uses the characteristics of vehicle movements to predict the future behavior of vehicles, and to select a route with the longest lifetime to connect to the wired network. The proposed protocol aims at spreading the advertisement messages through multi-hops without flooding the network and performing seamless handovers. The proposed protocol is simulated by considering mobility scenarios, generated by the MOVE tool, in the ns-2 simulator tool. The simulation results show that the proposed scheme increases the packet delivery ratio and decreases the packet delay and overhead.

Over the past decade, smartphones have become the point of convergence for many applications and services. There is a growing trend in which traditional smart-card based services like banking, transport and access control are being provisioned through smartphones. Smartphones with Near Field Communication (NFC) capability can emulate a contactless smart card; popular examples of such services include Google Pay and Apple Pay. Similar to contactless smart cards, NFC-based smartphone transactions are susceptible to relay attacks. For contactless smart cards, distance-bounding protocols are proposed to counter such attacks; for NFC-based smartphone transactions, ambient sensors have been proposed as potential countermeasures. In this study, we have empirically evaluated the suitability of ambient sensors as a proximity detection mechanism for contactless transactions. To provide a comprehensive analysis, we also collected relay attack data to ascertain whether ambient sensors are able to thwart such attacks effectively. We initially evaluated 17 sensors before selecting 7 sensors for in-depth analysis based on their effectiveness as potential proximity detection mechanisms within the constraints of a contactless transaction scenario. Each sensor was used to record 1000 legitimate and relay (illegitimate) contactless transactions at four different physical locations. The analysis of these transactions provides an empirical foundation on which to determine whether ambient sensors provide a strong proximity detection mechanism for security-sensitive applications like banking, transport and high-security access control.

Urban computing is a wide area for research, which has picked up momentum after the introduction of Smart City vision. Smart City initiative aims to improve the quality of life of its citizens by building a strong and resilient framework for the city. Noise pollution is one of the main problems affecting citizens all over. Citizen science, when combined with Mobile crowdsensing and Data mining can help researchers and town planners to collect urban noise data at a massive spatio-temporal scale, and analyse it to understand urban dynamics. This work is an effort to use urban noise computing to detect anomalies and interesting events happening in the city. The paper introduces the readers to the basics of Urban noise computing. It covers a comprehensive literature survey in the area of Urban noise computing and Anomaly detection. This work also includes problem statement, problem modelling and partial implementation details.

The convergence of social networking and mobile computing is expected to generate a new class of applications, called Mobile Social Networking (MSN) applications, that will be of significant importance for the coming years. Indeed, MSN enhances the capabilities of more traditional Online Social Networking (OSN) to a great extent by enabling mobile users to benefit from opportunistically created social communities; these communities should be determined not only by common interests or contacts but also by mobility-related context, such as physical location and co-presence. In this paper we precisely define what we intend for MSN applications and overview the primary MSN support solutions available in the current literature and that specifically address the underlying technical challenges, design issues, and emerging middleware guidelines. Our primary goal is to identify engineering design criteria for future MSN middleware solutions, capable to flexibly adapt to different application domains and deployment requirements. To this purpose, we present a novel taxonomy of MSN structures and describe how various existing middleware approaches fit the proposed classification; moreover, the survey takes the opportunity of these descriptions to discuss related middleware design/implementation choices determining specific tradeoffs between expressive power, flexibility, and scalability.

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring)a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.

Ad-hoc Mobile Cloud (AMC) computing represents an opportunistic platform for inexpensive and collaborative resource provisioning. However, service discovery in AMC faces resource limitation and dynamic context challenges. Current attempts to address the impact of context change on service discovery have either not investigated device context or only considered device profile. Meanwhile, service discovery in AMC is resourcesensitive and the dynamic nature of this environment implies the relevance of discovered services is context-dependent. Discovering relevant services therefore requires enough context information, especially resource context, which specifies a device's resource capability. This capability enhances discovery efficiency because having sufficient context information promotes adaptability, which enables discovery mechanisms to tailor services. This paper presented a service discovery mechanism that polled and utilized device context to discover services in a resource-aware manner. We developed a prototype with Node Monitor and Discovery Engine components to gather device context and perform proactive service discovery respectively. With these components, we achieved a service discovery process that utilized device context to measure the resource capability of a client device during service discovery. The results indicated that this approach optimized device resource usage and improved quality of service discovery.

Οι πολύπλοκες διαδικασίες κρατήσεων ή οι μη διαισθητικές διαδικασίες κρατήσεων και
έλλειψη εξατομίκευσης, μπορούν να δημιουργήσουν σύγχυση και να οδηγήσουν σε
απογοήτευση και ενδεχομένως σε εγκατάλειψη της διαδικασίας κράτησης. Η αντιμετώπιση
των συγκεκριμένων κοινών ζητημάτων σχεδιασμού UX, τα οποία είναι αρκετά συνήθη και
κοινά, μπορεί να βελτιώσει σημαντικά τη συνολική εμπειρία του χρήστη στις εφαρμογές και
τους ιστοτόπους των τουριστικών πρακτορείων, οδηγώντας σε αυξημένη ικανοποίηση και
αφοσίωση των πελατών (Foris, Tecau, Hartescu, & Foris, 2020). Η αρχιτεκτονική
πληροφοριών διαδραματίζει καθοριστικό ρόλο στην οργάνωση, επισήμανση και διάρθρωση
των πληροφοριών σε ιστότοπους, εφαρμογές και άλλα περιβάλλοντα πλούσια σε
πληροφορίες, επηρεάζοντας σημαντικά τη χρηστικότητα του εκάστοτε συστήματος (Guizani,
2022). Μια βασική πτυχή της αρχιτεκτονικής πληροφοριών είναι ο σχεδιασμός των φίλτρων,
της αναζήτησης και της ταξινόμησης, χαρακτηριστικά απαραίτητα για την αποτελεσματική
ανάκτηση πληροφοριών. Η τεχνητή νοημοσύνη διαδραματίζει καθοριστικό ρόλο στην
ανάλυση της συμπεριφοράς των χρηστών και στην παροχή εξατομικευμένων προτάσεων
(Vasantham, Preetham, Kumar, et al., 2023), έτσι δύναται να συμβάλλει στη βελτίωση των
λειτουργιών ταξινόμησης και φιλτραρίσματος, διευκολύνοντας τους χρήστες να βρίσκουν τις
πληροφορίες που χρειάζονται σε ένα πλαίσιο διαπολιτισμικής επικοινωνίας (Bansal, 2024).

Smart phones today are equipped with inbuilt sensors that give safety enhancement to the drivers on road. The accelerometer and a GPS tracking system of smart phone can give assistance to the driver while travelling. In this paper, the accelerometer of an Android based smart phone is used to record motions generated while passing the obstacles like humps and potholes. GPS is also used to find the location of obstacles on the road. This information will be stored on the server and will generate alert message to the drivers before 300 meters before the point of obstacles. With real time analysis and alerts of these factors, we can increase a driver's overall awareness to maximize safety. Google Earth is used to create road condition maps using GPS coordinates. We propose an early warning system that uses a smartphone based application to alert the driver in advance when the vehicle is approaching the obstacles like humps, rough roads or potholes. In addition, the application also ...

Blockchain technology has gained prominence recently by virtue of its strong security features for clinical data. Automation of blockchain transactions enables data transactions and records, providing decentralized, secure, and dependable access. Through intelligence-sharing agreements, it can also manage member relationships without the need for a middleman or other third party. Researchers in the health-care industry using blockchain algorithms to safeguard security of data, which is properly stored, are on the rise. In addition, this technology is patient adaptive. Patients and other health-care users can now trust the technology because it prevents any third party from accessing the medical data. Many platforms intended for use in the health-care domain are emerging, including Gem Health Network and MedRec. Using blockchain in health-care protects user data and grants them full authority over their data. However, blockchain technology is also confronting challenges and limitations regarding data privacy and storage capacity. This paper explores the implementation of blockchain within health-care sector while providing an overview of this technology and the different consensus algorithms used in blockchain technology.

Assessing the driver's cognitive load has become an increasing research interest. Methods described in literature fall into three categories: self-assessment, physiological measures, and performance-based measures. We claim that cognitive load can also be deducted by environmental factors, such as visual complexity of the context or driving speed. This paper describes an experiment aimed at backing this claim and its first results.

Security is vital for the reliable operation of vehicular ad hoc networks (VANETs). One of the critical security issues is the revocation of misbehaving vehicles. While essential, revocation checking can leak private information. In particular, repositories receiving the certificate status queries could infer the identity of the vehicles posing the query and the target of the query. An important loss of privacy results from this ability to tie the checking vehicle with the query's target, due to their likely willingness to communicate. In this paper, we propose an Efficient and Privacy-Aware revocation Mechanism (EPA) based on the use of Merkle Hash Trees (MHT) and a Crowds-based anonymous protocol, which replaces the time-consuming certificate revocation lists checking process. EPA provides explicit, concise, authenticated and unforgeable information about the revocation status of each certificate while preserving the users' privacy. Moreover, EPA reduces the security overhead for certificate status checking, and enhances the availability and usability of the revocation data. By conducting detailed performance evaluation, EPA is demonstrated to be reliable, efficient, and scalable.

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

According to recent studies, such as the World Bank report, COVID-19 and the pandemic crisis triggered a massive erosion of human capital at critical moments in the life cycle, derailing development for millions of children and youth, particularly in low- and middle income countries. Analysis of global data on young people who were under 25 at the start of the pandemic highlighted the impact of the pandemic on young people at key developmental stages: early childhood (0-5 years), school age (6-14 years) and youth (15-24 years). It was revealed that today's students could lose up to 10% of their future earnings due to the educational disruptions caused by COVID-19 and that the cognitive deficit of today's toddlers could be translated into a 25% drop in their earnings when they become adults.
Human capital - the knowledge, skills and health that people accumulate over their lifetime - is the key to unlocking a child's potential and enabling countries to achieve resilient recovery and strong future growth. However, the pandemic has closed schools and workplaces and disrupted other essential services that protect and promote human capital, such as maternal and child care and job training.
Due to the pandemic, preschool children lost a lot of their preschool education and experienced a lot of food insecurity during the pandemic. In addition, school-age children lost, on average, about thirty-two days of learning for every thirty days of school closures. This is because school closures and ineffective distance learning measures taken, made students miss out on learning and also forget what they had already learned. As a result, learning poverty has further increased. In addition, COVID-19 has dealt a heavy blow to youth employment. It is estimated that at the end of 2021, 40,000,000 people did not enter the labor market due to the pandemic, exacerbating youth unemployment trends. Also, youth earnings shrank by 15% in 2020 and 12% in 2021.
The window for addressing delays in human capital accumulation is small, as gaps in the early stages of the life cycle tend to widen over time. Without long-term targeting, the pandemic will further deepen poverty and inequality.
In this frame, this study focuses on policy options available in order to reverse the consequences of the pandemic on today's youth, underlining the priority of the issue for the future development of all countries. Given that today's under-25s, those most affected by the erosion of human capital, will make up more than 90% of the most productive-age workforce in 2050, reversing the impact of the pandemic on them and investing in their future should be a top priority for governments. Otherwise, these groups will represent not just a lost generation, but rather many lost generations.
Keywords: Pandemic, COVID-19, crisis, Human capital, COVID-19 effects/consequences, response

According to recent studies, such as the World Bank report, COVID-19 and the pandemic crisis triggered a massive erosion of human capital at critical moments in the life cycle, derailing development for millions of children and youth, particularly in low- and middle income countries. Analysis of global data on young people who were under 25 at the start of the pandemic highlighted the impact of the pandemic on young people at key developmental stages: early childhood (0-5 years), school age (6-14 years) and youth (15-24 years). It was revealed that today's students could lose up to 10% of their future earnings due to the educational disruptions caused by COVID-19 and that the cognitive deficit of today's toddlers could be translated into a 25% drop in their earnings when they become adults.
Human capital - the knowledge, skills and health that people accumulate over their lifetime - is the key to unlocking a child's potential and enabling countries to achieve resilient recovery and strong future growth. However, the pandemic has closed schools and workplaces and disrupted other essential services that protect and promote human capital, such as maternal and child care and job training.
Due to the pandemic, preschool children lost a lot of their preschool education and experienced a lot of food insecurity during the pandemic. In addition, school-age children lost, on average, about thirty-two days of learning for every thirty days of school closures. This is because school closures and ineffective distance learning measures taken, made students miss out on learning and also forget what they had already learned. As a result, learning poverty has further increased. In addition, COVID-19 has dealt a heavy blow to youth employment. It is estimated that at the end of 2021, 40,000,000 people did not enter the labor market due to the pandemic, exacerbating youth unemployment trends. Also, youth earnings shrank by 15% in 2020 and 12% in 2021.
The window for addressing delays in human capital accumulation is small, as gaps in the early stages of the life cycle tend to widen over time. Without long-term targeting, the pandemic will further deepen poverty and inequality.
In this frame, this study focuses on policy options available in order to reverse the consequences of the pandemic on today's youth, underlining the priority of the issue for the future development of all countries. Given that today's under-25s, those most affected by the erosion of human capital, will make up more than 90% of the most productive-age workforce in 2050, reversing the impact of the pandemic on them and investing in their future should be a top priority for governments. Otherwise, these groups will represent not just a lost generation, but rather many lost generations.
Keywords: Pandemic, COVID-19, crisis, Human capital, COVID-19 effects/consequences, response

Current MAC (Medium Access Control) protocols for data collection scenarios with a large number of mobile nodes that generate bursty traffic are typically based on low-power listening (LPL) for network synchronization and FSA (Frame Slotted ALOHA) as the channel access mechanism. However, FSA has a MAC layer efficiency bounded to an upper limit of 36.8% due to contention effects, which reduces packet throughput and increases energy consumption. In this paper, we target such scenarios by presenting LPDQ (Low-Power Distributed Queuing), a highly efficient and low power MAC protocol. LPDQ is able to self-schedule data transmissions, acting as a FSA MAC under light traffic and seamlessly converging to a TDMA (Time Division Multiple Access) MAC under congestion. The paper presents the design principles and the implementation details of LPDQ using low-power commercial radio transceivers. Experimental tests demonstrate a MAC efficiency close to 99% which is independent of the number of nodes in the network and is fair in terms of resource allocation to nodes.

Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.