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In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

Vaccination is one of the effective programs to combat the devastating effects of COVID-19. These vaccines are handled carefully and monitored from their suppliers down to the vaccination sites, preserving the quality. Current vaccine tracking device implementation needs the opening of the vaccine carrier, which leads to the risk of exposure to unwanted light and temperature. This paper aims to design and develop a device for vaccine carriers to aid health workers in monitoring vaccines without exposure to unwanted conditions. The development was done working closely with target end users and made iterations before making the monitoring device available. The monitoring device attached to the off-the-shelf ice box used an ESP32 development board with sensors programmed in Arduino IDE. The device has undergone 8-hour and 24-hour temperature tests. The designed monitoring device on the test could record an acceptable temperature range for 22 hours and 26 minutes. The results indicate that the monitoring device can run reliably, even for an extended period. The off-the-shelf ice box used with the monitoring device can be a viable alternative to the scarce and expensive WHO-accepted vaccine carriers. The monitoring device can also be used with other vaccines if the temperature requirement ranges from 2°C to 8°C. [ABSTRACT FROM AUTHOR]

Industry 4.0 fuses ubiquitous computing and leads to a culmination of physical, digital, and biological paradigms. Previously inanimate objects are enabled to "talk" to each other and to the users, and guide specific behaviours of speed, breadth, depth, and systems impact. Research suggests however that 70% of large-scale technological change programs don't reach their stated goals. Current theories and methodologies including Technological Transitions (TT), Digital Transformation (DT), Technology Acceptance Models (TAM, TAM2 & UTAUT), Multi-Level Perspectives (MLP), come with challenges and limitations. Management and key decision makers need new tools that will assist them to identify which factors their companies must consider to efficiently adapt to Industry 4.0. The aim of this paper is to address existing TAM, DT and MLP challenges, and propose an Industry 4.0 specific digital transformation model, involving business and management practices. Ideas, best practices and different prospective, from diverse markets and industries, will be mitigated and combined. The synergetic methodology will support management, enhance organizational problem-solving capabilities, and provide recommendations to formulate a strategic approach based on company lifecycle, corporate governance, enterprise culture, change management, company size and structure. Key lessons learned will help in forming evolutionary perspectives specific to the needs and challenges of Industry 4.0 transitions. [ABSTRACT FROM AUTHOR]

Ubiquitous networks comprise a diverse set of homogeneous or heterogeneous networks to perform ubiquitous computing operations. In the real-time application context, users can access data from any place and at any time by using mobile devices/nodes such as computer/laptop, mobile, tablet and so on. Nowadays, mobile computing paradigms have an impact on the spread of mobile computing devices, resulting in the transmission of data across ubiquitous networks. To avoid uncertainty, it is critical to maintain data consistency across all devices. This research work has proposed a context aware data synchronization using cognitive agents as a case study on shopping environments. The proposed data synchronization scheme detects network changes, resolves any potential conflicts, and updates various synchronizing devices with different device attributes. Cognitive agents are used to track the data across multiple devices. Context aware ubiquitous networks respond to customer requests based on their personal profiles, which often include individual preferences while considering the technological restrictions of the mobile device and the context of operating environment. The proposed work is compared with other existing methods in terms of data accuracy, data synchronization time, and computation and communication overhead. [ABSTRACT FROM AUTHOR]

The evolution of knowledge and information has changed the practices of health services over the years. The use of Information systems (IS), with the underlying Information Technology (IT), plays a vital role in managing medical information and associated resources, always to provide a better quality of health care. IS/IT is increasingly developing globally, with new features and tasks greatly expanded, leaving paper records and voluminous files behind. The ubiquitous information and computing resources increasingly allow the sharing of all types of data between various entities. New IS/IT will have to be thought to correspond to the new types of monitoring and analysis of biomedical data. This work intends to explore the IS/IT in health and its evolution over the last few years. [ABSTRACT FROM AUTHOR]

The modern tactical network demands a highly dynamic communications environment that involves autonomous platforms and systems. In order to achieve the full potential of modern technologies, it is critical to ensure the critical data finds the optimum path at the right time in a highly aggressive environment employing secure and best possible available communication path or network resources. Moreover, Contextual information aware optimal communication, such as the power of the radios, battery level, distance among the radios, and maximum and minimum data rates of the radio aware optimal path selection, remains an unresolvable research area. The mentioned limitation engenders various complex challenges in mission-critical networks. Therefore, contextual information-aware optimal communication in radio networks is a priority demand raised as a primary objective for military communication research. In this paper, we have performed a comprehensive review of various proposed methodologies to achieve the objective, followed by a critical comparative analysis to anticipate the state of the mentioned research area that leads to a future research direction for military researchers. [ABSTRACT FROM AUTHOR]

Information systems are continuously evolving in nature and complexity. In the healthcare environment, information and information exchange are critical for providing health care at all levels. Hence, the healthcare environment is particularly relevant when discussing IT infrastructure monitoring and disaster prevention since availability and communication are vital for the proper functioning of healthcare units, whether acting in isolation or on a network. This work focuses on understanding what comprises a good monitoring solution, analyzing the monitoring solutions currently available in the optics of a multi-location healthcare environment and, finally, proposing a pervasive and comprehensive conceptual architecture for a monitoring system that is capable of handling such environments. [ABSTRACT FROM AUTHOR]

Researchers and practitioners in various fields, including e-commerce customization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management, have realized the value of contextual information. Context-aware recommender systems assist users in finding their chosen material in a reasonable amount of time by utilizing information that describes the scenario in which the items will be consumed. For better personalized user recommendation, recommender systems leverage the contextual information in their process of recommendation called context-aware recommendation. Classification is used for context-prediction which represents the prediction of future context based on recorded previous context. The context prediction algorithm's goal is to recognize typical behavior patterns that have been seen in the past and then offer the most likely continuation of a presently observed collection of context components based on this knowledge. In this article we study the correlation between the multi-class classification and the context-aware recommendation.With this correlation we conclude that the linkage between contextual information and classification enhance and improve the recommendation results. [ABSTRACT FROM AUTHOR]

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today's standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research. [ABSTRACT FROM AUTHOR]

The emergence of ubiquitous computing, especially with the Internet of Things (IoT), releases new prospects to traditional information systems by merging new technologies and services for seamless access to information sources at anytime and anywhere. Concurrently, this emergence opens new threats to information security and new challenges to control access to the resources. To ensure security, several techniques have been employed, and access control (AC) is one of the essential security requirements for IoT and non-IoT systems. Various authentication and AC methods are proposed to enforce AC policy and to prevent any unauthorized access to logical/physical assets. The continuous technology upgrades and the diversity of AC models force the need to find AC metamodels with higher level of abstraction that serve as a unifying framework for specifying any AC policy. AC metamodels are proposed to encompass AC features and are used to derive various instances of AC models and methods. In this paper we review the proposed AC metamodels and their implementation scenarios, we analyze them, their objectives, their limitations, and present open research questions and issues that still need to be addressed. [ABSTRACT FROM AUTHOR]

With the widespread of data, applications, and devices in today's dynamic computing environments, controlling access to assets from multiple sources is a key challenge, especially with the presence of cybercriminals and cyberattacks. Several access control (AC) models are developed and implemented in different computing environments to control users' access to resources. But, the emergence of ubiquitous computing, especially the concept of industry 4.0 and IoT applications, releases new prospects to traditional information systems by merging new technologies and services for seamless access to information sources at anytime and anywhere. With this fact, it is realized that these AC models no longer meet the increasing demand for privacy and security standards. Hence, several AC metamodels with higher level of abstraction are developed as unifying frameworks for specifying any AC policy. Unfortunately, the proposed AC metamodels have several limitations. One of these limitations is that they are not generic enough to include all features and the heterogeneous AC models. In this paper we propose a solution for this limitation by developing a generic AC metamodel where its features can be upgraded to answer the needs and facts of the new technologies. [ABSTRACT FROM AUTHOR]

Physiological response is an automatic reaction that triggers a physical response to a stimulus such as stress, emotion, pain, etc. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), measures changes in perspiration by detecting the changes in electrical conductivity of skin. Previous studies have already shown that EDA is one of the leading indicators for a stimulus. However, the EDA signal itself is not trivial to analyze. To detect different stimuli in human subjects, variety of features are extracted from EDA signals such as the number of peaks, max peak amplitude, to name a few, showing the prevalence of this signal in bio-medical as well as ubiquitous and wearable computing research. In this paper, we present an open-source Python toolkit for EDA signal preprocessing and statistical and automatic feature extraction. To the best of our knowledge, this is the first effort for developing a versatile and generic tool to extract any number of automatic features from EDA signals. We evaluate our toolkit using different machine learning algorithms applied to the Wearable Stress and Affect Detection (WESAD) dataset. Our results show higher validation accuracy for a stress detection task using the the features automatically extracted by pyEDA. [ABSTRACT FROM AUTHOR]

Aging in place happens when people age in the residence of their choice, usually their homes because it is their preference for living as long as possible. This paper introduces the implementation of a computational platform to support active aging in place with a particular focus on the caregivers and their requirements to accomplish their tasks with engagement and comfort. The platform is supported by IoT, using low-cost technology to increment the platform modularly. It is a modular platform capable of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding the person under supervision, as well as providing conditions for constant and more effective monitoring, through modules and tools that support decision making and tasks for active living. The paper describes the platform principles and its modular architecture. Preliminary results are presented in the form of an experimental installation scenario. [ABSTRACT FROM AUTHOR]

Adaptation of smart devices is frequently rising, where a new generation of smartphones is growing into an emerging platform for personal computing, monitoring, and private data processing. Smartphone sensing allows collecting data from immediate environments and surroundings to recognize human daily living activities and behavioral contexts. Although smartphone-based activity recognition is universal; however, there is a need for coinciding recognition of in-the-wild human physical activities and the associated contexts. This research work proposes a two-level scheme for in-the-wild recognition of human physical activities and the corresponding contexts based on the smartphone accelerometer data. Different classifiers are used for experimentation purposes, and the achieved results validate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

An Intelligent Museum built under the Ubiquitous Computing paradigm, where services follow the user seamlessly across different environments, emerges as a new type of digital institution present beyond its physical limits: the Ubiquitous Museum. Mixed Reality provides the means of harvesting knowledge from memory institutions and smart devices connected to the Internet of Things. At the same time, it enables embedding multimodal contents from the Museum across the Intelligent Environment. We research the user experience of knowledge dissemination through the Ubiquitous Museum. We exemplify how our work aims to help achieve this. We also discuss how using embedded Museum contents helps sustainable development. [ABSTRACT FROM AUTHOR]

Navigation in unfamiliar places is a big challenge for partially sighted and visually impaired people. Improving visual information on the location and content of objects such as drugs can help navigation in unfamiliar environments. There are several existing navigation solutions capable of helping these people. However, navigation solutions are rarely adopted and implemented in reality. In order to optimize the perception of digital information of objects from sensors and to naturally interact with the pervasive computing landscape, Augmented Reality (AR) equipment has to be seamlessly integrated into the user's environment. For this purpose, we develop an architecture of text and objects recognition based on AR in order to assist partially sighted and visually impaired people. Such architecture makes navigation easier in the environment by helping users to find drugs and to verify the number of pills using speech. The proposed architecture uses context-aware mobile computing and shows great potential to integrate AR for object recognition through AR engines. [ABSTRACT FROM AUTHOR]

Aiming at the lack of regional overall planning and reasonable construction schemes in the development of the Ubiquitous Power Internet of Things, based on the form and planning boundaries of the current development, this paper proposes a planning about "aiming at one goal, planning six areas and creating eighteen key projects". About the 18 key projects, the overall technical architecture are about six major areas, including "application innovation, platform construction, communication network, information perception, security protection, business development". And the planning implementation path is "Energy business datamation, Energy data servitization, Energy service valuation, Energy value ecological cultivation, Energy ecological business updation ". They provide technical framework and planning proposal suggestions for regional Ubiquitous Power Internet of Things planning and construction. [ABSTRACT FROM AUTHOR]

This paper examines the predictive value of peer's observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor's data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant's smartphone, and peer-derived features (pDFs) from the peer's assessments. We evaluated the pDFs on a binary classification task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classification accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classification accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The findings are encouraging about the incorporation of peer's observations in machine learning with potential benefits in the fields of personal sensing and pervasive computing, especially for mental health and well-being. [ABSTRACT FROM AUTHOR]

Mobile technologies are becoming more ubiquitous and multifaceted in today's society. The rise of smart mobile devices like mobile phones, tablets, etc. has sparked a revolution in mobile learning paradigm. Educational researchers have started exploiting the potential and scope of smart mobile technologies in education. The mobile technology embraced by new smart pedagogies, can disrupt the notions of engineering education. We have examined the nature of several smart pedagogies in engineering education and explored the way they intersect with the mobile pedagogies. We have unpacked philosophies of modernization and disruption. We have then discussed smart mobile learning activities for engineering students, together with the pedagogical principles supporting them. We have also outlined the ways to implement 'feasible disruptions' through the smart mobile pedagogies. [ABSTRACT FROM AUTHOR]

In vehicular networks, wireless communications between smart vehicles can make a highway travel safer and faster. But hackers can use the system to cause accidents. To avoid this problem and guarantee ubiquitous public safety on the roads, vehicular networks need an appropriate security mechanism to protect them from various malicious attacks. In this paper, we present an intrusion detection system available to detect internal malicious nodes. When an accident appears on the road, the vehicles must have information about this. But the existence of malicious nodes, this information will be deleted from the network. Because of this, we have adopted a mathematical model based on coalition and signaling game theory to design an Intrusion Detection Game (IDG) modeling the interaction between malicious nodes and the Coalition Head that equipped with Intrusion Detection System (CH-IDS) agent, and seek its Bayesian Nash Equilibrium (BNE) for the optimal detection strategy. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to quantify and highlight the effects of how contextually guided gossip in ad hoc dynamic vehicular networks during emergency situations perform. Two traffic scenarios are simulated, each one of the scenarios was simulated with the effect of contextual filtering and without any filtering (broadcast gossip). Our environment assumes the minimum specifications available, which is the presence of an on-board unit only in each car. We also analyze the effect of applying such filtering and if it is worth applying in such a critical environment that has human lives at stake. The simulation results show that our proposed concept could be applied safely without much affecting the time that the gossip takes to reach other vehicles. The amount of redundant and unneeded messages transmitted is reduced drastically with higher road traffic. [ABSTRACT FROM AUTHOR]

Stress levels among people rise through the years, and passive sensing data from mobile phones or other ubiquitous devices have started to found its place in applications of mental health observation. With the ultimate goal of creating an automatic human mental health assistant that helps people to have a better mental condition, a step is taken by creating a stress recognition model. In previous works, the researchers have found correlations between sensor data and mental health conditions. They attempted to predict the stress level by different data sources. Due to there is no direct link between any sensor data with mental health, Machine Learning algorithms are employed to uncover relations with multiple sensors and mental well-being. The utilized machine learning algorithms work with non-sequence data hence the researchers need to extract features that represent historical sensor data as one value features. However, extracted features cannot completely represent time-varying sequence data. Within the scope of this study, we demonstrate that LSTM, CNN and CNN-LSTM algorithms, which accept sequence data as input can also work in passive mobile phone sensor data to predict human mental stress. The performance of the model on StudentLife dataset that includes passive mobile sensing data and stress feedbacks of college students has 62.83% accuracy on 460 test instances by training with 800 instances with LSTM model. The diversity of the data is limited and the size is tiny for the data-hungry LSTM model. Consequently, the model could not generalize on adapted features with the small sample size. Although we did not adapt complex features, the results encourage us to improve data size and continue to research on this topic. [ABSTRACT FROM AUTHOR]

Abstract High confidence in the safe operation of autonomous systems remains a critical hurdle on their path to becoming ubiquitous. Recent accidents of Uber and Google driverless cars illustrate the difficulty ahead. Leading collision avoidance framework for autonomous systems fail to properly capture and account for the high variability of geometries, shapes, and sizes of the agents (e.g., 18 wheels truck vs. 4 doors sedan), capabilities that are critical in situations with high risk of accident (e.g., intersection crossing). We introduce a simple and efficient multi-agent collision avoidance framework for Autonomous Vehicles (AV) in various collision configurations (i.e., glancing, away, clipping). Machine learning techniques are proposed to properly train the autonomous systems involved. Vehicle-to-Vehicle (V2V) communication technologies and shape-based spatial-temporal collision avoidance algorithms are leveraged to ensure the accurate prediction of the collision and correct decision on the appropriate steps to avoid its occurrence. A prototype implementation and simulation is currently under development for a clipping collision problem at a lightless intersection crossing using the AirSim platform. [ABSTRACT FROM AUTHOR]

Security of dig data applications is paramount, and authentication plays an important aspect in cloud computing and ubiquitous computing. Trust management has been identified as vital component for establishing and maintaining successful relational exchanges between trading partners in cloud and ubiquitous systems. Establishing trust is the way to build good relationship with both client and provider which positive activates will increase trust level, otherwise destroy trust immediately. We propose a new context-aware authentication system using a trust management system between client and server, and between servers, a trust which induces partnership, thus to a close cooperation between these servers. We defined the rules (algorithms), as well as the formulas to manage and calculate the trusting degrees depending on context, in order to uniquely authenticate a user, thus a single sign-on, and to provide him better services. [ABSTRACT FROM AUTHOR]

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smart-phone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user’s personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications. [ABSTRACT FROM AUTHOR]

The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applications, visual recognition systems which encompasses image classification, localization and detection have achieved great research momentum. Due to significant development in neural networks especially deep learning, these visual recognition systems have attained remarkable performance. Object detection is one of these domains witnessing great success in computer vision. This paper demystifies the role of deep learning techniques based on convolutional neural network for object detection. Deep learning frameworks and services available for object detection are also enunciated. Deep learning techniques for state-of-the-art object detection systems are assessed in this paper. [ABSTRACT FROM AUTHOR]

Context aware applications are seeking for the technologies that provides regularly available data between internal and external environments. Wireless access infrastructure is integrating real time data and heterogeneous devices to establish ubiquitous communication. A wireless sensor network (WSN) is primarily undertaking remote monitoring of any task in any sensing field further to extend for context fetching and refining to collect essential context information for pervasive environment. The applications are well utilized on context observation and interpretation for mobile users. The proposed method assists to combine various basic low level contexts to provide reliable decision making in the pervasive environment using sensor networks and context aware computing. Pervasive computing has powerful and influencing factors to establish dynamic and smart environment with its dominant features in the backend. Pervasiveness provides the regulated smart interactions and interfaces together to avail the information for various applications. Using rich context for event notification than simple position and time, aim to bring automated decision along with accuracy information. The proposed model also facilitate users and group members to create and submit an event. It provides ability to submit an event using a various mode of input. [ABSTRACT FROM AUTHOR]

In this article, we proposed a framework of the intelligent indoor plant factory system. In the proposed system, we will plant with the natural agricultural cultivation, that is, no fertilizers and pesticides in our system. The proposed system includes four modules. They are the setting up system, the collecting data system, the transmitting data system, and the analysis of data systems. We introduce the big data and artificial intelligence technologies to achieve the best plant growth environment. [ABSTRACT FROM AUTHOR]

With the emergence of ubiquitous sensing technologies, it is now possible to continuously monitor users during their everyday activities in order to provide personalized feedback and interventions. For example, fitness trackers can count steps and motivate users to keep active. With their rich set of sensors, smartphones are also capable of monitoring user behavior such as physical activity and location. Smartphones’ inertial sensors have been used to recognize different types of activities. Usually, statistical sets of features from time and/or frequency domain are extracted from the raw signals to train machine learning models. The final performance of the system will depend on the set of features which needs to be defined by the researcher. In this work, we propose a method based on recurrence plots’ distance matrices and convolutional neural network (CNN) that does not require feature engineering. A recurrence plot is a visualization of the recurrent states of a dynamical system. For the activity recognition task, the raw acceleration signal is transformed into an image-like representation of recurrent states and a CNN is then trained with those images. The results show that this method is able to achieve better results than a feature based approach in terms of accuracy and recall. [ABSTRACT FROM AUTHOR]

Nowadays, the world’s population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized course content in ubiquitous learning, considering learning styles and context-awareness. The proposed approach aims to support learners by presenting course materials generated by an adaptive engine based on adaptation rules. [ABSTRACT FROM AUTHOR]

Confidentiality is among the most important obstacles for widespread adoption of cloud services. In fact, researchers have invented a new promising form of encryption, homomorphic encryption, that can be considered as an effective way to overcome these obstacles. Since cloud computing environment is more threatened by attacks and since cloud consumers often use resource-constrained devices to access to cloud computing services, the homomorphic encryption schemes must be promoted, in terms of security level and running time, to work efficiently. At EMENA-TSSL’16, we boosted the standard RSA encryption scheme at security level, Cloud-RSA. In this paper, we propose a new fast variant of the Cloud-RSA scheme to speed up its algorithms. The proposed variant uses a modulus of the form N = p r q s for r, s ≥ 2 and employs Hensel lifting and Chinese remaindering to decrypt. Simulation results show that the proposed variant gives a large speed up over the Cloud-RSA scheme while preserving a prescribed security level. [ABSTRACT FROM AUTHOR]

Proxemic dimensions can help mediate interactions between entities (people, objects) in Internet of Things environments and constitute an important element in the construction of proactive, context-aware and human-centric systems. However, as evidenced by the literature, little attention has been paid to implementing proxemic interactions in IoT platforms. We present ProximiThings, a framework for facilitating the incorporation of proxemic capabilities in IoT-based systems. We describe the construction of various prototypes to show how ProximiThings can be used by developers in real settings. [ABSTRACT FROM AUTHOR]

In a new smart-world, users are getting accustomed to fast-responding applications that make their everyday tasks and daily life easier. In order to meet their expectancies, mobile applications are shifting towards a new era of context awareness. Nonetheless, it seems that context-aware applications are still struggling to provide the user with a real situation understanding. They only consider non-evolving limited scenarios and react to them using only generic services. To address these concerns, we have developed the Long Life Application, a dynamic context-aware situation-based distributed mobile application dedicated to assist end-users in their everyday needs. This application considers the requirements of the users and provides them with the appropriate services according to their current context. In this paper we focus in the way that the user’s context is considered by the application. We propose a hybrid approach that combines both high-level context (top-down approach), by injecting user-related context, and a low-level context (bottom-up approach), by inferring it from sensor data. [ABSTRACT FROM AUTHOR]

Smartphones are ubiquitous devices that enable users to perform many of their routine tasks anytime and anywhere. With the advancement in information technology, smartphones are now equipped with sensing and networking capabilities that provide context-awareness for a wide range of applications. Due to ease of use and access, many users are using smartphones to store their private data, such as personal identifiers and bank account details. This type of sensitive data can be vulnerable if the device gets lost or stolen. The existing methods for securing mobile devices, including passwords, PINs and pattern locks are susceptible to many bouts such as smudge attacks. This paper proposes a novel framework to protect sensitive data on smartphones by identifying smartphone users based on their behavioral traits using smartphone embedded sensors. A series of experiments have been conducted for validating the proposed framework, which demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Energy conservation is an important measure to protect the environment and conserve resources. To reduce power consumption of appliances, automatic and remote controllable smart meter has become one of the most advantageous means for power energy management. By integrating several internet-of-things (IoT) technologies, this paper proposes a ubiquitous power management system, which comprises a monitoring, ubiquitous controlling, and power management modules. In the monitoring module, multiple sensors are integrated to detect ambient information, such as temperature, humidity, and foreground moving objects. For real-time video surveillance, we develop a fast foreground segmentation algorithm using parallel computation. In the ubiquitous controlling module, the sensed datasets as decision making basis are reported to smart meters via a wireless network. Based on both environment perception and remote controlling signals, the power management module utilizes the smart meter to operate appliances automatically by specifying operation parameters or switching power supply. The proposed system was tested in a building and reduced around 35.7% power consumption, which show a significant portability and effectiveness in IoT. [ABSTRACT FROM AUTHOR]

Services on pervasive computing are delegated or offloaded to cloud computing platforms, because pervasive computing devices have only limited resources. However, access control models in pervasive computing environments and cloud computing platforms are different, where the former tends to be context-dependent and the latter to be subject-based, e.g., role-based access control (RBAC). To connect such different models, we propose a framework for managing context-aware services executed at pervasive computing and cloud computing. The framework is constructed as a world model for specifying contextual information in the real world. This paper describes the design and prototype implementation of the proposed model. [ABSTRACT FROM AUTHOR]

Nice (July 14 2016), Berlin (December 19 2016), London (March 22 2017) and Stockholm (April 07 2017) – these four European metropoles are marked by a common occurrence burnt deep into the souls of all European citizens: recent terror attacks made by cars injuring several accidental people passing by. These attacks shall cause fear across Europe where state security bodies and units are trying to avoid those. Yet, there is already the technology to enhance the security of all people reinforce the effort aiming to prevent such accidents or car assaults: the autonomous emergency brake assist. This paper aims to open discussion about the (im)possibility of integrating the autonomous emergency brake assist in all cars and how it would impact such attacks. [ABSTRACT FROM AUTHOR]

Recently, with the advances of information and electrical technologies, Internet of Thing (IoT) has moved from being a far vision to an increasing market reality. IoT promises to connect all physical “things” via a dynamic global network infrastructure and create intelligent behaviors in different contexts without human manipulations. In line with this development, the number of IoT solutions also have augmented exponentially covering many domains from health care, urban management to life quality improvement and so forth. However, separate appearances of these solutions today has led to barriers in enlarging smart environments because they lack a mechanism for integration. It is well-known that in the very near future, IoT requires the capabilities of high scale and dynamic adaptation with linking many new and existing smart contexts together. Hence, the need of having a comprehensive management method for the large-scale IoT environments is urgent at the time. In this paper, we focus on designing an overlay management architecture based on Chord algorithm to manage things in heterogeneous space. The architecture is a collection of multiple peer Chord rings, and thus each of these rings is considered as a smart context. We also bring forward operation mechanisms for the architecture such as thing identification, key insert/remove, key lookup and node joining/leave. Our proposed architecture is simulated and evaluated with diverse properties. The gained results showed the feasibility of our approach in reality meeting scalability, robustness and maintainability requirements applying to manage and control cyber-physical systems. [ABSTRACT FROM AUTHOR]

Abstract: Ambient environments involve a variety of smart devices. Communities of these devices form a ubiquitous information processing system to which the physical environment should provide an intuitive and flexible user interface. Indeed, due to the proliferation of the number of services in ubiquitous environments, the problem of dynamic service composition becomes a complex task. Our work tries to solve the problem of service composition based on AI planning. The approach used in our framework is based on a temporal reasoning system called Event Calculus. This formalism is used to generate an action plan to a symbolic level from a composition plan described in XML modeling the behavior of the composition of services. Event Calculus has a double interest to propose a solution to the problem of temporal persistence and to provide a usable technique for planning. The framework for service composition using Event Calculus includes mechanism for the service selection, based on an estimation of the reputation for abstract services and the Quality of Service (QoS). [ABSTRACT FROM AUTHOR]

Abstract: Timely loss-of-contact detection between devices in pervasive systems using mobile networks is an important aspect for both functionality and user experience. Traditional schemes where time-to-live is defined by the service provider are not adequate in mobile networks where communication failures are frequent. On the other hand, schemes using keep-alive messaging tend to increase the communication overhead, in particular if the time to detect a loss of contact needs to be short. We introduce a strategy where the time between keep-alive messages is adjusted dynamically according to the need in a particular application. Our solution was evaluated with usage data from a professional application built on the PalCom framework. The measurements show a 96 % decrease of the communication overhead while still maintaining the same system responsiveness. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, the heterogeneity of interconnected things and communication technologies creates several small worlds composed of a single object and a smartphone. For each object, the user needs to download a specific application, search and connect the device. The result is a waste of valuable resources: several objects are able to communicate with the smartphone, but they cannot directly interact among them. In this paper, we propose a model that can be used to define a set of standard interfaces suitable for every smart object. Devices that adhere to the same model can be easily controlled and placed in relation among them, creating multi-object behaviors for a smart space. The smartphone is still a control center, but with a single application it is possible to control and personalize spaces in a holistic way, instead of using the traditional one-to-one approach. Moreover, personalization should be portable: it is desirable that a behavior works in as many smart spaces as possible, at least in a similar way as it does in the environment in which it was configured, freeing the user from the tedious task of adapting it manually every time s/he goes to another space. A portable personalization extends the bring your own device paradigm to a new “bring your own space” paradigm. The model is inspired in the object-oriented programming, reinterpreting features such as inheritance and polymorphism to the real world, so it is possible to provide a software system able to adapt existing behaviors to new spaces. The use of the model is exemplified in the paper with two examples of smart spaces. [ABSTRACT FROM AUTHOR]

Abstract: Context-aware applications need to react and adapt to changes in their computing environment, which is in general ubiquitous and inherently distributed. Therefore, the context communication faces the problem of context representations heterogeneity, the limitation of devices capacity and network bandwidth. In order to overcome this challenging issue, a particular management of the context is required either for its collection, update, and exchange. In this paper, we propose an approach to manage the context in ubiquitous environments based on the semantic representation of the context and its update using standard languages such as RDF and RDF(s). Our proposed approach provides the simplicity and flexibility that allow the developer to express the context without the need for a unified context model. The results of the experimentation carried out using a prototype implementation of a middleware supports the proposed approach, are in favour of using our approach in the case of resource-constrained devices and network. [ABSTRACT FROM AUTHOR]

Abstract: The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network paradigm which is providing various types of solutions to the community and the individual user in various sectors including environmental monitoring, entertainment, transportation, security, and healthcare. We explore and compare the features of wireless sensor networks and ubiquitous sensor networks and based on the differences between these two types of systems, we classify the security-related challenges of ubiquitous sensor networks. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges that need to be addressed to design more secure ubiquitous sensor networks in the future. [ABSTRACT FROM AUTHOR]

Abstract: RF signal is seemingly ubiquitous, these days. Majority of the devices that people are using today are continuously generating RF signals. Apparently, only a portion of these signals are being utilized and the greater part of it becomes wasted energy. However, these signals can be taken advantaged off. Scavenging techniques have raised to popularity because of its potential to convert ambient energy to usable electrical energy. It can be done by means of energy harvesting. Presented in this paper is a design of self-biased cross coupled rectifier that operates in Wi-Fi (2.4 GHz) frequency band which is suitable for Energy Harvesting Application. The design is implemented in 65nm CMOS Process Technology. It also introduces integrated dual threshold technique which further improved performance of the rectifier. At six (6) stages, the rectifier was able to extract 1.52 V usable DC voltage and attained a maximum power conversion efficiency (PCE) of 15.81%. [ABSTRACT FROM AUTHOR]

Abstract: Modeling people's behavior in e.g. travel demand models is an extremely complex, multidimensional process. However, the frequency of occurrence of day-long activity schedules obeys a ubiquitous power law distribution, commonly referred to as Zipf's law. 1 This paper discusses the role of aggregation within the phenomenon of Zipf's law in activity schedules. Aggregation is analyzed in two dimensions: activity type encoding and the aggregation of individual data in the dataset. This research employs four datasets: the household travel survey (HTS) NHTS 2009, two six-week travel surveys (MobiDrive 1999 and Thurgau 2003) and a 24-week set of trip data which was donated by one individual. Maximum-likelihood estimation (MLE) and the Kolmogorov- Smirnov (KS) goodness-of-fit (GOF) statistic are used in the “PoweRlaw” R package to reliably fit a power law. To analyze the effect of aggregation in the first dimension, the activity type encoding, five different activity encoding aggregation levels were created in the NHTS 2009 dataset, each aggregating the activity types somewhat differently. To analyze aggregation in the second dimension, the analysis moves from study area-wide aggregated data to subsets of the data, and finally to individual (longitudinal) data. [ABSTRACT FROM AUTHOR]