Learning-Based Coordination Model for On-the-Fly Self-Composing Services Using Semantic Matching

Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage IoT, exploit devices capabilities (sensing, acting), the data they locally store at different time and geographic locations, and arise from the spontaneous interactions among those devices. Thanks to a learning-based coordination model, and without any pre-designed composition, reliable and pertinent spatial services dynamically and fully automatically arise from the self-composition of available services provided by connected devices. In this paper, we show how we extended our learning-based coordination model with semantic matching, enhancing syntactic self-composition with semantic reasoning. The implementation of our coordination model results in a learning-based semantic middleware. We validated our approach on various experiments: deployments of the middleware in various settings; instantiation of a specific scenario and various other case studies; experiments with hundreds of synthetic services; and specific experiments for setting up key learning parameters. We also show how the learning-based coordination model using semantic matching favours service composition, by exploiting three ontological constructions (is-a, isComposedOf, and equivalentTo), de facto removing the syntactic barrier preventing pertinent compositions to arise. Spatial services arise from the interactions of various objects, provide complex and highly adaptive services to users in seamless way, and are pertinent in a variety of domains such as smart cities or emergency situations.

Programmable MAC in Body Area Networks, One Command at a Time

The discussion concerning programmable MAC in Body Area Networks (BANs) is due to the demand for simple and low-power sensor nodes. Additionally, the diverse applications in BANs require low-level modifications to support adaptive services or custom functions. In this work, we propose a novel scheme for programmable MAC, which requires a minor modification to the beacon frame of IEEE 802.15.6-2012. Specifically, our main contribution is the attachment of a command to the beacon, which is broadcasted at the beginning of each superframe by the hub. The hub requests an action, typically a modification of a MAC capability field, by the nodes with a metric which satisfies a constraint. Thus, one command at a time, the proposed scheme is applied with a negligible overhead. Two adaptive use cases, based on signal strength, are implemented to demonstrate this scheme. Firstly, the hub requests the nodes with high signal strength to enable relay support and secondly, the hub requests the nodes with low signal strength to set a sleeping pattern. In the first case packet delivery increases significantly, while in the second case each node saves an amount of energy.

Programmable MAC in Body Area Networks, One Command at a Time

The discussion concerning programmable MAC in Body Area Networks (BANs) is due to the demand for simple and low-power sensor nodes. Additionally, the diverse applications in BANs require low-level modifications to support adaptive services or custom functions. In this work, we propose a novel scheme for programmable MAC, which requires a minor modification to the beacon frame of IEEE 802.15.6-2012. Specifically, our main contribution is the attachment of a command to the beacon, which is broadcasted at the beginning of each superframe by the hub. The hub requests an action, typically a modification of a MAC capability field, by the nodes with a metric which satisfies a constraint. Thus, one command at a time, the proposed scheme is applied with a negligible overhead. Two adaptive use cases, based on signal strength, are implemented to demonstrate this scheme. Firstly, the hub requests the nodes with high signal strength to enable relay support and secondly, the hub requests the nodes with low signal strength to set a sleeping pattern. In the first case packet delivery increases significantly, while in the second case each node saves an amount of energy.

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

Cultural essentialism and tailored hotel service for Chinese: the moderating role of satisfaction

Purpose The purpose of this study is to understand Chinese consumers’ responses to ethnically tailored hotel services from the theoretical perspective of cultural essentialism. Design/methodology/approach Data collection was conducted through an online survey with Chinese respondents. Hierarchical moderated regression was performed to analyze the data. Findings The results show a positive relationship between cultural essentialism and consumer responses to hotel services that are tailored to their culture. Furthermore, the findings show that prior service satisfaction does not only positively influence the consumer responses, but also amplifies the link between cultural essentialism and the consumer responses. Practical implications Hoteliers are recommended to consider the cultural essentialism of Chinese consumers when adaptive services are introduced. Hotel services that are tailored to Chinese culture is a viable strategy if most Chinese customers are cultural essentialists. Originality/value This study adds knowledge to the hospitality scholarship by introducing cultural essentialism and demonstrating its role in influencing consumer preferences for familiarity as opposed to exotic hotel services. Furthermore, the moderating role of service satisfaction extends the consumer behavior literature.

Developing Self-Adaptive Service-Oriented Mobile Applications: A Framework Based on Dynamic Deployment

Today’s society is increasingly dependent on the use of mobile or smart devices (e.g. smartphones, tablets, hybrid devices, smart-TVs, smart-watches, among others), which have changed over these last 10 years the way people perform their daily tasks. In parallel, Internet of Things (IoT) systems have played a prominent position in this scenario, since they enable to exchange information among different types of devices and services (e.g. physical devices, vehicles, home appliances, sensors, among others). This scenario has boosted the demand for development of high-quality Mobile Applications (MobApps), which can be pre-installed on such devices during manufacturing platforms, or delivered as applications by the mobile stores or third parties. According to [1–3], mobile devices have some physical limitations (e.g. processing and storage) compared to personal computers. Thus, service-oriented MobApps have been a feasible alternative to overcome such limitations, improving the efficiency of the development life cycle of these applications with adoption of third-party components (e.g. software components, web services, and other mobile applications). In another perspective, it is also been noted a change in the user behavior of MobApps, which demand applications capable of operating in adverse conditions, maintaining their integrity of execution. Considering the relevance of such applications, this paper reports the extension of a framework to support the development of Self-adaptive Services-oriented MobApps (Self-MobApps), which enables modification of services at runtime [4] by means of a deployment dynamic approach. To show the feasibility of our framework, a case study for a smart restaurant was conducted in a mobile environment. The results of this study enable us to create a positive perspective on the contribution of our framework to the research communities involved.

Context-Aware Systems for Chronic Disease Patients: Scoping Review (Preprint)

BACKGROUND Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work. OBJECTIVE This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures. METHODS A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients’ health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted. RESULTS The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results. CONCLUSIONS The use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.