Showing papers on "Context awareness published in 2016"

Human Behavior Analysis by Means of Multimodal Context Mining.

Abstract: There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

Learning Graph-based POI Embedding for Location-based Recommendation

Abstract: With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). However, the extreme sparsity of user-POI matrix and cold-start issue create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Moreover, location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real-time manner. To address these challenges, we stand on recent advances in embedding learning techniques and propose a generic graph-based embedding model, called GE, in this paper. GE jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI-POI, POI-Region, POI-Time and POI-Word)into a shared low dimensional space. Then, to support the real-time recommendation, we develop a novel time-decay method to dynamically compute the user's latest preferences based on the embedding of his/her checked-in POIs learnt in the latent space. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its superiority over other competitors, especially in recommending cold-start POIs. Besides, we study the contribution of each factor to improve location-based recommendation and find that both sequential effect and temporal cyclic effect play more important roles than geographical influence and semantic effect.

CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments

Abstract: The proliferation of RF networks coupled with the diverse and growing set of mobile devices, opened the doors for a new class of context awareness through contact-free ambient sensing. Since our initial challenges paper in 2007, the field of device-free passive sensing has witnessed an exponential growth; covering areas such as intrusion detection, mobile healthcare, whole-home gesture recognition, traffic estimation, border protection, among others. In this talk, we give a holistic overview of the area of contact-free ambient sensing based on RF technology, highlighting how it evolved over a decade from binary-detection in controlled environments to commercial systems for border protection and smart homes. We also give insights about the current trends and possible future research challenges.

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Abstract: The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991–2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.

Advanced User Assistance Systems

Abstract: Information technology (IT) capabilities are increasing at an impressive pace, but users’ cognitive abilities are not developing at the same speed. Thus, there is a gap between users’ abilities and available IT. Handbooks or online help functions such as ‘‘F1 help’’ try to close this gap by providing explanatory information for the IT capabilities at hand. However, there is strong empirical evidence that traditional support structures are not as effective as intended (Sykes 2015); on the contrary, they distract users from their work (Barrett et al. 2004), which results in decreased efficiency and effectiveness as well as lower job satisfaction. Initial attempts to support users with more comprehensive integrated assistance functions failed miserably. A well-known example of such a dismal failure is ‘‘Clippy, the paperclip’’, a cartoon character developed by Microsoft that automatically popped up to assist users of Microsoft Office. However, instead of supporting the user with clear and precise guidance, studies show that Clippy ‘‘was considered to be annoying, impolite, and disruptive of a user’s workflow’’ (Veletsianos 2007, p. 374). In the end, Clippy, the ‘‘non-intelligent artificial intelligence assistant’’, was so despised that even Microsoft made fun of it. However, more recent attempts to support users, primarily in the private-life context (on mobile devices), are becoming more sophisticated and have been met with a more favorable response (e.g., Apple’s Siri and Google’s Google Now). Moreover, Microsoft has integrated its personal assistant, Cortana, into the latest version of the operating system Windows 10, which is available for private and business environments. One domain that is far more mature with regard to ‘‘user’’ support is the automotive sector. For more than 30 years there has been research into assistance systems that proactively support drivers (Bengler et al. 2014). Early driver assistance systems (DAS) only measured the parameters inside the car, for example with regard to vehicle stabilization (electronic stability control). Later on, sensors also captured the car’s external environment. The use of the collected data, navigation systems, adaptive cruise control, and parking assistance can assist drivers in avoiding hazardous situations and increasing driver comfort. Advanced DAS, considered to be the third phase of DAS evolution, are about to become commercialized as Accepted after three revisions by Prof. Dr. Sinz.

ISANA: Wearable Context-Aware Indoor Assistive Navigation with Obstacle Avoidance for the Blind

Abstract: This paper presents a novel mobile wearable context-aware indoor maps and navigation system with obstacle avoidance for the blind. The system includes an indoor map editor and an App on Tango devices with multiple modules. The indoor map editor parses spatial semantic information from a building architectural model, and represents it as a high-level semantic map to support context awareness. An obstacle avoidance module detects objects in front using a depth sensor. Based on the ego-motion tracking within the Tango, localization alignment on the semantic map, and obstacle detection, the system automatically generates a safe path to a desired destination. A speech-audio interface delivers user input, guidance and alert cues in real-time using a priority-based mechanism to reduce the user’s cognitive load. Field tests involving blindfolded and blind subjects demonstrate that the proposed prototype performs context-aware and safety indoor assistive navigation effectively.

ORACON: An adaptive model for context prediction

Abstract: Context prediction has been receiving considerable attention in the last years. This research area seems to be the next logical step in context-aware computing, which, until a few years ago, had been concerned more with the present and the past temporal dimensions. Most of research works related to context prediction employ the same algorithm for all cases. We did not find any approach that automatically decides the best prediction method according to the situation. Therefore, we propose the ORACON model. ORACON adapts itself in order to apply the best algorithm to the case. This adaptive behavior is the main contribution of this work and differentiates the proposed model of other related works. Furthermore, ORACON supports other important aspects of ubiquitous computing, such as, context formal representation and privacy. We have built a functional prototype that allowed us to conduct two experiments. The first experiment successfully tested the main functionalities provided by ORACON to support context prediction and privacy aspects. The test used context histories generated with a location database that contains 22 millions chekins across 220,000 users in the location sharing services Foursquare and Twitter. The second experiment assessed the adaptive feature of the ORACON. The test simulated the behavior of 30 users for a period of 30 days, using context histories generated through the Siafu simulator. This tool generates data for the evaluation and the comparison of machine learning methods in mobile context-aware settings. We concluded that ORACON chose the most accurate prediction algorithm in the simulated scenario, proving that the model reached the main contribution sought by this research.

Cost-Efficient Sensory Data Transmission in Heterogeneous Software-Defined Vehicular Networks

Abstract: Sensing and networking have been regarded as key enabling technologies of future smart vehicles. Sensing allows vehicles to be context awareness, while networking empowers context sharing among ambients. Existing vehicular communication solutions mainly rely on homogeneous network, or heterogeneous network via data offloading. However, today’s vehicular network implementations are highly heterogeneous. Therefore, conventional homogeneous communication and data offloading may not be able to satisfy the requirement of the emerging vehicular networking applications. In this paper, we apply the software-defined network (SDN) to the heterogeneous vehicular networks to bridge the gaps. With SDN, heterogeneous network resources can be managed with a unified abstraction. Moreover, we propose an SDN-based wireless communication solution, which can schedule different network resources to minimize communication cost. We investigate the problems in both single and multiple hop cases. We also evaluate the proposed approaches using traffic traces. The effectiveness and the efficiency are validated by the results.

In-situ investigation of notifications in multi-device environments

Abstract: Smart devices have arrived in our everyday lives. Being able to notify the user about events is a core feature of these devices. Related work investigated interruptions caused by notifications on single devices. In this paper, we investigate notifications in multi-device environments by analyzing the results of a week-long in-situ study with 16 participants. We used the Experience Sampling Method (ESM) and recorded the participants' interaction with smartphones, smartwatches, tablets and PCs. Disregarding the type or content of notifications, we found that the smartphone is the preferred device on which to be notified. Further, we found that the proximity to the device, whether it is currently being used and the user's current location can be used to predict if the user wants to receive notifications on a device. The findings can be used to design future multi-device aware smart notification systems.

Distributional semantic pre-filtering in context-aware recommender systems

Abstract: Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user's ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.

A framework for context-aware heterogeneous group decision making in business processes

Abstract: In Business Process Management great attention is given to Computational Intelligence for supporting process life-cycle. Several approaches have been defined to support human decision making. The main drawback is that there are no solid criteria for determining optimal decisions since context, matter of discussion, and involved actors may differ at each execution. This work focuses on the definition of a framework to support and trace human decision making activities, in business processes, when heterogeneous decision-makers have to find a consensus to select most promising alternative to follow. The framework relies on Fuzzy Consensus Model and implements Reinforcement Learning algorithm to learn weight of the decision-makers through the analysis of past process executions considering context and performances of business processes. Context awareness relies on semantic web technologies enabling ontological reasoning to evaluate context similarity used to assign the right weight to the involved decision-makers also in the case when more general or more specific context occurs. The framework has been instantiated in the case study of Supply Chain Management. The analysis of the simulation results reveal that the proposed weight learning algorithm and the considered initial weight association strategies (Starting Weight and Training Executions), even if the cold start, give to decision-makers the chance to fill the gap with respect to more experienced decision makers.

Hefestos: an intelligent system applied to ubiquitous accessibility

Abstract: This article proposes Hefestos, an intelligent system applied to ubiquitous accessibility. This model uses ubiquitous computing concepts to manage accessibility resources for people with disabilities. Among the concepts employed, context awareness, user profiles and trails management can be highlighted. The paper proposes an ontology for accessibility and delineates scenarios of its application in everyday life of people with disabilities. Moreover, the implementation of a smart wheelchair prototype and its application in a practical experiment is described. Ten users with a range of disability degrees tried the system and filled out a survey based on the technology acceptance model. This experiment demonstrated the main functionalities and the acceptance of the system. The results showed 96 % of acceptance regarding perceived easy of use and 98 % in perceived usefulness. These results were encouraging and show the potential for implementing Hefestos in real life situations.

A model for learning objects adaptation in light of mobile and context-aware computing

Abstract: The growth usage of mobile technologies and devices such as smartphones and tablets, and the almost ubiquitous wireless communication set the stage for the development of novel kinds of applications. One possibility is exploiting this scenario in the field of education, so creating more intelligent, flexible and customizable systems. Mobile devices can be used to help students to learn, considering their learning styles, surroundings, devices and profiles. In this way, the main goal of this article is to propose EduAdapt, an architectural model for the adaptation of learning objects considering device characteristics, learning style and other student's context information. To make this adaptation we used inferences and rules in a proposed ontology, named OntoAdapt. We believe that such ontology can help recommending learning objects to students or adapt these objects according to the context (context-aware computing). We evaluate this proposal in two ways. Firstly, we used scenarios and metrics to assess the ontology. Secondly, we developed a prototype of EduAdapt model and submitted to a class of 20 students with the intention of evaluating the usability and adherence to adapted objects, resulting in a 78 % of acceptance. In brief, the evaluation presented encouraging results, indicating that the proposed model would be useful in the learning process.

Research and trends in mobile learning from 1976 to 2013: A content analysis of patents in selected databases

Abstract: Mobile learning has been a very popular topic in the past several decades. As more patents in this field have been submitted, the analysis of patents has surfaced as an important mechanism to understand trends, uses, targeted audiences and other aspects in the mobile learning space. Based on the CNIPR, USPTO, and Espacenet databases, this paper provides an analysis of mobile learning from 1976, when the first patent in mobile learning emerged, to 2013. One hundred thirty patents were analyzed from two dimensions: the instructional dimension (including target audience, situation and purpose) and the patent dimension (including technology and style). It was found that 'students' was the most popular target audience; 'out of class for education' was the most utilized situation; 'provide more friendly peripheral service' was the primary purpose; 'wireless, mobile and ubiquitous technologies for learning, pervasive computing for learning, u-computing in learning' were the most utilized technologies; and 'system and method' was the most common style. Currently, patents in mobile learning are more inclined to provide personalized, contextualized, easily-retrievable, auto-updated and intelligent pushed learning content. Additionally, providing multipresentation, supporting seamless learning, adopting learner analysis, improving learner diversity and context awareness are becoming the characteristics of mobile learning patents. [ABSTRACT FROM AUTHOR]

Towards Situation Driven Mobile Tutoring System for Learning Languages and Communication Skills: Application to Users with Specific Needs

Abstract: Introduction Advances in mobile devices and related technologies are increasingly allowing the emergence of new applications. However, the very changing characteristics of mobile devices and their surrounding environment may lead to undesired and unpredictable situations preventing the user to use required services at a given time. Moreover, even though those characteristics may still unchanged, the user's mobility or her disabilities implies new or different situations and activities requiring new supporting services or adaptation of existing ones (Conde et al., 2009). For instance, during a common day life, the user may experience activities within which she is required to communicate and use domain specific expressions. She may be required to speak in a different language than her native or also experience strong communication problems and can't be aware of her surrounding environment (Massaro, 2004). Acquiring new communication skills in formal or informal way by making use of technology have been addressed by researches since a long time (Shute & Zapata-Rivera, 2012). Indeed many researches in e-learning had established pedagogical methods, standards, tools and platforms in order to support learners and to provide them with learning as well as assessment activities for learning languages or social skills (Grawemeyer, Johnson, Brosnan, Ashwin, & Benton, 2012). Some have addressed communication aspects intended to people with disabilities such as autism or impaired hearing (Jaballah & Jemni, 2013; El-Sattar, 2008; Venkatesh, Greenhill, Phung, Adams, & Duong, 2012). While others have focused on reviews to establish the effectiveness of the use of computer to educating people with disabilities (Askari et al., 2015; Sansosti, Doolan, Remaklus, Krupko, & Sansosti, 2014; Sanchez, Bartel, Brown, & DeRosier, 2014). New challenging learning scenarios taking into account the learner's context has been also provided in e-learning settings and many attempts are being taken in the context of mobile learning (Boticario & Santos, 2007; Fragale, 2014; Judy & Krishnakumar, 2012). Moreover, intelligent adaptability aspects have been already successfully integrated in ITSs which are considered as a particular category of one-on-one e-learning systems. For instance, ITSs main purposes are to simulate the real teacher's behavior and adapt learning processes and content to one's learners specific needs (Murray, 1999). Unfortunately, this kind of adaptation is always defined at design time. Additionally, even though there are some works that have tackled mobility issues for ITSs (Badaracco, Liu, & Martinez, 2013), those ITSs' architectures have not addressed modularity or dynamic adaptability to take account new user's mobile devices, physical contexts and new emerging needs especially those related to the appropriate use of a language within a specific context (Mahmoud, Belal & Helmy, 2014). In order to overcome those drawbacks, relevant mechanisms dealing with context awareness, mobility, adaptability as well as adaptation of mobile apps are strongly required. This paper, aims to provide an adaptable and re-configurable mobile ITS for supporting learning approaches addressed specifically to learning languages and communication skills for people with disabilities. In this ITS, mobility, context awareness and adaptability are addressed by combining and porting OSGi features and Semantic Web technologies on top of an Android platform. The main contributions of the present paper are (1) the learning approach adopted to take advantages of the user's context (mobility and specific needs) and the ITS provided (2) the flexible ITS's architecture and its modular, lightweight and fine grained components (3) the mechanism provided for handling the user's context and the model on which it is based and (4) the semantic and ontological descriptions of the user's mobile context, the learning approach as well as the components and services provided by the ITS. …

Towards 5G: Context Aware Resource Allocation for Energy Saving

Abstract: With the objective of providing high quality of service (QoS), 5G system will need to be context-aware that uses context information in a real-time mode depends on network, devices, applications, and the environment of users'. In order to continue enjoying the benefits provided by future technologies such as 5G, we need to find solutions for reducing energy consumption. One promising solution is taking advantage of the context information available in today's networks. In this paper, we take a step towards 5G by utilizing context information in the scheduling process as conventional packet scheduling algorithms are mainly designed for increasing throughput but not for the energy saving. We investigate a Context Aware Scheduling (CAS) algorithm which considers the context information of users along with conventional metrics for scheduling. An information model of context awareness along with a context aware framework for resource management is also presented in this paper. CAS is simulated applying a system level simulator and the results obtained show that considerable amount of energy is saved by utilizing the context information compare to conventional scheduling algorithms.

Facing the Millimeter-Wave Cell Discovery Challenge in 5G Networks With Context-Awareness

Abstract: The introduction of millimeter-wave (mm-wave) technologies in the future 5G networks poses a rich set of network access challenges. We need new ways of dealing with legacy network functionalities to fully unleash their great potential, among them the cell discovery procedure is one of the most critical. In this paper, we propose novel cell discovery algorithms enhanced by the context information available through a C-/U-plane-split heterogeneous network architecture. They rely on a geo-located context database to overcome the severe effects of obstacle blockages. Moreover, we investigate the coordination problem of multiple mm-wave base stations that jointly process user access requests. We show that optimizing the resource allocated to the discovery has a great importance in defining perceived latency and supported user request rate. We have performed complete and accurate numerical simulations to provide a clear overview of the main challenging aspects. The results show that the proposed solutions have an outstanding performance with respect to basic discovery approaches and can fully enable mm-wave cell discovery in 5G networks.

Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

Abstract: Purpose Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems. Design/methodology/approach Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status. Findings This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit. Research limitations/implications To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season. Practical implications Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated. Originality/value This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.

Smart Wearables or Dumb Wearables?: Understanding how Context Impacts the UX in Wrist Worn Interaction

Abstract: Recent advances in technology fostered the commercialization and usage of wearable devices. Among diverse form factors, wrist worn devices stand out. Benefitting from a conventional format and easy access, wrist worn devices, such as smart watches and fitness trackers, have been gaining popularity. While their continuous usage and close contact with the human body enable various applications, their limited computational resources summed with continuous changes in the context of use challenge designers in providing effective interactive solutions for end users. Seeking to understand how the context of use impacts the user experience and interaction with ten popular wrist worn wearables, in this study we analyzed the users' feedback: 545 users' comments were collected from Amazon, coded and aggregated. Based on the users' feedback, we identify 31 major problems that are currently faced in wrist worn interfaces. The analyses of the users' feedback led to a discussion about the causes and severity of those problems, and also to the definition of a set of design implications aimed at improving the user interaction with the next-generation wrist worn wearables.

On Context Awareness for Multisensor Data Fusion in IoT

Abstract: With the advances in sensor technology, data mining techniques and the internet, information and communication technology further motivates the development of smart systems such as intelligent transportation systems, smart utilities and smart grid. With the availability of low cost sensors, there is a growing focus on multi-sensor data fusion (MSDF). Internet of Things (IoT) is currently connecting more than 9 billion devices. IoT includes the connectivity of smart things which focuses more on the interactions and interoperations between things and people. Key problem in IoT middleware is to develop efficient decision level intelligent mechanisms. Therefore, we focus on IoT middleware using context-aware mechanism. To get automated inferences of the surrounding environment, context -aware concept is adopted by computing world in combination with data fusion. We conduct a comprehensive review on context awareness for MSDF in IoT and discuss the future directions in the area of context-aware computing.

Context-aware wireless mobile autonomic computing and communications: research trends and emerging applications

Abstract: Growing attention has recently been devoted to context-aware computing and communication systems, in particular concerning their evolution toward the new paradigm of context-aware autonomic computing and communications. Indeed, context awareness and autonomicity appear to be the indispensable glue technologies to accomplish efficient integration of modern software-intensive cyber-physical systems, operating in open and non-deterministic environments, and to master their complexity. This article provides an update on the latest developments in this field. Proposed methodologies and techniques are discussed and framed in the overall research problem. Moreover, future trends and research challenges are outlined by relating the basic concepts of context-aware autonomic computing and communications to the emerging paradigm of the self-aware Internet of Things, applications of which appear to be very promising examples of next-generation computing and communication systems.

Using Contextual Information for Recognising Human Behaviour

Abstract: Identifying human behaviours in smart homes from sensor observations is an important research problem. The addition of contextual information about environmental circumstances and prior activities, as well as spatial and temporal data, can assist in both recognising particular behaviours and detecting abnormalities in these behaviours. In this paper, the authors describe a novel method of representing this data and discuss a wide variety of possible implementation strategies.

On security level usage in context-aware role-based access control

Abstract: Huge contemporary trend is adding context awareness into software applications. It allows both better user experience as well as a lot useful features for application owner. Nowadays, there are various approaches enabling particular context awareness but none of them concerns security. We tackle this problem and describe it further in the paper. Our solution extends role based access control with certain context awareness elements. Based on already existing solutions we propose own lightweight, universal solutions, which allows instant enhancement of current RBAC even in existing applications. The uniqueness of our solution is based on using security levels, which are granted to user based on his context. Security levels represents how the users can be trusted and are determined during users login procedure. The levels are used as additional security constrain so to access resources in application user need to have not only right permission granted through roles, but also to have corresponding level.