A service-oriented middleware for composing context aware mobile services

Recent advances in wireless networks and mobile devices have brought about new scenes for the provision of services to end-users. Besides traditional services, new ones may be provided that transparently adjust and adapt to the user context. The user would have more choice and flexibility if, besides using the services, he could also compose his own services in an ad-hoc way. This paper presents iCas, an architecture to create context-aware services on the fly and discusses its main components. Also an application scenario is briefly described

Opportunistic data dissemination in mobile phone sensor networks

Situated communication technologies in emergencies are subject to decay or fail because of their inadequate services. With the advances in tiny-sensor technologies and ubiquity of smart phones, public awareness on urgent situations can be raised in more efficient and distributed ways. We center on opportunistic data dissemination schemes where the objective is to provide an operability among sensing, communication, and data spread. Such mobile networks do not require end-to-end routes or servers; however connectivity and scalability issues may last forever without determinism. We have started implementing context-aware services to understand the mobile phone carriers’ characteristics and routines in order to increase the quality of service in our collaboration and dissemination objectives. We are currently working on real implementation of mobile ad hoc networks and routing algorithms. Besides, we simulate an urban city network with different scenarios and objectives to analyze the open research challenges

Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices. The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%

Context-aware Services for Mobile Devices: From Architecture Design to Empirical Inference

Currently, mobile devices are aware of user position, which can be provided to mobile apps for the development of tailored services known as Location-Based Services. Further advances on current Location-based Services (LBS), i.e. using any other information from the user such as gender, music preferences etc, may lead to transition from a Location-Based environment to a fully developed ContextAware environment.The current trend towards Context-aware Services (CAS) is reflected in academic research since more than twenty years as well as in the progress in Software Development Kits (SDKs) of the main mobile operating systems, where CAS frameworks are currently being used. However, there is no community agreement for modelling context CAS and little is known about the architecture of these context management frameworks of the mobile operating systems.Based on previous research in the area of CAS, I establish and analyse a reasoning architecture, the Context Engine (CE), that enables the main steps of designing and implementing context-aware services. The chief utility of CAS is their ability to formulate and encapsulate information, obtain user context through context acquisition tools and distribute it to third-party applications that build personalised services based on the provided information. The CE has the responsibility of selecting the optimal context acquisition tool to solve a concrete problem which is discussed in this dissertation.Furthermore, this thesis contributes to the development of context inference tools by studying two particular cases. The first case aims at inferring user (semantic) location information based on mobile phone usage data. This first case has been carried out in collaboration with Microsoft Finland, which provides a similar context inference solution to mobile developers through their Software Development Kit (SDK). The second case aims at inferring user information based on social network information, i.e. infer user information based on his or her connections. Both studies yield positive results and have the potential to be extended to obtain better context acquisition tools and, therefore, better user context

AN INVESTIGATION INTO CONTEXT-AWARE AUTOMATED SERVICE IN SMART HOME FACILITIES: SEARCH ENGINE AND MACHINE LEARNING WITH SMARTPHONE

Technological advances, in general, coupled with the widespread use of smartphones, create ever more opportunities for mobile applications. This thesis considers the use of such devices within embedded systems to provide automated services in smart home automation. The overall approach links together context-aware data from the physical environment, sensors and actuators for domestic appliances and statistics-based decision-making. A prototype system named ‘Wireless Sensor/Actuator Mobile Computing in the Smart Home’ (WiSAMCinSH) is developed, which in turns aims to provide services that can benefit clients who are currently dependent on others in their daily activities. This research highlights and covers the following concepts. Firstly, it addresses the need to improve the prototypical decision-making model by enabling it to take into account context-aware information as conditions under which particular action decisions are appropriate. Secondly, an essential aspect of context-aware performance architecture is that its features must be of high accuracy, explicitly readable and fast. Thirdly, it is necessary to determine which probability-based rules are most effective in generating the dynamic environment to control the home facilities. Finally, it is important to analyse and classify in depth the accuracy of context acquisition and the corresponding context control using cross-validation methods. A case study uses integrated mobile detection technology to improve the efficiency of mobile applications, taking into account the resource limitations forced on the use of mobile devices. It also utilises other embedded sensing technologies to predict expectations, thereby enabling automatic control of facilities in the home. The main approach is to combine search engines and machine learning to create a system architecture for a context-aware computing service. Among the major challenges are finding the best statistics-based rules for decision-making and overcoming the heterogeneous character of the many devices which are used together. The results achieved show very promising potential for the use of mobile applications within a context-aware computing service, albeit one which still presents problems to be resolved through future research

Effect of oil palm empty fruit bunches (OPEFB) fibers to the compressive strength and water absorption of concrete

Growing popularity based on environmentally-friendly, low cost and lightweight building materials in the construction industry has led to a need to examine how these characteristics can be achieved and at the same time giving the benefit to the environment and maintain the material requirements based on the standards required. Recycling of waste generated from industrial and agricultural activities as measures of building materials is not only a viable solution to the problem of pollution but also to produce an economic design of building

A Flexible and Reconfigurable 5G Networking Architecture Based on Context and Content Information

The need for massive content delivery is a consolidated trend in mobile communications, and will even increase for next years. Moreover, while 4G maturity and evolution is driven by video contents, next generation (5G) networks will be dominated by heterogeneous data and additional massive diffusion of Internet of Things (IoT). The current network architecture is not sufficient to cope with such traffic, which is heterogeneous in terms of latency and QoS requirements, and variable in space and time. This paper proposes architectural advances to endow the network with the necessary flexibility helping to adapt to these varying traffic needs by providing content and communication services where and when actually needed. Our functional hardware/software (HW/SW) architecture aims at influencing future system standardization and leverage the benefits of some key 5G networking enablers described in the paper. Preliminary results demonstrate the potential of these key technologies to support the evolution toward content-centric and context-aware 5G systems