Mobile Cloud Computing (MCC) addresses the resource limitations of the mobile devices, but with a hidden cost depending on the requirements of the mobile applications. In this work, we investigate the proclaimed benefits of MCC for smart phones when thin clients are used to access remote cloud services, especially for applications that capitalize on the data generated from the mobile device. The added value of MCC can be hampered if the power of today's leading-edge mobile technology is not estimated. The opportunistic use of cloud is inevitable for the maximum benefit of the devices. Our experiments show that many resource and performance trade-offs exist and the current deployment schemes for these kind of applications are rough around the edges. There is a need for an automated deployment decision support for mobile applications to exploit the benefits of the cloud as well as the power of today's high-end mobile devices.

Nowadays, most mobile applications rely on device screen locking mechanisms for ensuring practical security, which expects the users to explicitly authenticate with a PIN or biometric irrespective of the perceived threats. Owing to this usability issues, many avoid using device locks potentially compromising the security. To overcome the limitations of this binary approach, we present an implicit authentication framework called PRISM (Policy-driven Risk-based Implicit locking for improving the Security of Mobile end-user devices). It provides risk based authentication by detecting anomalies in the usual behavior patterns of the users which include their expected locations, activities and application usage. Its device unlocking decisions are driven by policies that are defined either automatically by mining sensor data or manually by the end-users. Our experiments show that PRISM is able to discover useful behavior patterns efficiently even with limited data. The number of required explicit authentications is significantly reduced while assuring the preferred security for everyday scenarios.

In the Internet of Things, heterogeneous and distributed streams of sensor events is a driver for context-aware behavior in intelligent environments. However, processing the event data usually cross-cuts the business logic of IoT applications and offering such reusable functionality as a service towards a variety of customers with different needs is often faced with scalability concerns. We present SAMURAI, a multi-tenant streaming context architecture that integrates and exposes well-known components for complex event processing, machine learning, knowledge representation, NoSQL persistence and in-memory data grids. SAMURAI pursues a twofold approach to achieve scalability: (1) distributed deployment with horizontal scalability, (2) shared resources through multi-tenancy. For the scenario used in the experimental evaluation of our architecture, the results show little overhead to support multi-tenancy, with near-linear scalability and flexible elasticity for deployment schemes with data partitioning per tenant.

The intelligent home environment is a well-established example of the Ambient Intelligence application domain. A variety of sensors and actuators can be used to have the home environment adapt towards changing circumstances and user preferences. However, the complexity of how these intelligent home automation systems operate is often beyond the comprehension of non-technical users, and adding new technology to an existing infrastructure is often a burden. In this paper, we present a home automation framework designed based on smart widgets with a model driven methodology that raises the level of abstraction to configure home automation equipment. It aims to simplify user-level home automation management by mapping high-level home automation concepts onto a low-level composition and configuration of the automation building blocks with a reverse mapping to simplify the integration of new equipment into existing home automation systems. Experiments have shown that the mappings we proposed are sufficient to represent household appliances to the end user in a simple way and that new mappings can easily be added to our framework.

The use of parallelism enhances the performance of a software system. Its excessive use, however, can degrade the performance. In this paper we propose a parallelism viewpoint to optimize the use of parallelism by eliminating unnecessarily used threads in legacy systems. The viewpoint describes the parallelism behaviour of the system, which can be used to analyze for overheads associated with threads. We illustrate the proposed viewpoint with the help of an industrial case, a parallelism-intensive electron microscope software system. We use the viewpoint to analyze threads suitable to be replaced with a small sized thread pool in this system. Results show that the viewpoint provides a profound insight into the thread-model of the system that is required to reduce the parallelism. In the thread pool analysis, we found that more than 50% threads are underused. They were replaceable with a pool of approximately 11% of these threads.

In this paper, we present different modeling and execution frameworks that allow us to efficiently analyze, design and verify complex systems, mainly to cope with the specific concerns of the Real-time and embedded systems (RTE) domain. First we depict a UML /MARTE based methodology for executable RTE systems modeling with a framework and its underlying model transformations required to execute UML models conforming to the MARTE standard. The advantages of adopting a more generic action language with formal features are highlighted, in order to raise the level of abstraction with formal features. Then, we investigate how MARTE, with its Time Model facilities, can be made to represent faithfully AADL periodic/aperiodic tasks communicating through event or data ports, in an approach to end-to-end flow latency analysis. An analytical framework allows us to optimize port-based communication by generating a run time executive that utilizes shared data areas where appropriate, while ensuring the timing semantic assumed by the control application. An analysis of the AADL mode change protocol is also provided, exposing a translation process that takes as an input an AADL model and produces as an output a time Petri net. We show how an AADL model transformation provides a formal model for model checking activities and we suggest that model transformation provides useful support to improve the integration of formal verification in an industrial engineering process. As a case study we use an implementation of a UDP /IP protocol stack.

In the near future vehicles will be equipped with embedded communication capabilities in order to perform context-sensitive tasks such as traffic flow control and incident avoidance. In large scale vehicular networks it is of vital importance that the information exchanged between the vehicles meets a certain level of quality so that they can make informed automated decisions. In this paper we define Quality of Context (QoC) and Peer Reputation (PR) for nodes in vehicular networks and propose ways to apply them for efficient communication. Thus, we provide a two-fold solution in which on the one hand we focus on the quality of the information and on the other hand we aim at determining the reputation of the nodes involved in the communication. This helps to eliminate the use of erroneous, ambiguous and imprecise information provided by unknown entities. Our simulated experiments show that our mechanism significantly reduces network traffic usage, eases out the decision making process and guarantees a minimum level of quality.

In recent trends, the Architecture Analysis and Design Language (AADL) has received increasing attention from safety-critical software development industries. Specific about the AADL is its strong syntactic and semantic support for the description of both hardware and software architectures. Considering the existing range of software architectures, we study the support AADL offers for the description of software architecture. As a case study we use an implementation of a UDP/IP protocol stack. Based on our experiences, our position is that a number of abstract concepts, e.g. a generic component concept, are missing in the AADL to make it well-suited for the high- level description of software architecture.

The applicability of pervasive and mobile computing in the health care sector is beyond dispute. This paper explores the use of the mobile phone as a tool for personalized health care assistance for individuals diagnosed with diabetes. By monitoring user location and activity on the mobile phone, recognizing past behavior and augmenting the logging of blood glucose levels with context data, our prototype application assists with taking well-informed decisions on daily drug dosage to achieve and maintain stable blood glucose levels. Evaluation of the prototype application indicate that a brief training of the application suffices to capture patterns in the user's relevant context that simplify glucose level trends analysis. We describe some of the details of the user study and the prototype application, and conclude with plans to investigate context-driven activity prediction to further improve the decision support for the user.

This paper presents Geo-Linda, a physically distributed tuple space. Geo-Linda targets ubiquitous computing applications involving the detection of movement patterns of objects and people, such as the meeting of two people or the insertion of a product in a shopping cart, the loading of container in a boat... Existing distributed tuple spaces suffer two main limitations: (1) they cannot detect precisely movement patterns, leading to inconsistencies between the state of applications and the physical world; (2) they cannot detect several kinds of movement patterns. Geo-Linda tackles these two limitations, by introducing a geometric addressing mode and two new reading operations to access the tuple space. In this paper, we illustrate Geo-Linda with several small examples and a full application.

This paper revisits a problem that was identified by Kramer and Magee: placing a system in a consistent state before and after runtime changes. We show that their notion of quiescence as a necessary and sufficient condition for safe runtime changes is too strict and results in a significant disruption in the application being updated. In this paper, we introduce a weaker condition: tranquillity. We show that tranquillity is easier to obtain and less disruptive for the running application but still a sufficient condition to ensure application consistency. We present an implementation of our approach on a component middleware platform and experimentally verify the validity and practical applicability of our approach using data retrieved from a case study.

This paper revisits a problem that was identified by Kramer and Magee: placing a system in a consistent state before and after runtime changes (1990). We show that their notion of quiescence as a necessary and sufficient condition for safe runtime changes is too strict and violates the black-box design principle. We introduce a weaker condition, tranquility; easier to obtain, less disruptive for the system and still sufficient to ensure application consistency. We also present an implementation of this concept in a component middleware platform

This position paper describes ongoing work in which the Java-based SEESCOA component system is extended with functionality for run-time evolution. First, an assessment is made of the state-of-the-art in dynamic updating, and the applicability of existing systems for dynamic updating is examined. Then a new approach is presented, in which the concept of ports is used to redirect messages between components. The problem of class-file reloading in the JVM is avoided by modification of the classes at load-time to include version information. The predictability requirement of embedded systems is assured by updating all component instances at once.