Cristiano André da Costa

SWTRACK: An intelligent model for cargo tracking based on off-the-shelf mobile devices

Abstract Logistics represent the biggest cost for transportation companies. Companies are investing in monitoring and tracking systems aiming at improving services, reducing costs and ensuring the safety in cargos transports. This article presents an intelligent model to obtain the position of vehicles, using off-the-shelf mobile devices named SWTRACK. The proposal allows distributors companies to track loads and to have real-time information about their whereabouts during a travel. SWTRACK presents an automatic travel management to initialize and finish travels without user interaction. Furthermore, it provides a mechanism to monitor detours in planned routes and to send alarms notification through mobile devices. To build that mechanism, we studied the Geofence concept and existing techniques to prepare two solutions based on continuous monitoring. Those solutions were compared and discussed. We also conducted a model evaluation using off-the-shelf mobile devices. Moreover, we provide an impact analysis regarding GPS precision and battery power consumption. In the end, we show that the proposed model generates a reliable system for tracking vehicles.

AutoElastic: Automatic Resource Elasticity for High Performance Applications in the Cloud

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.

An intelligent model for logistics management based on geofencing algorithms and RFID technology

The proposed model manages travels and cargos, without user interaction.We developed a hardware that obtains context information using RFID technology.The model uses context information and geofencing algorithms to perform decisions.The model sends notifications, speeding decision-making up, and reducing costs. The cargo transport represents the largest share of logistics costs in most companies. Consequently, companies are investing in tracking and tracing systems aiming to improve services, reduce costs and ensure the safety in cargo transportation. To play a part in this area, we propose in this article SafeTrack, a model for logistics management based on geofencing algorithms and radio-frequency technology. In this approach, the focus is on dealing with delivery management. The main scientific contribution of SafeTrack is automatic delivery management. Besides dealing with deliveries without user interaction, we provide a mechanism to detect inconsistencies at real-time. Furthermore, the model monitors detours in planned routes and deals with alarms notifications using mobile devices. To provide that features, we employed Geofence concept with two solutions that enable to detect, in real-time, the occurrence of detours in planned routes. We also created a component, named SafeDuino, to control loads delivery and pickups. The decision on the occurrence of inconsistencies during the logistics flow is performed through the fusion of context information, obtained from SafeDuino and a mobile device, using radio-frequency technology. We built a complete and functional prototype, which was evaluated in a controlled environment, testing several conditions. The test scenario was executed twenty times, showing that the proposed model is capable to identify all inconsistencies along the travels. We concluded that SafeTrack improves logistics operation, optimizing decision-making, avoiding losses during the logistics flow, and also allowing companies to remain competitive in the market.

MUCS: A model for ubiquitous commerce support

The evolution of computing technology and the emergence of wireless networks have contributed to the miniaturization of mobile devices and their increase in power, providing services anywhere and anytime. In this context, new opportunities for technology support have arisen in different areas, for example, education, games and entertainment, automobile, and commerce. We propose a model for ubiquitous commerce support (MUCS). This model uses ubiquitous computing concepts to look for deal opportunities for users who act either as buyers or sellers. This paper also describes two everyday scenarios, in which the MUCS model could be applied, and explains the implemented prototype to be used in them. Finally, we present the results obtained from a practical experiment, which was performed with the participation of users who filled out an evaluation questionnaire.

Towards a distributed architecture for context-aware mobile applications in UbiComp

The applications in Ubiquitous Computing (UbiComp) environments must be aware of their contexts of interest and adapt to changes in them. Thus, a major research challenge in the area of UbiComp is related to context awareness. Considering the high distribution, heterogeneity, dynamism, and mobility of ubiquitous environments, this paper presents an architectural model for context awareness, called EXEHDA-UC (Execution Environment for Highly Distributed Applications - Ubiquitous Context awareness). The proposal includes elements to support contextual data acquisition, actuation on the environment, and processing of contextual information. We consider that the main contribution of this work is an architecture that supports the managing of the acquisition, storage, and processing of context data, in a distributed way, independently of the application, in an autonomic and rule-based perspective. To assess the functionalities of the EXEHDA-UC, we present a case study, highlighting the prototypes developed, technologies employed, and tests realized.

Hefestos: a model for ubiquitous accessibility support

This paper presents the Hefestos Model, designed to provide ubiquitous accessibility. The project aims at supporting accessibility for People With Disability (PWD) and elderly in various situations of their everyday life. Offering context awareness, user profiles and trails management, the model is a base for applying assistive technologies that fulfills accessibility needs. Moreover, in the proposed model, we devised an ontology. Besides the benefits of intrinsic semantic representation, the use of an ontology fosters future integration with others solutions. We also present a case study based on a prototype, which was developed to evaluate the model. This assessment used a smart wheelchair, operated by a paraplegic person, which is under development at University of the Sinos Valley (Unisinos).

A model for context awareness in Ubicomp

In this paper we present a view of EXEHDA middleware and a novel service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the proposed service we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital.

Towards a programming model for context-aware applications

In mobile computing scenarios, users move in different environments and applications can automatically explore their surroundings. This type of context-aware application is an emerging trend, so far not widely disseminated. We believe that the proposal of a programming model specifically designed for the specification of context-aware applications, using general abstractions, will foster a rapidly widespread of such kind of software. In this article, we propose the use of the Holoparadigm (in short Holo) to fill this gap. We created a programming language based on Holo (called HoloL) to demonstrate the strengths of Holoparadigm to develop context-aware applications. The article uses a sample scenario to show the Holos practical aspects related to implementation of context-aware applications. We also present a platform to HoloL (called HoloEnv), composed of a compiler, a virtual machine, and a distributed execution environment. Finally, different performance aspects of the HoloEnvs components were evaluated through experimental results.

Managing adaptation in Ubicomp

In this paper we present a view of EXEHDA middleware and a novel service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the proposed service we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital.

Internet of things scalability: Analyzing the bottlenecks and proposing alternatives

Internet of Things, or IoT, is a new revolution of the Internet, supporting a connection between objects and humans yet more strong. This area is known by the huge quantity of data that can be generated from the RFID sensors at realtime. On managing this data, the IoT middleware introduces some time constrains regarding both accessing applications and filtering and storing data actions. Thus, middleware scalability is a key requirement for supporting the current growth of IoT. In this context, this article firstly presents μIB - Micro Internet of Things Benchmark - that aims at evaluating the scalability of EPC-compliant middlewares. μIB combines data from tags, RFID readers, sequential requests and number of threads to compose an evaluation setup to assess EPCIS and ALE components. We used this benchmark to evaluate Fosstrak, which is one of the most used IoT middlewares, in a single server. The tests emphasized that the exhaustion in one component influences the performance of the others directly. Following this research line, this article also describes the first ideas on redesigning the IoT middleware using techniques such as cloud virtualization and elasticity, service decomposition and multithreading programming.