Carlos Oberdan Rolim

Situation awareness and computational intelligence in opportunistic networks to support the data transmission of urban sensing applications

Abstract Smart cities can be seen as large-scale Cyber-Physical Systems with sensors monitoring cyber and physical indicators and with actuators dynamically changing the complex urban environment in some way. In this context, urban sensing is a new paradigm that exploits human-carried or vehicle-mounted sensors to ubiquitously collect data to provide a holistic view of the city. A challenge in this scenario is the transmission of sensed data in situations where the networking infrastructure is intermittent or unavailable. This paper outlines our research into an engine that uses opportunistic networks to support the data transmission of urban sensing applications. It applies situation awareness and computational intelligence approaches to perform routing, adaptation, and decision-making procedures. We carried out simulations within a simulated environment that showed our engine had 12% less overhead than other compared approaches.

An Architecture for Resilient Ubiquitous Systems

With the perspective of ubiquitous computing becoming more common form of technology in our everyday lives, our increasing dependency on these systems will require them to be always available, failure-free, fully operational and safe. They will also enable more activities to be carried out and provide new opportunities for solving problems. In view of the potential offered by ubiquitous computing and the challenges it raises, this work proposes a self-healing architecture to support ubiquitous applications aimed at healthcare The goal is to continuously provide reliable services to meet their requirements despite changes in the environment. We outline the application scenario and proposed architecture, as well as giving a detailed account of its main modules with particular emphasis on the fault detector.

Percontrol: A pervasive system for educational environments

This paper presents the development of a control system for learning environments specialized in special education. Amongst its various possible uses, the system focuses on managing the class attendance of special education students, teachers and other classroom users through the use of pervasive and ubiquitous technologies. For developing tests and obtaining results, electronic devices that monitor and control the environment were used, in particular, SunSpot devices. These nodes detect and store environmental data in an omnipresent distributed database. Another contribution present in this work is the use of a hybrid middleware for controlling the application and the devices used in these environments.

An adaptive fault tolerance approach to enhance the execution of applications on multi-cluster grid configurations from mobile grid interfaces in wireless networks

Mobile grid environments can be broadly divided into two different types of configurations. In the first case, devices are used as grid interfaces. In the second configuration, mobile devices are considered to be grid resources. However, the interaction between mobile devices and wired networks still has to overcome several difficulties before it can produce an operational mobile grid infrastructure, in either type of configuration. Examples of these challenges include the submission of distributed applications to execute tasks in a coordinated manner, frequent disconnections, restrictions on power consumption and the selection of suitable grid resources for application executions. In this paper, a research study is conducted involving an adaptive fault tolerance approach for grid interfaces in wireless networks to enhance the submission and execution of distributed applications. The approach examines submission, monitoring and management functions through the use of fault tolerance and workflow mechanisms, especially when disconnections occur and the mobile devices are executing an application. The empirical results show that the proposed approach can achieve a satisfactory level to support grid interfaces in a wireless network, and provide a more robust environment for application executions with less power.

An ubiquitous service-oriented architecture for urban sensing

In the transformation from traditional to smart cities, there is an increasing trend around the world towards intelligent dynamic infrastructures that provide citizens with new services that can improve their quality of life and fulfill the criteria of energy efficiency and sustainability. In the light of this, an important challenge is how to enable citizens and cities to promote the sensing of data with regard to a number of different factors. This paper outlines the early stages of our research which is concerned with an ubiquitous service-oriented architecture for urban sensing called UrboSenti. The proposed approach differs from other sensing platforms since it provides a set of services to collect data from several sources and assists in the development of new sensing applications. In addition, our model encompasses all the sensing activities, ranging from the collection of data to the generation of reports about events in the city.

MultiS: A Context-Server for Pervasive Computing

Context-aware applications are capable of recognizing environmental changes and adapting their behavior to the new context. This process can be divided into three stages: monitoring, context recognition and adaptation. On the monitoring layer, raw information about the environment is collected from sensors. The context recognition layer processes the data acquired from the context and transforms it into information which can be useful for the adaptation process. With this information, the adaptation system can determine what behavior is correct for the application in each different context. This paper proposes a context server called MultiS, which has the goal of solving the problems arising from the context recognition layer, and which includes the following advantages: a) the production of new context data based on the information of several sensors and an ability to react to changes in the environment; b) definition of a composed language for the context data called CD-XML; c) support for mobility.

Energy saving on DTN using trajectory inference model

Delay or Disruption Tolerant Networks (DTN) are characterized by long delays and intermittent connectivity, requiring efficient energy consumption for increasing the mobile nodes lifetime. The movements of nodes modify the network topology, changing the number of connection opportunities between nodes. This paper proposes a new technique for energy saving on DTN by using a trajectory inference model for mobile nodes powered by machine learning techniques. The objective of this work is to reduce the energy consumption of DTN using a mobility prediction method. Experimental results indicate more than 47% of energy saving on data communication applying the trajectory inference model.

A failure detector based on processes’ relevance and the confidence degree in the system for self-healing in ubiquitous environments

Ubiquitous systems have become a common technology in our everyday lives, thus increasing our dependency on them. However, the occurrence of failures in these types of systems can reduce their applicability/usability which may result in some difficult, or even dangerous, consequences. Such systems must, therefore, possess self-healing capabilities in order to detect failures and make the necessary adjustments to prevent their impact on applications. In such a context, this work proposes a new and flexible unreliable failure detector (FD), denoted as the Impact failure detector, which is based on process relevance and the confidence degree in the system, for self-healing systems in ubiquitous environments. The output of the Impact FD concerns the confidence in the system as a whole. By expressing the relevance of each node by an impact factor value as well as a margin of acceptable failures of the system, the Impact FD enables the user to tune the failure detection configuration in accordance with the requirements of the application. The performance evaluation results confirm the degree of flexible applicability of our FD and, that due to the margin of failure, the number of false responses may be reduced when it is compared with traditional unreliable FDs.

A novel engine to underlie the data transmission of social urban sensing applications

Social urban sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to ubiquitously collect data for large-scale urban sensing. A challenge of such scenario is how to transmit sensed data in situations where the networking infrastructure is intermittent or unavailable. In this context, this paper outlines our researches of a novel engine that uses Opportunistic Networks paradigm to underlie the data transmission of social urban sensing applications. It applies Situation awareness, Fuzzy logic and Machine Learning approaches to perform routing and decision-making process. As we know, this is the first paper to use such approaches in Smart Cities area with focus on social sensing application. As well as being original, the results from our simulations signals the way that further research can be carried out in this area.

Towards a Novel Engine to Underlie the Data Transmission of Social Urban Sensing Applications

Social urban sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to ubiquitously collect data for large-scale urban sensing. A challenge of such scenario is how to transmit sensed data in situations where the networking infrastructure is intermittent or unavailable. In this context, this paper outlines the early stages of our research which is concerned with a novel engine that uses Opportunistic Networks paradigm to underlie the data transmission of social urban sensing applications. It applies Situation awareness, Neural Networks and Fuzzy Logic for routing and decision-making process. As we know, this is the first paper to use such approaches in Smart Cities area with focus on social sensing application. As well as being original, the preliminary results from our simulations signals the way that further research can be carried out in this area.