Dan Puiu

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on peoples everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

Robust camera pose and scene structure analysis for service robotics

Successful path planning and object manipulation in service robotics applications rely both on a good estimation of the robots position and orientation (pose) in the environment, as well as on a reliable understanding of the visualized scene. In this paper a robust real-time camera pose and a scene structure estimation system is proposed. First, the pose of the camera is estimated through the analysis of the so-called tracks. The tracks include key features from the imaged scene and geometric constraints which are used to solve the pose estimation problem. Second, based on the calculated pose of the camera, i.e. robot, the scene is analyzed via a robust depth segmentation and object classification approach. In order to reliably segment the objects depth, a feedback control technique at an image processing level has been used with the purpose of improving the robustness of the robotic vision system with respect to external influences, such as cluttered scenes and variable illumination conditions. The control strategy detailed in this paper is based on the traditional open-loop mathematical model of the depth estimation process. In order to control a robotic system, the obtained visual information is classified into objects of interest and obstacles. The proposed scene analysis architecture is evaluated through experimental results within a robotic collision avoidance system.

Autonomic monitoring approach based on CEP and ML for logistic of sensitive goods

The main objective of this work is to develop a framework for supporting the development of applications for logistic companies that transport perishable goods (food and medicines). Reducing the amount of lost and damaged perishable goods during transportation and storage represents a substantial global challenge, which imply the implementation of cold chain monitoring at all levels of the supply chains. The framework contains several components that enable: (1) the real-time monitoring of goods during transportation; (2) forecast the temperatures of parcels; (3) generate real-time alerts/early warning when the product are not stored according to the acceptance criteria.

Using Complex Event Processing for implementing a geofencing service

Geofencing is one of the components in the wider spectra of Ambient Assisted Living related applications. Such applications are meant to provide support to persons with disabilities or to those impaired, as well as to their caretakers. Within this context, geofencing targets the safe mobility of such persons. As mobility related data can be translated into events with a temporal dimension, we have developed a geofencing service based on the Complex Event Processing paradigm. This decision was supported also by the fact that Ambient Assisted Living applications, in general, are often considered for integration into event rich environments, such as those resulting from smart cities infrastructure. The ability of Complex Event Processing to handle vast amounts of time correlated events from multiple sources meets the requirements imposed by the geofencing service. This paper presents the architecture of a Complex Event Processing based geofencing service along with the results obtained during the evaluation phase of the first prototype.

Predictive analytics based on CEP for logistic of sensitive goods

Reducing the amount of lost and damaged perishable goods (food and medicines) during transportation and storage represents a substantial global challenge, which imply the implementation of cold chain monitoring at all levels of the supply chains. The main objective of this work is to develop an IoT enabled infrastructure, which supports the development of smart transportation specific applications. The proposed infrastructure contains an emulator, which can be used for emulating the conditions inside a truck during the transportation process and some CEP based tools for processing the data generated by the sensors.

Traffic condition monitoring using complex event processing

As the number of vehicles present on todays roads constantly increases along with the number of intelligent traffic infrastructure elements capable of providing traffic related data, the need for making the best use of this data in order to provide proper traffic and resources management increases at the same pace. Traffic Management System designers are facing thus the problem of handling vast amounts of traffic related data, coming from a variety of sources in the least time consuming manners. Complex Event Processing is one of the latest techniques which provides the ability to deal with this kind of constraints. In this paper we present a Complex Event Processing based method for traffic condition monitoring along with its evaluation based on the simulation environment we have developed to support our work.

Modelling a flexible manufacturing system using reconfigurable finite capacity Petri nets

This paper addresses the issue of modelling concurrent systems whose structure is subject to changes by using an extension of the Petri nets formalism. Within this scope, we introduce the concept of reconfigurable finite capacity Petri nets and we apply it to a flexible manufacturing system model. Besides providing the designer a facile means of expressing the dynamic character of the flexible manufacturing system, the formalism also simplifies the model. In the second part of the paper we present and also compare the Petri net model and the equivalent reconfigurable finite capacity Petri net model for a such a system and we evaluate these models using PetriNetExec, a software library supporting the integration of Petri nets into Java applications.

Real-time collision avoidance for redundant manipulators

The paper presents a joints trajectories planning strategy for a redundant manipulator. The control system modifies, in real-time, the robot manipulator joints references positions, during a transition, when an unexpected moving obstacle is getting closer to one of the robot components (links and joints). The position of the moving objects is determined using a digital image processing system. The control system determines if the moving obstacle and the arm robot are in collision using an interference detection method based on oriented bounding boxes. When the arm robot control system detects a collision, it modifies the arm robot joints trajectories in order to increase the distance between the robot and the obstacle and, in the same time, maintain the initial trajectory of the end-effecter. Computer simulation and experimental results are given for a 7 DOF redundant arm and demonstrate the collision avoidance capability.

The time delay control of CAN messages for real-time communication

The latest advances in the field of microelectronics and modern control systems demand for more flexible control architectures. As a result of that, the centralized control architectures are replaced by flexible distributed control systems based on intelligent drives and powerful communication networks. CAN network was designed for car networking in order to reduce the complexity of the related wiring harnesses. To achieve the real-time communication constraints the traditional CAN application technique must be updated. This paper presents a new approach of using the dynamically priority allocation technique for a CAN network. Our solution is to increase gradually the message priority until it becomes the message with the highest priority from the network and it is transmitted. This method of message priority allocation allows us to determine its maximum transmission time.

A public transportation journey planner enabled by IoT data analytics

Using the CityPulse framework we developed a proof of concept application for Braşov public transportation company. The application provides route recommendations and incident notifications for the citizens who travel by bus. This is achieved by processing in real-time the data streams about bus arrivals in stations and the incidents reported by citizens. The proof of concept application was presented to the representatives of Braşov municipality and public transport company.