Mário Antunes

Context storage for M2M scenarios

As the number of environmental sensors grows, it becomes increasingly difficult to manage, store and process all these sources of information. Several context representation schemes try to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss efficient ways to deal with this diversity of representation schemes. We defined the basic requirements for flexible context storage systems, proposed an implementation and compared our implementation against two other approaches. Our solution provides more value than the remaining solutions without suffering a significant decrease in performance.

Scalable semantic aware context storage

In recent years the Internet has grown by incorporating billions of small devices, collecting real-world information and distributing it though various systems. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. Several context representation schemes have tried to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss an efficient way to deal with this diversity of representation schemes. We define the basic requirements for context storage systems, analyse context organizations models and propose a new context storage solution. Our solution implements an organizational model that improves scalability, semantic extraction and minimizes semantic ambiguity.

On the application of contextual IoT service discovery in Information Centric Networks

The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several efforts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among different devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios.

Semantic-Based Publish/Subscribe for M2M

The number of connected devices is expected to soar in the coming years, each one of them collects and distributes real-world information though various systems. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, however none of them have been widely adopted. Publish/subscribe paradigm has proven to be an adequate abstraction for large scale information dissemination, but none of current variations is well suited for context information. In a previous publication we addressed these challenges, however our solution has some drawbacks: poor scalability and semantic extraction. The aim of this paper is twofold. First, we discuss an efficient way to deal with representation schemes diversity and propose a d-dimensional context organization model. Second, we propose a semantic-based publish/subscribe system that is well suited for M2M scenarios. Our evaluation shows that d-dimensional organization model outperforms our previous solution in both speed and space requirements.

Aerial Ball Perception Based on the Use of a Single Perspective Camera

The detection of the ball when it is not on the ground is an important research line within the Middle Size League of RoboCup. A correct detection of airborne balls is particularly important for goal keepers, since shots to goal are usually made that way. To tackle this problem on the CAMBADA team , we installed a perspective camera on the robot. This paper presents an analysis of the scenario and assumptions about the use of a single perspective camera for the purpose of 3D ball perception. The algorithm is based on physical properties of the perspective vision system and an heuristic that relates the size and position of the ball detected in the image and its position in the space relative to the camera. Regarding the ball detection, we attempt an approach based on a hybrid process of color segmentation to select regions of interest and statistical analysis of a global shape context histogram. This analysis attempts to classify the candidates as round or not round. Preliminary results are presented regarding the ball detection approach that confirms its effectiveness in uncontrolled environments. Moreover, experimental results are also presented for the ball position estimation and a sensor fusion proposal is described to merge the information of the ball into the worldstate of the robot.

Learning Semantic Features from Web Services

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. It is our personal belief that IoT and M2M scenarios will only achieve their full potential when all the devices will work and learn together without human interaction. In this paper we review the most relevant semantic metrics and propose a new unsupervised model that minimizes sense-conflation problem. Our solution was evaluated against Miller-Charles dataset, outperforming our previous work in every metric.

Contour-Based Object Extraction and Clutter Removal for Semantic Vision

This paper focuses on object extraction from images, a functionality that can be relevant both for category learning and object recognition in diverse applications. The described object extraction approach, which doesn’t take into account any prior knowledge about the target objects, works on the edge-based counterpart of the original image. In a first step, groups of neighboring edge pixels are traced to form contour segments. These contour segments are then coherently aggregated to reconstruct the shapes of the different objects present in the original image. The approach is particularly relevant for extracting objects with few if any distinctive local features, thus objects mainly characterized by their shape. The developed functionalities can be used to segment and extract objects from images with multiple objects, as those obtained from the Internet by searching for a specific object category name. They can also be used to discard clutter from image sub-windows expected to contain a single object, as those delivered by an object detector.

Improve IoT/M2M Data Organization Based on Stream Patterns

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area. With this in mind we propose a tailored generative stream model, with two main uses: stream similarity and generation. Sensor data can be organized based on pattern similarity, that can be estimated using the proposed model. The proposed stream model will be used in conjunction with our context organization model, in which we aim to provide an automatic organizational model without enforcing specific representations. Moreover, the model can be used to generate streams in a controlled environment. Useful for validating, evaluating and testing any platform that deals with IoT/M2M devices.

Towards IoT data classification through semantic features

Abstract The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller–Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.

Unified Platform for M2M Telco Providers

Although many environments are powered by M2M solutions, users do not have a simple way to gather their collective knowledge and program devices’ behaviour. Also, Telco providers still lack proper components for enabling integrated services over their networks. We present the final architecture of the APOLLO project, which delivers a enhanced M2M platform encompassing sensors, management and applications platform for a major Telco provider. APOLLO builds on top of ETSI M2M specifications and rich service execution environments providing easy orchestration of services to end-users.