COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system’s agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data

Coordinating distributed autonomous agents with a real-time database: the CAMBADA project

DETIInterest on using mobile autonomous agents has been growing, recently, due to their capacity to cooperate for diverse purposes, from rescue to demining and security. However, such cooperation requires the exchange of state data that is time sensitive and thus, applications should be aware of data temporal coherency. In this paper we describe the architecture of the agents that constitute the CAMBADA robotic soccer team developed at the University of Aveiro, Portugal. This architecture is built around a real-time database that is partially replicated in all team members and contains both local and remote state variables. The temporal coherency of the data is enforced by an adequate management system that refreshes each database item transparently at a rate specified by the application. The application software accesses the state variables of all agents with local operations, only, delivering both value and temporal coherency

Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks

This paper demonstrates, for the first time to our knowledge, hierarchical learning framework for inter-domain service provisioning in software-defined elastic optical networking (EON). By using a broker-based hierarchical architecture, the broker collaborates with the domain managers to realize efficient global service provisioning without violating the privacy constrains of each domain. In the proposed hierarchical learning scheme, machine learning-based cognition agents exist in the domain managers as well as in the broker. The proposed system is experimentally demonstrated on a two-domain seven-node EON testbed for with real-time optical performance monitors (OPMs). By using over 42000 datasets collected from OPM units, the cognition agents can be trained to accurately infer the Q-factor of an unestablished or established lightpath, enabling an impairment-aware end-to-end service provisioning with an prediction Q-factor deviation less than 0.6 dB

Resilience for Multi-filter All-source Navigation Framework with Integrity

The Autonomous and Resilient Management of All-source Sensors (ARMAS) framework monitors residual-space test statistics across unique sensor-exclusion banks of filters, (known as subfilters) to provide a resilient, fault-resistant all-source navigation architecture with assurance. A critical assumption of this architecture, demonstrated in this paper, is fully overlapping state observability across all subfilters. All-source sensors, particularly those that only provide partial state information (altimeters, TDoA, AOB, etc.) do not intrinsically meet this requirement. This paper presents a novel method to monitor real-time overlapping position state observability and introduces an observability bank within the ARMAS framework, known as Stable Observability Monitoring (SOM). SOM uses real-time stability analysis to provide an intrinsic awareness to ARMAS of the capabilities of the fault detection and exclusion (FDE) functionality. We define the ability to maintain consistent all-source FDE to recover failed sensors as navigation resilience. A resilient FDE capability then is one that is aware of when it requires more sensor information to protect the consistency of the FDE and integrity functions from corruption. SOM is the first demonstration of such a system, for all-source sensors, that the authors are aware. A multi-agent 3D environment simulating both GNSS and position and velocity alternative navigation sensors was created and individual GNSS pseudorange sensor anomalies are utilized to demonstrate the capabilities of the novel algorithm. This paper demonstrates that SOM seamlessly integrates within the ARMAS framework, provides timely prompts to augment with new sensor information from other agents, and indicates when framework stability and preservation of all-source navigation integrity are achieved

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

Design of a middleware for QoS-aware distribution transparent content delivery

Developers of distributed multimedia applications face a diversity of multimedia formats, streaming platforms and streaming protocols. Furthermore, support for end-to-end quality-of-service (QoS) is a crucial factor for the development of future distributed multimedia systems. This paper discusses the architecture, design and implementation of a QoS-aware middleware platform for content delivery. The platform supports the development of distributed multimedia applications and can deliver content with QoS guarantees. QoS support is offered by means of an agent infrastructure for QoS negotiation and enforcement. Properties of content are represented using a generic content representation model described using the OMG Meta Object Facility (MOF) model. A content delivery framework manages stream paths for content delivery despite differences in streaming protocols and content encoding. The integration of the QoS support, content representation and content delivery framework results in a QoS-aware middleware that enables representation transparent and location transparent delivery of content

Interaction-aware Kalman Neural Networks for Trajectory Prediction

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network. Attributed to the multiple traffic data sources, our end-to-end trainable approach technically fuses dynamic and interaction-aware trajectories boosting the prediction performance. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium (IV) 202

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201