Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Concept and design of the hybrid distributed embedded systems testbed

Wireless mesh networks are an emerging and versatile communication technology. The most common application of these networks is to provide access of any number of users to the world wide Internet. They can be set up by Internet service providers or even individuals joined in communities. Due to the wireless medium that is shared by all participants, effects like short-time fading, or the multi-hop property of the network topology many issues are still in the focus of research. Testbeds are a powerful tool to study wireless mesh networks as close as possible to real world application scenarios. In this technical report we describe the design, architecture, and implementation of our work-in-progress wireless testbed at Freie Universität Berlin consisting of 100 mesh routers that span multiple buildings. The testbed is hybrid as it combines wireless mesh network routers with a wireless sensor network

Using SensLAB as a First Class Scienti c Tool for Large Scale Wireless Sensor Network Experiments

International audienceThis paper presents a description of SensLAB(Very Large Scale Open Wireless Sensor Network Testbed) that has been developed and deployed in order to allow the evaluation through experimentations of scalable wireless sensor network protocols and applications. SensLAB's main and most important goal is to o er an accurate open access multiusers scienti c tool to support the design, the development tuning, and the experimentation of real large-scale sensor network applications. The SensLAB testbed is composed of 1024 nodes over 4 sites. Each site hosts 256 sensor nodes with speci c characteristics in order to o er a wide spectrum of possibilities and heterogeneity. Within a given site, each one of the 256 nodes is able both to communicate via its radio interface to its neighbors and to be con gured as a sink node to exchange data with any other "sink node". The hardware and software architectures that allow to reserve, con gure, deploy rmwares and gather experimental data and monitoring information are described. We also present demonstration examples to illustrate the use of the SensLAB testbed and encourage researchers to test and benchmark their applications/protocols on a large scale WSN testbed

Large-scale mobile sensing enabled internet-of-things testbed for smart city services

Smart cities are one of the key application domains for the Internet-of-Things paradigm. Extending the Web into the physical realm of a city, by means of the widespread deployment of spatially distributed Internet-addressable devices with sensing and/or actuation capabilities, allows improving efficiency of city services. Vehicles moving around the city become excellent probes when the objective is to gather information across the city in a cost effective manner. Public transportation fleets, taxis, or vehicles such as waste collection trucks cover most of the urban areas with a limited number of vehicles. This paper presents the deployment of a large scale Internet-of-Things testbed that has been carried out in the city of Santander. It extends previous descriptions by providing a specification of one of the unique features of the testbed, namely, the devices that have been installed on 140 buses, taxis, and vans that every day drive around the city. Besides the physical characteristics of the devices installed and the lessons learnt during the deployment, the paper introduces the three mobile sensing network strategies used for distributing the data gathered. Finally, the paper sketches some of smart city services which might be provided using the information coming from the mobile IoT devices.This work has been partially funded by Research Project SmartSantander, under FP7-ICT-2009-5 of the 7th Framework Programme of the European Community. The authors would like to acknowledge the collaboration with the rest of partners within the consortium leading to the results presented in this paper.The authors would also like to express their gratitude to the Spanish government for the funding in the following project: “Connectivity as a Service: Access for the Internet of the Future,” COSAIF (TEC2012-38574-C02- 01)

Shawn: A new approach to simulating wireless sensor networks

We consider the simulation of wireless sensor networks (WSN) using a new approach. We present Shawn, an open-source discrete-event simulator that has considerable differences to all other existing simulators. Shawn is very powerful in simulating large scale networks with an abstract point of view. It is, to the best of our knowledge, the first simulator to support generic high-level algorithms as well as distributed protocols on exactly the same underlying networks.Comment: 10 pages, 2 figures, 2 tables, Latex, to appear in Design, Analysis, and Simulation of Distributed Systems 200

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves