In Distributed Control Systems (DCS) the distribution of sensor data needs to be optimized in order to reduce the client process redundancy, i.e.: same sensor data processing in different clients. This paper presents an implementation of Embedded Networked Sensors as Smart Resources that allows to access to high-level information abstracting the user from low level mechanisms. The distributed services provided by Smart Resources implement self-aware adaptation mechanisms in order to suit the performance to the context. Information integrity must be also ensured during this adaptation. The Smart Resource implementation is based on a RGBD sensor. Through a set of experiments is evaluated the performance of the reconfiguration and adaptation mechanisms and detailed the influence of the data integrity according to the provided service.

Current trends in the development of industrial applications enforce the use of wireless networks to communicate the system nodes mainly to increase flexibility and reliability of these applications and to reduce the implementation cost. However, in control applications, as consequence of the latency and jitter generated by the network, not always the results achieved by the experimental results and desired performance are coherent. This is due to the imprecise models for system analysis and design used and the non appropriated validation methods and platforms to support these models. Therefore this paper presents a method to achieve an optimal system configuration in order to fulfil the desired performance in control applications with a significant energy saving and minimum delay.

Geometric representation of the environment, known as mapping, plays an important role in mobile robotics as it support various tasks such as localization, path planning and motion control. Line models represent a popular approach to represent the geometric features of the environment. The mapping problem gives rise to a variety of solutions for using different exteroceptive sensors (sonar, infrared, laser, vision, etc.). One of the possible choices is to use an infrared sensor (IR sensor). These devices are inexpensive, practical and widely available. Infrared sensors are largely used in obstacle avoiding processes because they are fast and cheap, and require only simple signal processing. Although the collision avoidance routine has received good attention for many years, the infrared feature mapping has been neglected. A problem with this kind of sensor is its short range (about one meter). This problem forces to an incremental mapping approach, so the robot has to move and concurrently model the environment. In this paper we present an IR sensor able to measure distances based on the light that is back-scattered from objects. This IR sensor is used for line-based map building purposes, so we also present a methodology for line extraction, range data clustering and line segmentation. The experimental test has been carried out in a long corridor, and results show the usefulness of the IR sensor and the methodology we propose for feature extraction and line-based mapping processes.

This paper describes the design and implementation of a CAN time-triggered based communication protocol named SCoCAN, (shared channel on CAN), intended for use in distributed control systems, as is the case of autonomous mobile robots. This protocol enables real time communication between different intelligent nodes (sensors, actuators and controllers). The SCoCAN protocol has been implemented in the YAIR robot nodes, producing minimum jitter in the sensor-control-actuator loops. This hybrid protocol follows a scheme between pure TTP (time triggered protocol) and ETP (event triggered protocol), that guarantees acceptable response times and is deterministic