3. Control Systems

Pervasive computing systems are built around a controller that coordinates actuation with sensing. But which principles govern this coordination process? Is each system unique, applying its own principles? Or do all the systems have something in common, so that we can learn from successful examples how to build our own system? In this chapter, we will try to demonstrate that the latter is indeed the case. The range of sensors that can be used to discover context in pervasive computing is impressive. Luckily, most of these sensors are based on the same principle, saying that a physical quantity variation results in an electrical voltage at the terminals of the sensor. This allows us to restrict the scope here only to sensors that measure temperature, light, touch, and distance. The story starts with the analog voltage produced by a sensor and carrying information about the environment. This signal is digitized and processed by a software controller that usually makes use of machine learning to extract context. In this chapter, two types of control principles will be discussed: deliberative, which is slow, but strong in strategical searching and planning and reactive, that offers a fast reaction instead of deep thinking. Finally, the controller makes decisions that affect back the environment. In order to execute these decisions, the controller sends out commands that eventually reach the actuators. Examples of actuators are electrical heaters, motors, light sources, and simple screens and displays. The goal of this chapter is to demonstrate the principles behind the most commonly used types of control systems.