Darmstadt, TU, Master Thesis, 2016
Over the past few years technological advancements have supported the growth of the Internet of Things (IoT). The Internet of Things consists of (smart) objects embedded with sensors, actuators and controllers. These objects are connected to the Internet and are able to communicate with each other. The interconnection and communication of objects enable the creation of different application domains within the Internet of Things. Smart living is one of the major application areas for the Internet of Things. Sensors, actuators and controllers in a smart living environment (e.g. smart homes) are deployed anywhere; on objects or even on persons. As sensors have the capability to sense the environment, they can be used to collect useful information on location, motion, temperature, humidity, light, etc. Actuators can perform different actions based on data gathered from sensors, and controllers can process that data. Real-time situation awareness is one of the key tasks in a smart
living environment. Real-time recognition of situations is especially important in ambient assisted living environments, where elderly or disabled people need support in their everyday lives. Recognition of situations in real-time enables immediate identification of critical situations and provides just-in-time assistance.
To detect situations, data needs to be monitored, collected, analyzed and processed. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the inefficiency of continuously-running pattern/situation recognition algorithms, high requirement for processing capability and high frequency of the recognition process. Situation recognition algorithms must be executed constantly to handle the continuously generated data. For real-time recognition of situations in particular, these algorithms need to be executed permanently for all received data. The continuously-running recognition algorithms require high processing capabilities. The resource consumption of these algorithms is especially high when they are running on large sets of data. To overcome these problems there is a need for more intelligent approaches that are able to decide - based on target situation recognition purposes - which data
is important and should be processed and which algorithm should be used to process this data.
This study proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received. This allows reduction of the frequency of execution of the situation recognition algorithms, thus saving computational resources, such as CPU, memory, storage, bandwidth and power. Another advantage of our approach is that it can be applied to recognize situations in real-time, which is useful for ambient assisted living environments. We apply the proposed approach to implement a use case scenario on top of the universAAL IoT platform, which is an open-source platform for the development of IoT solutions.