Over the last twenty years, energy conservation has always been of great importance to individuals, societies and decision makers around the globe. As a result, IT researchers have shown a great interest in providing efficient, reliable and easy-to-use IT services which help users saving energy at home by making use of the current advances in Information and Communications Technology (ICT). Driven by the aforementioned motivation, we developed SMARTENERGY.KOM, our framework for realizing energy efficient smart homes based on wireless sensor networks and human activity detection. Our work is based on the idea that most of the user activities at home are related to a set of electrical appliances which are necessary to perform these activities. Therefore, we show how it is possible to detect the user's current activity by monitoring his fine-grained appliance-level energy consumption. This relation between activities and electrical appliances makes it possible to detect appliances which could be wasting energy at home. Our framework is organized in two components. On one hand, the activity detection framework which is responsible for detecting the user's current activity based on his energy consumption. On the other hand, the EnergyAdvisor framework which utilizes the activity detection for the purpose of recognizing the appliances which are wasting energy at home and informing the user about optimization potential.

Advances in ubiquitous computing over the last decade have allowed us to inch closer to the realization of true smart homes. Many sensors are already embedded in our living environments which can monitor several environmental parameters such as temperature, humidity, brightness and appliancelevel power consumption. However, in order to achieve the primary goal of the smart home, we should be able to detect, identify, and localize the entities inside it. Therefore, the user detection, identification and localization problems represent a crucial facet of the challenges introduced by the smart home problem. Our approach towards solving these challenges entailed the usage of Bluetooth technology for user identification and tracking, alongside a Wireless Local Area Network setup to collate the sensor data at a centralized server such as a home gateway which subsequently processed and stored the entries. Moreover, we have studied the efficacy of various pattern recognition algorithms for real time processing and decision modeling on the received data. We have hence demonstrated our solution represents a non-intrusive, inexpensive and energy-conserving methodology to solve an essential part of the smart home problem by integrating already existent devices and infrastructure in an innocuous manner to obtain good results with minimum overhead.

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