Wireless sensor networks play an essential role in today’s Internet of Things (IoT) systems. One of the most common applications is smart indoor spaces and detecting human activities in such areas. It is crucial, for these systems, to collect data, analyze it, and make decisions based on the analysis. Even though this is a quite well-defined pipeline of processing, the overall device energy consumption can be significant for wireless sensor systems to ensure the fidelity of data and longevity of the system. In this thesis, we first discuss various energy management methods and their respective merits. We also discuss the energy requirements and constraints of common IoT applications using sensor networks. We focus on IoT systems for human activity detection in indoor spaces. Then, we propose a method to maximize energy efficiency for these smart spaces. Lastly, we demonstrate the effectiveness of our proposed method, using both simulation and experimentation, with the help of our real Smart Space deployment. Our smart environment deployment consists of a variety of sensors including ultrasonic, microwave and vibration sensors. We demonstrate that our energy efficiency method does not affect data quality, thereby maintaining the accuracy of human activity detection. Our method shows up to 30% energy efficiency improvement over a system with no energy management techniques used.