Nowadays, many embedded systems such as mobiles and laptops to satellites and robotic systems, are often driven by limited energy sources like batteries. Hence, these devices are not only judged by their real-time and functional performance but also by their efficiencies in terms of energy management. Energy minimization is one of the primary design requirements for distributed embedded systems. The growing importance of complex applications in the distributed system introduces significant challenges in reducing energy consumption. This work addresses the problem of scheduling a real-time application abstracted as a directed acyclic graph (DAG), on a Dynamic Voltage and Frequency Scaling (DVFS) enabled uniform multiprocessor system, by proposing an efficient heuristic strategy called Energy-efficient Real-time DAG Scheduler (ERS). ERS effectively selects the appropriate processing frequency for each task-to-processor pair in the system such that overall energy saving is maximized while satisfying constraints related to resource, task precedence and deadline. We have evaluated the performance of the proposed framework using real-world benchmark applications. Obtained results reveal that ERS is able to deliver better performance in terms of energy savings than state-of-the-art works such as GSPM, SSPM, and PSPM.