End-to-end energy management in networked real-time embedded systems
Recent technological advances have opened up a wide range of distributed real-time applications involving battery-driven embedded devices with local processing and wireless communication capabilities. Some such applications include: wireless sensor networking, pervasive computing, and wireless industrial automation. Energy management is an integral part of the design and operation of such systems. In this work, we focus on the end-to-end energy management of data aggregation in distributed wireless sensor networks. Performing end-to-end energy management for the data aggregation application poses certain unique challenges particularly when the computational demands on the individual nodes are significant. In this paper, we address the problem of minimizing the total energy consumption of data aggregation with an end-to-end latency constraint while taking into account both the computational and communication workloads in the network. We consider a model where individual nodes support both Dynamic Voltage Scaling (DVS) and Dynamic Modulation Scaling (DMS) power management techniques and explore the energy-time tradeoffs these techniques offer. Specifically, we make the following contributions in this paper. First, we present an analytical problem formulation for the ideal case where each node can scale its frequency and modulation continuously. Second, we present a Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solution for the practical case where only few discrete frequency and modulation levels are supported by each node. Further, we analyze the system-level energy-time tradeoffs considering both the computation and communication workloads by defining a novel energy gain metric. Finally, we present polynomial time heuristic algorithms, which employ the energy-gain metric. We evaluated the performance of the proposed algorithms for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithms are comparable to that of the MILP solution.
wireless sensor networks energy-aware systems real-time and embedded systems scheduling