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 prove that the problem is NP-hard for practical scenarios where such continuity cannot be supported. We then present a mixed integer linear programming (MILP) formulation to obtain the optimal solution for the practical problem. Further, 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 MILP.

In this paper, we address the problem of scheduling a set of periodic real-time messages in a wireless network with the objective of minimizing the total energy consumption while meeting deadline and reliability constraints. We formally prove that this problem is NP-hard and solve it in two stages. First, we consider a simple model that assumes that the wireless channel is completely reliable and the network is fully provisioned. Using the technique of employing multiple hop-by-hop transmissions instead of a single direct hop transmission as the basis, we prove that the strategy of choosing the hop distances such that they are equidistant is optimal in terms of energy consumption under the deadline constraint. Based on the intuition provided by the optimal strategy, we present heuristic scheduling algorithms for a more realistic wireless channel model and network condition. Our simulation results show that the proposed scheduling algorithms provide significant energy savings over the baseline algorithms

Recent technological advances have opened up several distributed real-time applications involving battery-driven embedded devices with local processing and wireless com- munication capabilities. Energy management is the key is- sue in the design and operation of such systems. In this pa- per, we consider a single-hop networked real-time embed- ded system where each node supports both dynamic volt- age scaling (DVS) and dynamic modulation scaling (DMS) power management techniques to tradeoff time for energy savings. In this model, we address the problem of schedul- ing periodic complex tasks where each task consists of several precedence constrained message passing sub-tasks. Our contributions towards this problem are two fold. First, we analyze the system level energy-time tradeoffs consider- ing both the computation and communication workloads by defining a novel energy gain metric. We then present static (centralized) and dynamic (distributed) energy gain based slack allocation algorithms which reduce the total energy consumption, while guaranteeing the ready time, deadline and precedence constraints. We compare the performance of the proposed algorithms with several baseline algorithms through simulation studies. Our results show that the pro- posed algorithms perform significantly better than the base- line algorithms for the simulated conditions. Finally, we identify several interesting energy-aware research problems in the area of networked real-time embedded systems.