Joint Scheduling of Tasks and Messages for Energy Minimization in Interference-Aware Real-Time Sensor Networks

Emerging applications of wireless sensor networks mandate extensive in-network information processing and communication while requiring energy efficiency. Dense deployments of wireless nodes and shared wireless channel pose severe interference constraints. Several scheduling schemes in literature propose interference-aware message scheduling with the objective of energy minimization, but the problem of joint scheduling of tasks and messages for energy minimization in interference-aware manner has not been studied. We formulate a Mixed Integer Linear Program (MILP) for the joint scheduling of computation tasks and communication messages in data collection tree based networks. We propose a three phase heuristic which first performs joint scheduling of tasks and messages and then reduces the energy consumption of the network by using the energy saving techniques like Dynamic Voltage Scaling (DVS) for tasks and Dynamic Modulation Scaling (DMS) for messages. These techniques tradeoff energy with latency. However, in dense deployments of WSN with small transmitter receiver distances, DMS does not monotonically reduce the energy consumption. We use this knowledge to efficiently perform slack allocation. We present a Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solution. We evaluate the performance of the proposed algorithm for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithm competes closely with that of the MILP solution.