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.

One of the important objectives in Wireless Sensor Networks (WSNs) is to minimize energy consumption while satisfying delay constraints. Although, several energy-aware MAC layer protocols exist, the absence of channel adaptation and load adaptation results in energy wastage due to retransmissions and low success rate. In this paper, we propose an Adaptive- CSMA/CA protocol which accounts for varying channel and load conditions at a node by influencing the selection of either low energy or low delay transmission option. We devise an energy-delay metric that helps a node select the best message and modulation to obtain joint reduction in energy and delay at the time of transmission. Our simulation results show that our proposed Adaptive-CSMA/CA scheme results in upto 87% improvement in energy consumption under varying channel conditions and upto 150% higher success ratio under varying load as compared to the CSMA/CA specified in IEEE 802.15.4.