Improving Wireless Sensor Network Lifetime Using a Combination of Compact Sensing and Swarm Intelligence for Sleep/Wake Scheduling and Routing

One of the main challenges in wireless sensor networks is the limited energy and operational lifespan of nodes. In this paper, compressive sensing theory is employed to reduce the number of required transmissions, thereby extending the network’s lifetime. Data in many wireless sensor networks exhibits both spatial and temporal correlation. The proposed method leverages both forms of correlation to minimize the mean squared error of the reconstructed signal. Furthermore, a sleep/wake scheduling mechanism is applied to decrease node energy consumption. Selecting the optimal set of awake nodes is an NP hard problem; thus, a genetic algorithm is utilized to determine which nodes should remain active. Once the active nodes are identified, an ant colony optimization algorithm constructs an efficient data aggregation tree. Simulation results demonstrate that the proposed approach—combining node selection and routing—reduces reconstruction error by more than 48% and increases network lifetime by over 18% compared to existing methods.