minimum energy probabilistic reliable data delivery in wireless sensor networks
Many sensor network applications only require probabilistic data delivery, as they can tolerate some missing data samples. For example, in environmental monitoring, missing temperature, pressure and humidity level samples can often be inferred by spatial and/or temporal interpolations. In this paper we propose and study an adaptive p-persistent CSMA-based media access control protocol that supports end-to-end probabilistic reliability for sensor networks on a hop-by-hop basis. In an effort to reduce the probability of packet collisions, first we tune the carrier sensing range of the nodes; then given an end-to-end reliability requirement, we determine the optimal allocation of per-hop reliability requirements on each route to minimize the expected total number of transmissions needed; finally, our adaptive p-persistent CSMA protocol tunes its link persistence probability to further reduce the expected total number of transmissions, and thereby minimizes the energy consumption in the network. We formulate this latter problem as a constrained optimization problem, and then derive an algorithm to adapt the link persistence probabilities using the Lagrangian dual decomposition method.
reliability data delivery sensor networks