Heuristic Approaches for Transmission Scheduling in Sensor Networks with Multiple Mobile Sinks
A large part of the energy budget of traditional sensor networks is consumed by the hop-by-hop routing of the collected information to the static sink. In many applications it is possible to replace the static sink with one or more mobile sinks that move in a sensor field and collect the data through one-hop transmissions. This greatly reduces the power consumption of the nodes, which can be further reduced by choosing the appropriate moment of transmission. In general, the transmission energy increases quickly with the distance, and thus it makes sense for the nodes to transmit when one of the mobile sinks is in close proximity. Seeing the node as an autonomous agent, it needs to choose its actions of transmitting or buffering the collected data based on what it knows about the environment and its predictions about the future. The sensor agent needs to appropriately balance the following two objectives: the maximization of the utility of the collected and transmitted data and the minimization of the energy expenditure. We introduce the cummulative policy penalty as an expression of this interdependent pair of requirements. As a baseline, we describe a graph-theory-based approach for calculating the optimal policy in a complete knowledge setting. Then, we describe and compare three heuristics based on different principles (imitation of human decision making, stochastic transmission and constant risk). We compare the proposed approaches in an experimental study under a variety of scenarios.
sensor networks mobile sink agents