Heuristics for scheduling data gathering with limited base station memory

In this paper, we analyze scheduling in data gathering networks with limited base station memory. The network nodes hold datasets that have to be gathered and processed by a single base station. A dataset transfer can only start if sufficient amount of memory is available at the base station. As soon as a node starts sending a dataset, the base station allocates a block of memory of corresponding size. The memory is released when computations on the dataset finish. We prove that minimizing the total data gathering and processing time is strongly NP-hard. As this problem is a special case of a specific resource constrained flow shop scheduling problem, for which an exact exponential algorithm is known, we propose several simple polynomial-time heuristics and two groups of local search algorithms, and test their performance in computational experiments. We show that the local search algorithms produce very good schedules, and one of the simple heuristics delivers solutions of comparable quality in a very short time.