Minimum-Latency Aggregation Scheduling (MLAS ) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for global-scale information to compute the cumulative interferences at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log 3 n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [9] . We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.
Assuming a model where every node in the network knows its position, the network diameter and the number of neighbors, Li et al. REF presented a distributed algorithm with a latency bound of O(K ), where K is the logarithm of the ratio between the length of the longest link and that of the shortest link.
Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model
MSWIM '10
Computer Science