Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs), Data Aggregation Trees (DATs) are employed for energy efficient data gathering. Energy efficient data collection is a primary requirement in the smart world of Internet of Things (IoT) as it facilitates to extend the survivability of the network. DATs gather data efficiently by employing data aggregation functions at the aggregator nodes. The employed aggregation function influences the cost of communication and cost of computation at a node. The study in this paper presents the techniques to estimate the communication and computation costs incurred for DAT construction, asymptotically. The strength of the proposed techniques is its ability to enable the estimation of best, average and worst case cost of DAT construction and rescheduling scenarios. Based on the asymptotic analysis, the study in this paper demonstrates the utilization of the proposed techniques to estimate the best and worst cases for communication and computation cost to meet the design objective of adhoc WSN deployments.