Heuristics for Scheduling Data Aggregation Trees in Wireless Sensor Networks

Abstract

Sensor networks are the fundamental building blocks for Internet of Things (IoT). Gather ing data from these networks is a primary requirement in applications like environmental monitoring, military surveillance, industrial monitoring and structural health monitoring. By incorporating energy efficient data gathering techniques, the lifetime of these net works is improved. One of the techniques to extend Network Lifetime (NL) is to use Data Aggregation Trees (DAT). NL maximization using DATs is a known Non-deterministic Polynomial time Complete (NP-Complete) problem. A lot of research efforts to sched ule DATs using various algorithmic strategies based on heuristics, approximation and evolutionary techniques are seen. Researchers have typically employed single parameter heuristics using Residual Energy (RE) parameter of the sensor nodes to schedule DATs. newlineThe major contributions of the work proposed in this thesis are as follows. A generic model which is an abstraction of the method employed to schedule DATs is proposed. The model is represented as a sequence of four stages. From an application perspective, the proposed generic model presents the stages from deployment of sensor nodes in the Area of Interest (AoI) to gathering of data from the sensor network newline newline