Energy-Efficient Algorithm to Construct the Information Potential Field in WSNs

The information gradient-based routing and navigation protocols have been proved to be effective when collecting data from the distributed wireless sensor networks, because the data collector can achieve the global objective through local greedy decision based on the information gradient. An efficient method to establish this information gradient is to solve a discrete approximation to the harmonic function, which is called information potential field (IPF). However, the energy-efficient and quick convergence methods to construct the IPF should be fully investigated to trade off the energy efficiency and the quality of the IPF, especially in the large-scale networks with high dynamics. In this paper, two algorithms are proposed to efficiently construct the IPF, including hierarchical skeleton-based construction algorithm and value estimating substitution algorithm. Both algorithms obey the typical hypotheses on WSN settings and the gossip-styled propagation principle. In addition, we propose the advanced approaches to construct the IPF to tackle the challenges of its practical applications, such as obstacles, task priorities, and sensor energy budget. Comprehensive simulation results show the feasibility of the proposed algorithms, which can reduce the number of iterations to reach a convergence status by 80% so as to conserve energy, and they perform well considering the requirements of real applications.

I. Introduction

For the traditional data collection protocols using static sinks in wireless sensor networks (WSNs), the energy of sensor nodes near the sink will be depleted dramatically that shortens the lifetime of the network. Recently mobile sinks have been introduced to collect data and to effectively achieve the goal of extending the lifetime of the network through balancing the energy consumption of the entire network [1]. An example application using mobile sinks is the forest fire detection system, where numerous sensors are deployed to detect fire emergency based on the temperature and smoke concentration, which are related to the fire emergency event. Mobile sinks with fire-fighters move around the forests to collect the data from the sensor nodes and data-driven decisions effectively navigate fire-fighters to the fire emergency spots.