%0 Journal Article
%T Energy-Aware Distributed Intelligent Data Gathering Algorithm in Wireless Sensor Networks
%A Rongbo Zhu
%A Yingying Qin
%A Jiangqing Wang
%J International Journal of Distributed Sensor Networks
%D 2011
%I Hindawi Publishing Corporation
%R 10.1155/2011/235724
%X To plan the data collecting path for the mobile collector in wireless sensor network (WSN), an efficient energy-aware distributed intelligent data gathering algorithm (DIDGA) is proposed, which includes cluster formation and path formation phases. In cluster formation phase, an energy-efficient distributed clustering scheme is proposed to form a coverage-efficient WSN, which constructs a minimum connected dominating set (MCDS) based on maximal independent sets (MISs) in distributed and localized manner, and the node with more power is selected to be the cluster head in turn to prolong the network lifetime. In path formation phase, a path formation optimized algorithm (PFOA) is proposed to resolve the path formation NP problem with dynamic requirements. Then DIDGA uses the cluster head relay mechanism for planning the data gathering path. Compared with existed algorithms, detailed simulation results show that the proposed DIDGA can reduce average hop counts, average data gathering time, energy consumption, increase the efficiency of event detection ratio and prolong the network lifetime. 1. Introduction Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes [1], which consist of sensed and data processing, and communicating components, leverage the idea of wireless sensor networks (WSNs) [1, 2]. Typical applications of WSNs are the unmanned environmental monitoring, military surveillance, unmanned health monitoring, target tracking, inventory management, multimedia transmitting, and so on [3, 4]. Considering that battery is the main source of energy for the sensor nodes, how to reduce the high-energy expenditure in multihop routing and extend WSN's lifetime is a major challenge [2, 4]. One important task of WSNs is to collect useful information from the sensory field [5]. For a large-scale, data centric sensor network, it is inefficient to use a single, static data sink to gather data from all sensors [6, 7]. In some applications, sensors are deployed to monitor separate areas. In each area, sensors are densely deployed and connected, while sensors that belong to different areas may be disconnected. Unlike fully connected networks, some sensors cannot forward data to the data sink via wireless links [8, 9]. In some complex terrain environment, especially in noise interference and mobile case, how to effectively gather data is a challenge task with limited power. In general, most data-gathering schemes aim to prolong lifetime of WSNs by saving power consumption and
%U http://www.hindawi.com/journals/ijdsn/2011/235724/