A Comprehensive Review and Performance Analysis of Data Gathering Algorithms for Wireless Sensor Networks

Wireless sensor networks comprise of vast numbers of sensor nodes deployed to monitor a particular event (fire, intrusion, etc.) or measure a parameter (like temperature, pressure) value representative of the physical condition of the ambient environment. There is a growing need of using energy-efficient data gathering algorithms that can effectively aggregate the monitored/measured data from the individual sensor nodes through a properly constructed communication topology and transmit a single representative data to a control center (sink) that is typically located far away from the network field. In order to maximize node lifetime and be fair to all nodes in the network, such a communication topology has to be dynamically constructed for every round of data gathering by taking into consideration available energy levels of sensor nodes. This paper presents a comprehensive description of two broad categories of data gathering algorithms for wireless sensor networks – the classical algorithms that are not energy-aware and modern energy-aware data gathering algorithms. These algorithms can also be classified based on the communication topology they choose to construct and use for data gathering. The authors also present an extensive simulation study that demonstrates the individual as well as the comparative performance of these data gathering algorithms. DOI: 10.4018/jitn.2012040101 2 International Journal of Interdisciplinary Telecommunications and Networking, 4(2), 1-29, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. are in the transmission range of each other. Due to all of the above resource and operating constraints, it will not be a viable solution to require every sensor node to directly transmit their data to the sink over a longer distance. Also, if several signals are transmitted at the same time over a longer distance, it could lead to lot interference and collisions. Thus, there is a need for employing energy-efficient data gathering algorithms that can effectively combine the data collected at these sensor nodes and send only the aggregated data (that is a representative of the entire network) to the sink. In this paper, we consider the problem of periodically gathering the data from the sensor nodes in the network. Accordingly, the data gathering algorithms operate in several rounds, and during each round, data from the sensor nodes are collected, aggregated and forwarded to the sink through a communication topology determined on an underlying network graph based on the transmission range of the sensor nodes. The data gathering algorithms differ in the communication topology used for data aggregation and forwarding: clusters (Heinzelman et al., 2000), chain (Lindsey et al., 2002), grid (Meghanathan, 2010a), connected dominating set (Meghanathan, 2010b) and spanning trees (Meghanathan, 2010c). The data gathering algorithms target to optimize one or more of these performance metrics: (i) Node lifetime, (ii) Delay per round, (iii) Energy per round, and (iv) Energy * Delay per round. In this paper, the node lifetime will be referred to as the number of rounds the network can run before sustaining the first node failure due to exhaustion of battery charge; this definition for node lifetime is also sometimes interpreted as the network lifetime in the literature. Throughout the paper, the terms ‘node’ and ‘vertex,’ ‘link,’ and ‘edge,’ ‘gathering’ and ‘aggregation’ are used interchangeably. They mean the same. The rest of the paper is organized as follows: • First, we explain the system model as well as the performance metrics used in the description and analysis of the different data gathering algorithms presented in this paper. • Afterwards, we then explains the clusterbased LEACH (Low-Energy Adaptive Clustering Hierarchy) data gathering algorithm (Heinzelman et al., 2000), which still serves as one of the two standards (the other one being the PEGASIS algorithm discussed in after this) to which the performance of any data gathering algorithm newly proposed in the literature is compared to. • Followed by a description of the chainbased PEGASIS (Power-Efficient Gathering in Sensor Information Systems) algorithm (Lindsey et al., 2002) as well as analyzes the different procedures available in the literature to form the chain (Meghanathan, 2009) as well as select the leader node (Shukla & Meghanathan, 2009) and their impact on the performance of PEGASIS. • Next, we present two grid block energy based hierarchical data gathering (GBEDG) algorithms (Meghanathan, 2010a) that differ from each other depending on whether the nodes within a grid are arranged as a cluster or as a chain. • We describe an algorithm to construct an energy-aware connected dominating set (ECDS)-based data gathering tree (Meghanathan, 2010b) that prefers to include nodes with higher energy level as the intermediate aggregating nodes of the tree. • Afterwards, a spanning-tree based energyaware maximal leaf nodes data gathering (EMLN-DG) algorithm (Meghanathan, 2010c) that prefers to include only nodes that have a relatively larger number of uncovered neighbors as well as a higher energy level as the intermediate nodes of the tree and in turn maximizes the number of leaf nodes of the data gathering tree. This section also presents an algorithm to calculate the delay per round of data gathering, which is also applicable to the ECDS-based data gathering algorithm described previously. 27 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/comprehensive-reviewperformance-analysis-data/67574?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Communications and Social Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2