@article{558d4b4914d9467ea39bf7216d6da39f,
title = "Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (DAC) in wireless sensor networks",
abstract = "Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast - process of routing data from many sources to a sink - is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected. The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (= 1 - γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms' performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.",
keywords = "Convergecast, Data aggregation, Energy-efficiency, Spanning trees, Wireless sensor networks",
author = "S. Upadhyayula and Sandeep Gupta",
note = "Funding Information: Sandeep Kumar S. Gupta is an Associate Professor in the Ira A. Fulton School of Engineering in the Department of Computer Science and Engineering and Arizona State University. He received the B.Tech degree in Computer Science and Engineering (CSE) from Institute of Technology, Banaras Hindu University, Varanasi, India, M.Tech. degree in CSE from Indian Institute of Technology, Kanpur, and M.S. and Ph.D. degree in Computer and Information Science from The Ohio State University, Columbus, OH. He has served at Duke University, Durham, NC as a post-doctoral researcher; at Ohio University, Athens, OH as a Visiting Assistant Professor; and at Colorado State University, Ft. Collins, CO as an Assistant Professor. His current research is focused on dependable and adaptive distributed systems with emphasis on wireless sensor networks, mobile computing, and biomedical applications. His research has been funded by the National Science Foundation (NSF), the Consortium for Embedded Systems (CES), Intel Corporation and MediServe Information Systems. He is co-author of the book “Fundamentals of Mobile and Pervasive Computing” published by McGraw Hill, 2004. He has co-guest edited special issues of IEEE Personal Communication Magazine (on Pervasive Computing, 2001), IEEE Transactions on Computers (on Mobile Computing and Databases, 2002), ACM/Baltzer Winet (Advances in Mobile Computing and Wireless Systems, 2003) and ACM/Baltzer Monet (on Pervasive Computing, 2001). He was program chair for Int{\textquoteright}l workshop on Group Communication and a program co-chair for Int{\textquoteright}l Workshop on Wireless Networks and Mobile Computing, Int{\textquoteright}l Workshop on Pervasive Computing (PC{\textquoteright}00), and Future Trends in Distributed Computing Systems (FTDCS{\textquoteright}01), Int{\textquoteright}l Workshop on Wireless Security and Privacy (WiSPr{\textquoteright}2003). He is a member of the ACM and a senior member of the IEEE. He heads the IMPACT (Intelligent Mobile and Pervasive Applications and Computing Technologies) Lab at Arizona State University. For information about his recent research projects and publications visit http://impact.asu.edu . Funding Information: The authors would like to thank the reviewers for their constructive comments which helped to improve the quality of this paper. This work was supported in part by the NSF grant # ANI-0196156. ",
year = "2007",
month = jul,
doi = "10.1016/j.adhoc.2006.04.004",
language = "English (US)",
volume = "5",
pages = "626--648",
journal = "Ad Hoc Networks",
issn = "1570-8705",
publisher = "Elsevier",
number = "5",
}