Tree-Based Broadcasting in Multihop Radio Networks
IEEE Transactions on Computers
Introduction to algorithms
The algorithm design manual
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
RNC-Approximation Algorithms for the Steiner Problem
STACS '97 Proceedings of the 14th Annual Symposium on Theoretical Aspects of Computer Science
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Better approximation bounds for the network and Euclidean Steiner tree problems
Better approximation bounds for the network and Euclidean Steiner tree problems
Physiological value-based efficient usable security solutions for body sensor networks
ACM Transactions on Sensor Networks (TOSN)
A content-guided publish/subscribe mechanism for sensor networks without location information
Computer Communications
Hierarchical Ring-Based Data Gathering for Dense Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Journal of Network and Computer Applications
Wireless Personal Communications: An International Journal
Tight analysis of shortest path convergecast in wireless sensor networks
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Tight analysis of shortest path convergecast in wireless sensor networks
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
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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), @c, 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 @a (=1-@c), 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 @b-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 @b-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 @c at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.