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ACM Transactions on Sensor Networks (TOSN), Volume 1 Issue 1, August 2005

Introduction
Feng Zhao
Pages: 1-2
DOI: 10.1145/1077391.1077392

Navigation protocols in sensor networks
Qun Li, Daniela Rus
Pages: 3-35
DOI: 10.1145/1077391.1077393
We develop distributed algorithms for adaptive sensor networks that respond to directing a target through a region of space. We model this problem as an online distributed motion planning problem. Each sensor node senses values in its perception...

Integrated coverage and connectivity configuration for energy conservation in sensor networks
Guoliang Xing, Xiaorui Wang, Yuanfang Zhang, Chenyang Lu, Robert Pless, Christopher Gill
Pages: 36-72
DOI: 10.1145/1077391.1077394
An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the...

SeRLoc: Robust localization for wireless sensor networks
Loukas Lazos, Radha Poovendran
Pages: 73-100
DOI: 10.1145/1077391.1077395
Many distributed monitoring applications of Wireless Sensor Networks (WSNs) require the location information of a sensor node. In this article, we address the problem of enabling nodes of Wireless Sensor Networks to determine their location in an...

A framework for time indexing in sensor networks
Guanghui He, Rong Zheng, Indranil Gupta, Lui Sha
Pages: 101-133
DOI: 10.1145/1077391.1077396
In this article, we define the time-indexing problem as the in-network storage and querying of sensor network data based solely on the time attribute. We argue qualitatively why existing storage schemes may be insufficient as solutions. We...

A kernel-based learning approach to ad hoc sensor network localization
Xuanlong Nguyen, Michael I. Jordan, Bruno Sinopoli
Pages: 134-152
DOI: 10.1145/1077391.1077397
We show that the coarse-grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. This stems from an observation that the...