The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Models and solutions for radio irregularity in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
MSP: multi-sequence positioning of wireless sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
Sequence-Based Localization in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Sensor Node Localization Using Uncontrolled Events
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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In recent years, many localization algorithms have been proposed for wireless sensor networks, in which the hop-count based localization schemes are attractive due to the advantage of low cost. However, these approaches usually utilize discrete integers to calculate the hop-counts between nodes. Such coarse-grained hop-counts make no distinction among one-hop nodes. More seriously, as the hop-counts between nodes increase, the cumulative deviation of hop-counts would become unacceptable. In order to solve this problem, we propose the concept of fine-grained hop-count. It is a kind of float-type hop-count, which refines the coarse-grained one close to the actual distance between nodes. Based on this idea, we propose a fine-grained localization algorithm (AFLA). In AFLA, we first refine the hop-count information to obtain fine-grained hop-counts, then use the Apollonius circle method to achieve initial position estimations, and finally further improve the localization precision through confidence spring model (CSM). We conduct the comprehensive simulations to demonstrate that AFLA can achieve 30% higher average accuracy than the existing hop-count based algorithm in most scenarios and converge much faster than the traditional mass-spring model based scheme. Furthermore, AFLA is robust to achieve an approximate 35% accuracy even in noisy environment with a DOI of 0.4.