Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Using proximity and quantized RSS for sensor localization in wireless networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Sensor localization in concave environments
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
Sensor localization can be divided into two categories: range-based approaches, and rang-free approaches. Although range-based approaches tend to be more accurate than range-free approaches, they are more sensitive to errors in distance measurement. Despite of the efforts on recovering sensors’ Euclidean coordinates from erroneous distance measurements, as will be illustrated in this paper, they are still prone to distance errors, particularly in an obstruction abundant environment. In this paper, we propose a new algorithm for sensor localization based on Multiscale Radio Transmission Power(MRTP). It gradually increases the scale level of transmission power, and the distance is determined by the minimal scale of received signals. Unlike the range-based approaches, which treat each measured distance as an approximation of the true one, in our new approach, the measured distance serves as a constraint that limits the feasible location of sensors. Our simulations have shown that the MRTP-based approach is able to provide accurate and robust estimation of location, especially in an area abundant in obstructions, where most current approaches fail to perform well.