Using proximity and quantized RSS for sensor localization in wireless networks

By Neal Patwari, A. Hero. ACM International Conference on Wireless Sensor Networks and Applications, 2003

368 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/647b196d485477983f05b09a94ae65df86ba0ff7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Henk Wymeersch
2. Moe Z. Win
3. Yuan Shen
4. Jaime Lien
5. James She
6. Guoqiang Mao
7. David E. Evans
8. Baris Fidan

In Electrical and Electronic Engineering

1. Moe Z. Win
2. Yuan Shen
3. Henk Wymeersch
4. Jaime Lien
5. James She
6. Wafa Shuaieb
7. Huthaifa Obeidat
8. Guoqiang Mao

Citations in the corpus

• A Theoretical Foundation of Network Localization and Navigation (2018, neutral): “[102] N.”
• Cooperative Localization in Wireless Networks (2009, background): “Received signal strength (RSS) exploit s the relation between power loss and the distance between sender and receiver [66].”
• Fundamental Limits of Wideband Localization— Part I: A General Framework (2010, background): “RSS is also a useful metric that uses the strength of the received signal to estimate the propagation distance betwe en nodes [9], [34], [36].”
• Localization for mobile sensor networks (2004, background): “Range-based approaches have exploited time of arrival [43], received signal strength [2, 37] time difference of arrival of two different signals (TDOA) [40], and angle of arrival (AOA) [36].”
• BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities (2018, result): “Patwari and Hero [72] also concluded that the error rate of the distance measurement is even higher than the RSS measurement-based experimental results.”
• Locating the nodes: cooperative localization in wireless sensor networks (2005, neutral): “As a binary quantization of RSS, it is clear that connectivity is less informative than RSS and will result in higher localization variance bounds [84].”
• Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “In our theoretical analysis, we found that we should expect at least a 50% increase in standard deviation of localization error compared to using unquantized RSSI values, even if the threshold is set optimally (Patwari and Hero, 2003).”
• A Review of Indoor Localization Techniques and Wireless Technologies (2021, background): “The proximity approach does not matter if the mobile and the AP are exposed to the same fading channel or not, as long as they are within the communication range [117].”