Neighbors and Relative Location Identification Using RSSI in a Dense Wireless Sensor Network

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Mohammad M Abdellatif (Author) Other; Jose Manuel Oliveira (Author) FEP; Manuel Ricardo (Author) FEUP

Conference proceedings International

Title: 2014 13TH ANNUAL MEDITERRANEAN AD HOC NETWORKING WORKSHOP (MED-HOC-NET)

Pages: 140-145

13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)

Piran, SLOVENIA, JUN 02-04, 2014

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 10 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-009-PMB

DOI: 10.1109/medhocnet.2014.6849116

Abstract (EN): Wireless Sensor Networks (WSNs) are made of a large amount of small devices that are able to sense changes in the environment, and communicate these changes throughout the network. An example of such network is a photo voltaic (PV) power plant, where there is a sensor connected to each solar panel. Because such a network covers a large area, the number of sensors can be very large. The task of each sensor is to sense the output of the panel which is then sent to a central node for processing. As the network grows, it becomes impractical and even impossible to configure all these nodes manually. And so, the use of self-organization and auto-configuration algorithms becomes essential. In this paper, two algorithms are proposed that can be used to allow each node in the network to automatically identify its closest neighbors as well as its relative location in the network using the value of the Received Signal Strength indicator (RSSI) of the messages sent back and forth during the setup phase. Results show that the error in neighbor identification decreases as we increase the number of RSSI values used for decision making. Additionally, the number of nodes in the network affects the setup error greatly. However, the value of the error is still acceptable even for high number of simulated columns.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6