Open AccessObstacle mapping in wireless sensor networks via minimum number of measurements
Author(s) -
Moshtaghpour Amirafshar,
Akhaee Mohammad Ali,
Attarifar Masoud
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2014.0391
Subject(s) - obstacle , computer science , randomness , wireless , algorithm , compressed sensing , wireless sensor network , transmitter , computer engineering , representation (politics) , mathematics , telecommunications , computer network , channel (broadcasting) , statistics , political science , law , politics
In this study, a group of wireless sensors are tasked to trace indoor obstacles without the need to sense them, directly. The authors introduce a novel framework based on compressed sensing theory that allows sensors to map two‐dimensional spatial details, non‐invasively. By exploiting an alternative projection method which reduces the randomness nature of previous works, the resulting measurement matrix can provide linear samples from an unknown environment more efficiently. It is shown that how sparse representation of the spatial parameters in some domains can be utilised in order to realise obstacle mapping with minimum number of wireless transmissions and receptions. In addition, theoretical analyses along with simulation results illustrate premier performance of their framework. Furthermore, they test their method in different circumstances and show how drawbacks such as walls, bulkheads, and environmental constraints can affect the reconstruction performance. Therefore, they proposed two algorithms (i.e. reducing wall effect and reducing bulkhead effect) in order to decrease the impression of walls and bulkheads which is supported theoretically. Finally, a well‐applicable scenario based on their framework is defined and proposing the next best transmitter algorithm remarkable results are achieved.