Obstacle mapping in wireless sensor networks via minimum number of measurements

Abstract

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 twodimensional 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.

Compressed Sensing with Applications in Wireless Networks

“…In contrast, a method to track moving objects using a dynamic SLF model, as well as identifying the static ones, was reported in [141]. Exploiting the sparse occupancy of the monitored area by the target objects, sparsity-leveraging algorithms for constructing obstacle maps were developed [220,158,219]. This work adopts a related data model, but mainly focuses on the channel gain map construction for CR applications.…”

Compressed Sensing with Applications in Wireless Networks

“…In contrast, a method to track moving objects using a dynamic SLF model, as well as identifying the static ones, was reported in [141]. Exploiting the sparse occupancy of the monitored area by the target objects, sparsity-leveraging algorithms for constructing obstacle maps were developed [220,158,219]. This work adopts a related data model, but mainly focuses on the channel gain map construction for CR applications.…”

Geometria de Aquisição Circular com Múltiplos Sensores para Mapeamento Através de Paredes utilizando Sinais de RF

“…Contudo, se comparada ao esquema de aquisição com múltiplos sensores proposto em [10], a MCG obtém um número médio de raios por pixel aproximadamente 20% superior. Como a função de distribuição acumulada (CDF - Fig.…”

“…Cumulative Distribution Function) (CDF) na Fig. 6b indica, enquanto praticamente nenhum pixel na geometria proposta em [10]é percorrido por 13 raios, na MCG 50% dos pixels são atravessados por mais de 13 raios 1 .…”

“…(a) Número de raios por pixel; (b) CDF do número de raios por pixel para duas geometrias de aquisição com 78 sensores: MCG (azul) e a proposta em[10] (vermelho).…”