Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Abstract

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs.

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

“…In this approach, MH is used to generate the samples from a stationary distribution. In [38], the authors propose a spatialtemporal data gathering mechanism based on MH with delayed acceptance. This approach allows harvesting compressive data by sequentially visiting small subsets of nodes along a routing path.…”

Redução de Dados em Redes de Sensores sem Fio Baseada em Análise de Dispersão

“…Esta técnica aplica uma matriz de detecções Φ ∈ R m×n em um vetor de sinal n-dimensional x = (x 1 , ..., x n ) T a fim de obter um sinal m-dimensional y ∈ R m . A reconstrução dos dados, segundo [Zheng et al 2017], pode ser realizada através da propriedade Restricted Isometry Property (RIP) 4 , que garante a recuperação de x a partir do sinal comprimido y se m ≥ k log n/k. O trabalho de [Masoum et al 2013], utiliza o princípio de amostragem adaptativa para transmitir apenas as detecções que indicam uma mudança significante no ambiente sensoriado.…”

A fair credit-based incentive mechanism for routing in DTN-based sensor network with nodes’ selfishness

“…To solve this problem, we can implement the WSN based on delay tolerant network (DTN) using autonomous air vehicle as a data ferry [4]. DTN [5,6] has currently been focused as a solution for communication in discontinuous networks, such as satellite sensor network [7], vehicular sensor network [8], and mobile sensor network [9]. Therefore, DTN has become a kind of important network architecture for WSNs.…”