Sensor selection with correlated measurements for target tracking in wireless sensor networks

Liu, Sijia; Maşazade, Engin; Fardad, Makan; Varshney, Pramod K.

doi:10.1109/icassp.2015.7178728

Cited by 10 publications

(4 citation statements)

References 22 publications

(42 reference statements)

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“…However, the method selects one sensor node for the tasks at each step and does not consider the locations correlations of the sensing nodes. Literature [18,19] adopt an alternative conditional posterior Cramér-Rao lower bound (C-PCRLB) as the optimization criterion for node selection. Although the total number of participating nodes is limited by a time window, nodes are selected independently without considering the correlation among the observation values of nodes.…”

Section: Related Workmentioning

confidence: 99%

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Jiang

Shi

2018

Sensors

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In target tracking wireless sensor networks, choosing a part of sensor nodes to execute tracking tasks and letting the other nodes sleep to save energy are efficient node management strategies. However, at present more and more sensor nodes carry many different types of sensed modules, and the existing researches on node selection are mainly focused on sensor nodes with a single sensed module. Few works involved the management and selection of the sensed modules for sensor nodes which have several multi-mode sensed modules. This work proposes an efficient node and sensed module management strategy, called ENSMM, for multisensory WSNs (wireless sensor networks). ENSMM considers not only node selection, but also the selection of the sensed modules for each node, and then the power management of sensor nodes is performed according to the selection results. Moreover, a joint weighted information utility measurement is proposed to estimate the information utility of the multiple sensed modules in the different nodes. Through extensive and realistic experiments, the results show that, ENSMM outperforms the state-of-the-art approaches by decreasing the energy consumption and prolonging the network lifetime. Meanwhile, it reduces the computational complexity with guaranteeing the tracking accuracy.

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Section: Related Workmentioning

confidence: 99%

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Jiang

Shi

2018

Sensors

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“…Camera measurement noise is known to be statistically independent. RSSI measurement noise is considered correlated in many works [48][49][50] due to the influence of the environment when models such as (2) are adopted. However, as will be explained in Section 5 the RSSI measurement noise in our scheme has a negligible level of correlation and assuming uncorrelated measurement noise involves no practical influence in our scheme enabling the integration of RSSI measurements in RBFs.…”

Section: Camera and Rssi Measurement Integration Using Distributed Eifsmentioning

confidence: 99%

Efficient integration of RSSI for tracking using Wireless Camera Networks

2017

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This paper proposes a scheme that efficiently exploits synergies between RSSI and camera measurements in cluster-based target tracking using Wireless Camera Networks (WCNs). The scheme is based on the combination of two main components: a training method that accurately trains RSSI-range models adapted to the conditions of the particular local environment; and a sensor activation/deactivation method that decides on the individual activation of sensors balancing the different information contributions and energy consumptions of camera and RSSI measurements involved in sensing. The scheme also includes a distributed Extended Information Filter that integrates all available measurements. The combination of these components originates self-regulated behaviors that drastically reduce power consumption and computational effort with no significant tracking degradation w.r.t. existing schemes based exclusively on cameras. Besides, it shows better robustness to target occlusions. The proposed scheme has been implemented and validated in real experiments.

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“…Compared to the application of SDR in (33), the homogeneous QCQP leads to an SDP with a smaller problem size. We refer the readers to [22,Sec. V] and [20, Sec. V] for more details on the application of bilinear programming and SDR.…”

Section: Sensor Selection By Maximizing Trace Of Fisher Informationmentioning

confidence: 99%

Sensor Selection for Estimation with Correlated Measurement Noise

Liu,

Chepuri,

Fardad

et al. 2015

Preprint

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In this paper, we consider the problem of sensor selection for parameter estimation with correlated measurement noise. We seek optimal sensor activations by formulating an optimization problem, in which the estimation error, given by the trace of the inverse of the Bayesian Fisher information matrix, is minimized subject to energy constraints. Fisher information has been widely used as an effective sensor selection criterion. However, existing information-based sensor selection methods are limited to the case of uncorrelated noise or weakly correlated noise due to the use of approximate metrics. By contrast, here we derive the closed form of the Fisher information matrix with respect to sensor selection variables that is valid for any arbitrary noise correlation regime, and develop both a convex relaxation approach and a greedy algorithm to find near-optimal solutions. We further extend our framework of sensor selection to solve the problem of sensor scheduling, where a greedy algorithm is proposed to determine non-myopic (multi-time step ahead) sensor schedules. Lastly, numerical results are provided to illustrate the effectiveness of our approach, and to reveal the effect of noise correlation on estimation performance.

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Sensor selection with correlated measurements for target tracking in wireless sensor networks

Cited by 10 publications

References 22 publications

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Efficient integration of RSSI for tracking using Wireless Camera Networks

Sensor Selection for Estimation with Correlated Measurement Noise

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