Distributed Navigation Strategy of Mobile Sensor Networks With Probabilistic Wireless Communication Links

Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems ◽

10.1115/dscc2015-9964 ◽

2015 ◽

Author(s):

Aqeel Madhag ◽

Jongeun Choi

Keyword(s):

Sensor Networks ◽

Wireless Communication ◽

Prediction Error ◽

Mobile Sensor Networks ◽

Gaussian Random Fields ◽

Communication Model ◽

Mobile Sensor ◽

Communication Links ◽

Spatio Temporal ◽

Physical Phenomena

Mobile sensor networks have been widely used to predict spatio-temporal physical phenomena for various scientific and engineering applications. To accommodate the realistic models of mobile sensor networks, we incorporated probabilistic wireless communication links based on packet reception ratio (PRR) with distributed navigation. We then derived models of mobile sensor networks that predict Gaussian random fields from noise-corrupted observations under probabilistic wireless communication links. For the given model with probabilistic wireless communication links, we derived the prediction error variances for further sampling locations. Moreover, we designed a distributed navigation that minimizes the network cost function formulated in terms of the derived prediction error variances. Further, we have shown that the solution of distributed navigation with the probabilistic wireless communication links for mobile sensor networks are uniformly ultimately bounded with respect to that of the distributed one with the R-disk communication model. According to Monte Carlo simulation results, agent trajectories under distributed navigation with the probabilistic wireless communication links are similar to those with the R-disk communication model, which confirming the theoretical analysis.

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A Distributed Navigation Strategy for Mobile Sensor Networks With the Probabilistic Wireless Links

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4035008 ◽

2017 ◽

Vol 139 (3) ◽

Cited By ~ 1

Author(s):

Aqeel Madhag ◽

Jongeun Choi

Keyword(s):

Sensor Networks ◽

Wireless Communication ◽

Prediction Error ◽

Mobile Sensor Networks ◽

Gaussian Random Fields ◽

Communication Model ◽

Mobile Sensor ◽

Communication Links ◽

Spatio Temporal ◽

Physical Phenomena

Mobile sensor networks have been widely used to predict the spatio-temporal physical phenomena for various scientific and engineering applications. To accommodate the realistic models of mobile sensor networks, we incorporated probabilistic wireless communication links based on packet reception ratio (PRR) with distributed navigation. We then derived models of mobile sensor networks that predict Gaussian random fields from noise-corrupted observations under probabilistic wireless communication links. For the given model with probabilistic wireless communication links, we derived the prediction error variances for further sampling locations. Moreover, we designed a distributed navigation that minimizes the network cost function formulated in terms of the derived prediction error variances. Further, we have shown that the solution of distributed navigation with the probabilistic wireless communication links for mobile sensor networks are uniformly ultimately bounded with respect to that of the distributed one with the R-disk communication model. According to Monte Carlo simulation results, agent trajectories under distributed navigation with the probabilistic wireless communication links are similar to those with the R-disk communication model, which confirming the theoretical analysis.

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Optimal Coordination of Mobile Sensor Networks Using Gaussian Processes

ASME 2009 Dynamic Systems and Control Conference, Volume 2 ◽

10.1115/dscc2009-2677 ◽

2009 ◽

Author(s):

Yunfei Xu ◽

Jongeun Choi

Keyword(s):

Sensor Networks ◽

Gaussian Processes ◽

Mobile Sensor Networks ◽

Mobile Sensor ◽

Covariance Functions ◽

Limited Memory ◽

Optimal Coordination ◽

Spatio Temporal ◽

Nonparametric Prediction ◽

Navigation Strategies

In this paper, we introduce a family of spatio-temporal Gaussian processes specified by a class of covariance functions. Nonparametric prediction based on truncated observations is proposed for mobile sensor networks with limited memory and computational power. We show that there is a trade-off between precision and efficiency when prediction based on truncated observations is used. Next, we propose both centralized and distributed navigation strategies for mobile sensor networks to move in order to reduce prediction error variances at positions of interest. Simulation results demonstrate the effectiveness of the proposed schemes.

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Event-Based Communication and Finite-Time Consensus Control of Mobile Sensor Networks for Environmental Monitoring

Sensors ◽

10.3390/s18082547 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2547 ◽

Cited By ~ 7

Author(s):

Yu Hu ◽

Qiang Lu ◽

Yanzhu Hu

Keyword(s):

Sensor Networks ◽

Wireless Communication ◽

Environmental Monitoring ◽

Cooperative Control ◽

Mobile Sensor Networks ◽

Sensor Nodes ◽

Mobile Sensor ◽

Control Scheme ◽

Communication Module ◽

Event Based

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring.

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