Localization Control to Locate Mobile Sensors

2006 ◽  
pp. 81-88 ◽  
Author(s):  
Buddhadeb Sau ◽  
Srabani Mukhopadhyaya ◽  
Krishnendu Mukhopadhyaya
Keyword(s):  
Mobile Sensors

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

2019 ◽  
Vol 33 (11) ◽  
pp. 2218-2240 ◽  
Author(s):  
Yee Leung ◽  
Yu Zhou ◽  
Ka-Yu Lam ◽  
Tung Fung ◽  
Kwan-Yau Cheung ◽  
...  
Keyword(s):  
Air Pollution ◽  
Mobile Sensors ◽  
Pollution Profile

Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze-Sensing Using Mobile Sensors

2020 ◽  
Author(s):  
Soujanya Chatterjee ◽  
Md Mahbubur Rahman ◽  
Tousif Ahmed ◽  
Nazir Saleheen ◽  
Ebrahim Nemati ◽  
...  
Keyword(s):  
Mobile Sensors

scholarly journals Continuous trajectory planning of mobile sensors for informative forecasting

Automatica ◽  
2010 ◽  
Vol 46 (8) ◽  
pp. 1266-1275 ◽  
Author(s):  
Han-Lim Choi ◽  
Jonathan P. How
Keyword(s):  
Trajectory Planning ◽  
Mobile Sensors

Handling Failures of Static Sensor Nodes in Wireless Sensor Network by Use of Mobile Sensors

2011 ◽  
Author(s):  
Edison Pignaton de Freitas ◽  
Tales Heimfarth ◽  
Ivayr Farah Netto ◽  
Carlos Eduardo Pereira ◽  
Armando Morado Ferreira ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Mobile Sensors ◽  
Wireless Sensor

Coverage Control for Mobile Sensor Networks on a Circle

2014 ◽  
Vol 02 (03) ◽  
pp. 243-248 ◽  
Author(s):  
Cheng Song ◽  
Gang Feng
Keyword(s):  
Sensor Networks ◽  
Mobile Agents ◽  
Mobile Sensor Networks ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Control Laws ◽  
Coverage Control ◽  
Order Preservation ◽  
Multi Agent

This paper investigates the coverage problem for mobile sensor networks on a circle. The goal is to minimize the largest distance from any point on the circle to its nearest sensor while preserving the mobile sensors' order. The coverage problem is translated into a multi-agent consensus problem by showing that the largest distance from any point to its nearest sensor is minimized if the counterclockwise distance between each sensor and its right neighbor reaches a consensus. Distributed control laws are also developed to drive the mobile agents to the optimal configuration with order preservation. Simulation results illustrate the effectiveness of the proposed control laws.

Voronoi-based deployment of mobile sensors in the face of adversaries

2014 ◽  
Author(s):  
N. Bartolini ◽  
G. Bongiovanni ◽  
T. La Porta ◽  
S. Silvestri ◽  
F. Vincenti
Keyword(s):  
Mobile Sensors ◽  
The Face

scholarly journals Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1457
Author(s):  
Dieyan Liang ◽  
Hong Shen
Keyword(s):  
Optimal Algorithm ◽  
Approximate Solutions ◽  
Approximation Ratio ◽  
Mobile Sensors ◽  
Np Hard ◽  
Coverage Problem ◽  
Polynomial Time Approximation Algorithm ◽  
Weighted Point ◽  
Eulerian Graph ◽  
Set Of Points

As an important application of wireless sensor networks (WSNs), deployment of mobile sensors to periodically monitor (sweep cover) a set of points of interest (PoIs) arises in various applications, such as environmental monitoring and data collection. For a set of PoIs in an Eulerian graph, the point sweep coverage problem of deploying the fewest sensors to periodically cover a set of PoIs is known to be Non-deterministic Polynomial Hard (NP-hard), even if all sensors have the same velocity. In this paper, we consider the problem of finding the set of PoIs on a line periodically covered by a given set of mobile sensors that has the maximum sum of weight. The problem is first proven NP-hard when sensors are with different velocities in this paper. Optimal and approximate solutions are also presented for sensors with the same and different velocities, respectively. For M sensors and N PoIs, the optimal algorithm for the case when sensors are with the same velocity runs in O(MN) time; our polynomial-time approximation algorithm for the case when sensors have a constant number of velocities achieves approximation ratio 12; for the general case of arbitrary velocities, 12α and 12(1−1/e) approximation algorithms are presented, respectively, where integer α≥2 is the tradeoff factor between time complexity and approximation ratio.

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