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Editorial

Sensors in Collaboration Increase Individual Potentialities

Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Sensors 2012, 12(4), 4892-4896; https://doi.org/10.3390/s120404892
Submission received: 6 April 2012 / Accepted: 12 April 2012 / Published: 13 April 2012
(This article belongs to the Special Issue Collaborative Sensors)

Different applications require different sensor technologies and methods to achieve specific goals. Particular sensor designs are focused on solving problems. It is well-known that individual sensors can be limited when complex problems or applications are involved or the application requires sensing in different locations or even different geographical areas.

We could think of robotic applications where vision, ultrasounds or tactile technologies among others are considered as a whole with the goal of navigation and exploration. Individual sensors are insufficient for achieving the goal, but in collaboration the objective can be achieved and even with high effectiveness. Some sensor devices are arrays of single elements, such as tactile or electronic-noses. In both cases, sensors are related to a specific location. On the contrary, some applications are based on the distribution of sensors at different locations, interconnected under a network for collaboration. At each specific location different sensors can be working in collaboration.

Works on this special issue cover the topic of collaborative sensors under different configurations, always enhancing and improving performances of individual sensors, based on the fusion of the information provided by the different sensors. Different categories and specific applications are considered, where each work is assigned to one or more specific categories and listed in the appropriate references section.

Wireless Sensor Networks (WSN)

(1) coverage precedence routing algorithm, ensuring full functionality, for quality of service in WSN, [1]; (2) Diffusion-based Expectation-Maximization algorithm for energy-efficient solution in WSN [2]; (3) trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm for multiple event source localization using binary information from the sensor nodes in WSN [3]; (4) collaborative localization algorithms for nodes in WSN without GPS [4]; (5) prediction (data not sent to the sink node) accuracy for data reduction in WSN [5]; (6) grid-based distributed event detection scheme for WSN [6]; (7) WSNs for intelligent transportation systems [7]; remote testbed with WSN and mobile robots equipped with a set of low-cost off-the-shelf sensors for cooperative perception [8]; (8) wireless body area networks for monitoring health parameters are useful for transmitting data externally [9]; (9) distributed and formula-based bilateration algorithm used to provide initial set of locations in WSN [10]; (10) Artificial neural network to estimate the location of a mobile station in wireless communication systems [11]; (11) WSN and minimax method in early detection to neutralize intruders in strategic installations [12].

Medicine and Health Services

(1) wireless wearable and ambient sensors that cooperate to monitor person's vital signs such as heart rate and blood pressure during daily activities [13]; (2) body sensor networks with wireless technology can be used for the acquisition of health related information, which is transmitted to an external gateway, such as a PDA [14]

Inertial Measurement Units

(1) fusion algorithms for using multiple Inertial Measurement Unit (IMUs) to enhance performance in the context of pedestrian navigation [14]; (2) a set of distributed accelerometers are arranged and integrated as an IMU [15].

Micro-Electro-Mechanical Systems (MEMS)

(1) based on low-cost sensors along buried pipes in communication with a smart server for decision making [16]; (2) body sensor networks for health purposes [9].

Security in Intelligent Sensors

patterns-based security specifications and new ontological specification [17].

Oceanographic and Meteorological

instruments are installed on a buoy as a multisensory moored platform for continuous and autonomous monitoring of the pelagic system in Western Mediterranean [18].

Robotics

(1) Odometry and laser scanners are integrated for relative localization for navigation of a convoy of robotic units in indoor environments [19]; (2) autonomous robot-arm model for object manipulation in semi-structured environments based on an intelligent multi-sensor system [20]; (3) specific tasks are distributed and allocated to each element in a swarm robotics by applying optimization methods, such as genetic algorithms [21]; (4) social odometry, where robots learn from the others, based on cooperative reputation systems [22]; (5) 3D parallel mechanism robot-arm with three pneumatic actuators combined with a stereo vision system is developed for path tracking control [23]; (6) remote testbed with mobile robots and WSN equipped with a set of low-cost off-the-shelf sensors in cooperative perception, that present high degree of heterogeneity in their technology, sensed magnitudes, features, output bandwidth, interfaces and power consumption [8].

Automatic House

heterogeneous collaborative sensor networks for electrical and energy management on a self-sufficient solar house [24].

Gyroscope

a design of force to rebalance control for a hemispherical resonator gyro (HRG) based on FPGA [25].

3D Structure

(1) fusion of stereovision and range finder sensors applied to autonomous vehicles guidance [26];(2) fusion of Kinect with laser sensors for reducing limitations of the first [27].

Brain-Computer Interface

a hardware and software communication system that permits to control computers and external devices through cerebral activity, specifically appropriate for severely disable people [28].

Surveillance and Tracking

applied to detect ground targets through sensor nodes in a distributed network [29].

Acknowledgments

Thanks are due to all the authors for their valuable collaboration and contributions to this special issue—without them it could not exist. All papers presented to the call passed a rigorous refereeing process as full manuscripts. The accepted papers underwent final revision and approval for publication in a second round of reviewing. Gratitude is owed to the international team of reviewers for their diligence in assessing the papers and their thoughtful and constructive criticism. The papers selected for this special issue represent the quality, breadth and depth of the field of sensor technologies and sensor-based procedures applied for solving different problems involving several sensors in collaboration.

References

  1. Jiang, J.A.; Lin, T.S.; Chuang, C.L.; Chen, C.P.; Sun, C.H.; Juang, J.Y.; Lin, J.C.; Liang, W.W. A QoS-Guaranteed Coverage Precedence Routing Algorithm for Wireless Sensor Networks. Sensors 2011, 11, 3418–3438. [Google Scholar]
  2. Weng, Y.; Xiao, W.; Xie, L. Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks. Sensors 2011, 11, 6297–6316. [Google Scholar]
  3. Xu, X.; Gao, X.; Wan, J.; Xiong, N. Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs. Sensors 2011, 11, 6555–6574. [Google Scholar]
  4. Sahoo, P.K.; Hwang, I.S. Collaborative Localization Algorithms for Wireless Sensor Networks with Reduced Localization Error. Sensors 2011, 11, 9989–10009. [Google Scholar]
  5. Carvalho, C.; Gomes, D.G.; Agoulmine, N.; Souza, J.N. Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation. Sensors 2011, 11, 10010–10037. [Google Scholar]
  6. Ko, J.W.; Choi, Y.H. A Grid-Based Distributed Event Detection Scheme for Wireless Sensor Networks. Sensors 2011, 11, 10048–10062. [Google Scholar]
  7. Losilla, F.; Garcia-Sanchez, A.J.; Garcia-Sanchez, F.; Garcia-Haro, J.; Haas, Z.J. A Comprehensive Approach to WSN-Based ITS Applications: A Survey. Sensors 2011, 11, 10220–10265. [Google Scholar]
  8. Jiménez-González, A.; Martínez-de Dios, J.R.; Ollero, A. An Integrated Testbed for Cooperative Perception with Heterogeneous Mobile and Static Sensors. Sensors 2011, 11, 11516–11543. [Google Scholar]
  9. Rahman, M.O.; Hong, C.S.; Lee, S.; Bang, Y.C. ATLAS: A Traffic Load Aware Sensor MAC Design for Collaborative Body Area Sensor Networks. Sensors 2011, 11, 11560–11580. [Google Scholar]
  10. Cota-Ruiz, J.; Rosiles, J.G.; Sifuentes, E.; Rivas-Perea, P. A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods. Sensors 2012, 12, 839–862. [Google Scholar]
  11. Chen, C.S. Artificial Neural Network for Location Estimation in Wireless Communication Systems. Sensors 2012, 12, 2798–2817. [Google Scholar]
  12. Conesa-Muñoz, J.; Ribeiro, A. Consolidation of a WSN and Minimax Method to Rapidly Neutralise Intruders in Strategic Installations. Sensors 2012, 12, 3281–3301. [Google Scholar]
  13. Nakamura, M.; Nakamura, J.; Lopez, G.; Shuzo, M.; Yamada, I. Collaborative Processing of Wearable and Ambient Sensor System for Blood Pressure Monitoring. Sensors 2011, 11, 6760–6770. [Google Scholar]
  14. Bancroft, J.B.; Lachapelle, G. Data Fusion Algorithms for Multiple Inertial Measurement Units. Sensors 2011, 11, 6771–6798. [Google Scholar]
  15. Park, S.; Hong, S.K. Angular Rate Estimation Using a Distributed Set of Accelerometers. Sensors 2011, 11, 10444–10457. [Google Scholar]
  16. Metje, N.; Chapman, D.N.; Cheneler, D.; Ward, M.; Thomas, A.M. Smart Pipes—Instrumented Water Pipes, Can This Be Made a Reality? Sensors 2011, 11, 7455–7475. [Google Scholar]
  17. Bialas, A. Common Criteria Related Security Design Patterns for Intelligent Sensors—Knowledge Engineering-Based Implementation. Sensors 2011, 11, 8085–8114. [Google Scholar]
  18. Bahamon, N.; Aguzzi, J.; Bernardello, R.; Ahumada-Sempoal, M.A.; Puigdefabregas, J.; Cateura, J.; Muñoz, E.; Velásquez, Z.; Cruzado, A. The New Pelagic Operational Observatory of the Catalan Sea (OOCS) for the Multisensor Coordinated Measurement of Atmospheric and Oceanographic Conditions. Sensors 2011, 11, 11251–11272. [Google Scholar]
  19. Espinosa, F.; Santos, C.; Marrón-Romera, M.; Pizarro, D.; Valdés, F.; Dongil, J. Odometry and Laser Scanner Fusion Based on a Discrete Extended Kalman Filter for Robotic Platooning Guidance. Sensors 2011, 11, 8339–8357. [Google Scholar]
  20. Pomares, J.; Perea, I.; García, G.J.; Jara, C.A.; Corrales, J.A.; Torres, F. A Multi-Sensorial Hybrid Control for Robotic Manipulation in Human-Robot Workspaces. Sensors 2011, 11, 9839–9862. [Google Scholar]
  21. Jevtić, A.; Gutiérrez, A. Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots. Sensors 2011, 11, 10880–10893. [Google Scholar]
  22. Fraga, D.; Gutiérrez, A.; Vallejo, J.C.; Campo, A.; Bankovic, Z. Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes. Sensors 2011, 11, 11372–1138. [Google Scholar]
  23. Chiang, M.H.; Lin, H.T. Development of a 3D Parallel Mechanism Robot Arm with Three Vertical-Axial Pneumatic Actuators Combined with a Stereo Vision System. Sensors 2011, 11, 10880–10893. [Google Scholar]
  24. Castillo-Cagigal, M.; Matallanas, E.; Gutiérrez, A.; Monasterio-Huelin, F.; Caamaño-Martín, E.; Masa-Bote, D.; Jiménez-Leube, J. Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy. Sensors 2011, 11, 11544–11559. [Google Scholar]
  25. Wang, X.; Wu, W.; Luo, B.; Fang, Z.; Li, Y.; Jiang, Q. Force to Rebalance Control of HRG and Suppression of Its Errors on the Basis of FPGA. Sensors 2011, 11, 11761–11773. [Google Scholar]
  26. Hernández-Aceituno, J.; Acosta, L.; Arnay, R. Fusion of a Variable Baseline System and a Range Finder. Sensors 2012, 12, 278–296. [Google Scholar]
  27. Chávez, A.; Karstoft, H. Improvement of KinectTM Sensor Capabilities by Fusion with Laser Sensing Data Using Octree. Sensors 2012, 12, 3868–3878. [Google Scholar]
  28. Nicolas-Alonso, L.F.; Gomez-Gil, J. Brain Computer Interfaces, a Review. Sensors 2012, 12, 1211–1279. [Google Scholar]
  29. Kozma, R.; Wang, L.; Iftekharuddin, K.; McCracken, E.; Khan, M.; Islam, K.; Bhurtel, S.R.; Demirer, R.M. A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking. Sensors 2012, 12, 1336–1351. [Google Scholar]

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MDPI and ACS Style

Pajares, G. Sensors in Collaboration Increase Individual Potentialities. Sensors 2012, 12, 4892-4896. https://doi.org/10.3390/s120404892

AMA Style

Pajares G. Sensors in Collaboration Increase Individual Potentialities. Sensors. 2012; 12(4):4892-4896. https://doi.org/10.3390/s120404892

Chicago/Turabian Style

Pajares, Gonzalo. 2012. "Sensors in Collaboration Increase Individual Potentialities" Sensors 12, no. 4: 4892-4896. https://doi.org/10.3390/s120404892

APA Style

Pajares, G. (2012). Sensors in Collaboration Increase Individual Potentialities. Sensors, 12(4), 4892-4896. https://doi.org/10.3390/s120404892

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