scholarly journals Real-World Deployments of Participatory Sensing Applications: Current Trends and Future Directions

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Sameer Tilak
Keyword(s):  
Sensor Networks ◽  
Real World ◽  
Cell Phones ◽  
Research Community ◽  
Consumer Electronics ◽  
Mobile Sensors ◽  
Participatory Sensing ◽  
Future Directions ◽  
Sensing Applications ◽  
Current Trends

With the advent of participatory sensing (sensors integrated with consumer electronics such as cell phones and carried by people), exciting new opportunities arise. Mobile sensors (e.g., those mounted on cars or carried by people) can provide spatial sampling diversity not possible with traditional static sensor networks. Recently, participatory sensing has attracted considerable attention of research community. In this paper, we survey existing participatory sensing deployments and discuss current trends and few possible future directions.

scholarly journals A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

2017 ◽  
Author(s):  
Qikun Xiang ◽  
Jie Zhang ◽  
Ido Nevat ◽  
Pengfei Zhang
Keyword(s):  
Real World ◽  
Gaussian Processes ◽  
Spatial Regression ◽  
Participatory Sensing ◽  
Sensing Applications ◽  
Different Types ◽  
Inaccurate Estimation ◽  
Real World Datasets ◽  
Gp Model ◽  
Mixture Of Gaussian

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

scholarly journals Current Trends on Green Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4281
Author(s):  
J. Carlos López-Ardao ◽  
Raúl F. Rodríguez-Rubio ◽  
Andrés Suárez-González ◽  
Miguel Rodríguez-Pérez ◽  
M. Estrella Sousa-Vieira
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Full Time ◽  
Energy Balancing ◽  
Future Directions ◽  
Available Energy ◽  
Current Trends ◽  
Harvesting Techniques ◽  
The Cost

The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

scholarly journals Wireless Multimedia Sensor Networks: Current Trends and Future Directions

Sensors ◽  
2010 ◽  
Vol 10 (7) ◽  
pp. 6662-6717 ◽  
Author(s):  
Islam T. Almalkawi ◽  
Manel Guerrero Zapata ◽  
Jamal N. Al-Karaki ◽  
Julian Morillo-Pozo
Keyword(s):  
Sensor Networks ◽  
Wireless Multimedia Sensor Networks ◽  
Wireless Multimedia ◽  
Future Directions ◽  
Multimedia Sensor Networks ◽  
Current Trends

scholarly journals Correction to: Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

2019 ◽  
Vol 8 (3) ◽  
pp. 202-202
Author(s):  
Emma E. McGee ◽  
Rama Kiblawi ◽  
Mary C. Playdon ◽  
A. Heather Eliassen
Keyword(s):  
Cancer Epidemiology ◽  
Future Directions ◽  
Current Trends ◽  
Nutritional Metabolomics

scholarly journals Current Trends in Polymer Based Sensors

Chemosensors ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 108
Author(s):  
Giancarla Alberti ◽  
Camilla Zanoni ◽  
Vittorio Losi ◽  
Lisa Rita Magnaghi ◽  
Raffaela Biesuz
Keyword(s):  
Chemical Structure ◽  
Low Cost ◽  
Sensing Applications ◽  
Current Trends ◽  
Manufacturing Methods ◽  
New Devices

This review illustrates various types of polymer and nanocomposite polymeric based sensors used in a wide variety of devices. Moreover, it provides an overview of the trends and challenges in sensor research. As fundamental components of new devices, polymers play an important role in sensing applications. Indeed, polymers offer many advantages for sensor technologies: their manufacturing methods are pretty simple, they are relatively low-cost materials, and they can be functionalized and placed on different substrates. Polymers can participate in sensing mechanisms or act as supports for the sensing units. Another good quality of polymer-based materials is that their chemical structure can be modified to enhance their reactivity, biocompatibility, resistance to degradation, and flexibility.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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