Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Download Full-text

RNN-based multispectral satellite image processing for remote sensing applications

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-07-2021-0153 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Venkata Dasu Marri ◽

Veera Narayana Reddy P. ◽

Chandra Mohan Reddy S.

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Deep Learning ◽

Image Classification ◽

Satellite Image ◽

Multispectral Image ◽

Content Type ◽

Colliding Bodies Optimization ◽

Sensing Applications ◽

Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

Download Full-text

Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

Revista de Teledetección ◽

10.4995/raet.2020.13337 ◽

2020 ◽

pp. 35

Author(s):

M. Campos-Taberner ◽

F.J. García-Haro ◽

B. Martínez ◽

M.A. Gilabert

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Near Infrared ◽

Spatial Information ◽

Learning Algorithm ◽

Support Vector ◽

Sensing Applications ◽

Learning Techniques ◽

Remote Sensing Applications ◽

Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

Download Full-text

A Review on Deep Image Contrast Enhancement

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v6i1.258 ◽

2020 ◽

Vol 6 (1) ◽

pp. 4

Author(s):

Puspad Kumar Sharma ◽

Nitesh Gupta ◽

Anurag Shrivastava

Keyword(s):

Image Processing ◽

Deep Learning ◽

Image Enhancement ◽

Research Work ◽

Atmospheric Condition ◽

Input Image ◽

Learning Approaches ◽

Sensing Applications ◽

Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

Download Full-text

Deep learning in remote sensing applications: A meta-analysis and review

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2019.04.015 ◽

2019 ◽

Vol 152 ◽

pp. 166-177 ◽

Cited By ~ 188

Author(s):

Lei Ma ◽

Yu Liu ◽

Xueliang Zhang ◽

Yuanxin Ye ◽

Gaofei Yin ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Meta Analysis ◽

Sensing Applications ◽

Remote Sensing Applications

Download Full-text

Self-organising management of user-generated data and knowledge

The Knowledge Engineering Review ◽

10.1017/s026988891400023x ◽

2014 ◽

Vol 30 (3) ◽

pp. 237-264 ◽

Cited By ~ 11

Author(s):

Sam Macbeth ◽

Jeremy V. Pitt

Keyword(s):

Pervasive Computing ◽

User Generated Content ◽

Participatory Sensing ◽

Open Approach ◽

Computational Logic ◽

New Class ◽

Sensing Applications ◽

Development Framework ◽

Governance Model ◽

Institutional Analysis And Development

AbstractThe proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.

Download Full-text