scholarly journals Survey of Context Information Fusion for Sensor Networks Based Ubiquitous Systems

2014 ◽  
Vol 2 (3) ◽  
pp. 165-178
Author(s):  
Vijay Borges ◽  
Wilson Jeberson
Keyword(s):  
Sensor Networks ◽  
Information Fusion ◽  
Context Information ◽  
Ubiquitous Systems

scholarly journals Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

2016 ◽  
Vol 6 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Vijay Borges
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Information Fusion ◽  
User Preferences ◽  
Context Information ◽  
Ubiquitous Systems ◽  
Core Function ◽  
Contextual Computing ◽  
Ubiquitous Sensor Networks ◽  
Noisy Measurements

AbstractInternet-of-Things (IoT) is the latest buzzword, havings its origins in the erstwhile Sensor Networks. Sensor Networks produce a large amount of data. According to the needs this data requires to be processed, delivered and accessed. This processed data when made available with the physical device location, user preferences, time constraints; generically called as context-awareness; is widely referred to as the core function for ubiquitous systems. To our best knowledge there is lack of analysis of context information fusion for ubiquitous sensor networks. Adopting appropriate information fusion techniques can help in screening noisy measurements, control data in the network and take necessary inferences that can help in contextual computing. In this paper we try and explore different context information fusion techniques by comparing a large number of solutions, their methods, architectures and models. All the surveyed techniques can be adapted to the IoT framework.

scholarly journals Person Re-Identification Based on Attention Mechanism and Context Information Fusion

2021 ◽  
Vol 13 (3) ◽  
pp. 72
Author(s):  
Shengbo Chen ◽  
Hongchang Zhang ◽  
Zhou Lei
Keyword(s):  
Information Fusion ◽  
Irrelevant Information ◽  
Features Extraction ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Context Information ◽  
Feature Map ◽  
High Level ◽  
Surveillance Analysis ◽  
High Level Feature

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

Real-time pixel-wise grasp affordance prediction based on multi-scale context information fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yongxiang Wu ◽  
Yili Fu ◽  
Shuguo Wang
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Feature Fusion ◽  
Detection Performance ◽  
Context Information ◽  
Data Sets ◽  
Robotic Grasping ◽  
Content Type ◽  
Scale Feature ◽  
Multi Scale

Purpose This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion. Design/methodology/approach A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different grasp rotation prediction strategies (regression and classification), and their performances are evaluated and compared with a defined point metric. The regression network is further extended to predict the grasp rectangles for comparisons with previous methods and real-world robotic grasping of unknown objects. Findings The ablation study of the pyramid pooling module shows that the multi-scale information fusion significantly improves the model performance. The regression approach outperforms the classification approach based on same feature embedding framework on two data sets. The regression network achieves a state-of-the-art accuracy (up to 98.9%) and speed (4 ms per image) and high success rate (97% for household objects, 94.4% for adversarial objects and 95.3% for objects in clutter) in the unknown object grasping experiment. Originality/value A novel pixel-wise grasp affordance prediction network based on multi-scale feature fusion is proposed to improve the grasp detection performance. Two prediction approaches are formulated and compared based on the proposed framework. The proposed method achieves excellent performances on three benchmark data sets and real-world robotic grasping experiment.

SYLPH

2011 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Ricardo S. Alonso ◽  
Dante I. Tapia ◽  
Juan M. Corchado
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ambient Intelligence ◽  
Wireless Sensor ◽  
Context Aware ◽  
Preliminary Results ◽  
Ubiquitous Systems ◽  
Heterogeneous Wireless Sensor Networks ◽  
Innovative Solutions ◽  
Flexible Applications

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. The use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react upon it autonomously. This paper presents SYLPH, a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSN). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. A WSN infrastructure has been deployed for testing and evaluating this platform. Preliminary results and conclusions are presented in this paper.

Towards Adaptive Security for Ubiquitous Computing Systems

2011 ◽  
pp. 574-584
Author(s):  
Volkmar Lotz ◽  
Luca Compagna ◽  
Konrad Wrona
Keyword(s):  
Sensor Networks ◽  
Ubiquitous Computing ◽  
Context Information ◽  
Strong Impact ◽  
Adaptive Security ◽  
Mobile Environments ◽  
Computing Systems ◽  
Security Services ◽  
On Demand ◽  
Constant Change

The flexibility and dynamism of ubiquitous computing systems have a strong impact on the way their security can be achieved, reaching beyond traditional security paradigms like perimeter security and communication channel protection. Constant change of both the system and its environment demand adaptive security architectures, capable of reacting to events, evaluating threat exposure, and taking evolving protection needs into account. We introduce two examples of projects that contribute to meeting the challenges on adaptive security. The first focuses on an architecture that allows for adaptive security in mobile environments based on security services whose adaptation is guided by context information derived from sensor networks. The second addresses engineering aspects of secure ubiquitous computing systems through making security solutions accessible and deployable on demand and following emerging application-level requirements.

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