Distributed Intelligent Systems

Calhoun, GA, United States

Distributed Intelligent Systems

Calhoun, GA, United States
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Schreiber A.,Distributed Intelligent Systems | Struminski R.,Dusseldorf University of Applied Sciences
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Personal health data is acquired, processed, stored, and accessed using a variety of different devices, apps, and services. These are often complex and highly connected. Therefore, privacy violations and other use or misuse of the data are hard to detect for many people, because they are not able to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comics strips. Each strip of the comic represents a certain activity, such as entering data using an app, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to realize crucial points regarding their data. © Springer International Publishing AG 2017.


Sliwko L.,Distributed Intelligent Systems | Getov V.,Distributed Intelligent Systems
Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016 | Year: 2016

This paper presents the Accurate Google Cloud Simulator (AGOCS) - a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise, detailed parameters of the executed jobs, tasks, nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution, an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS, discusses our main design decisions, whilst also suggesting alternative, possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository. © 2016 IEEE.


Hirose N.,Distributed Intelligent Systems | Tajima R.,Distributed Intelligent Systems
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2017

The modeling and identification of a mechanical system is the most important issue for many control systems in order to realize the desired control specifications. In particular, the friction characteristics often deteriorate the control performance, such as in the fast and precise positioning performance in industrial robots, the force estimation accuracy based on a disturbance observer, and the posture control performance of an inverted pendulum robot. Rolling friction tends to cause overshoot, undershoot, or limit cycles of the target value in positioning systems. In previous research, some model structures for rolling friction have been proposed to express the hysteresis characteristics in order to overcome these control issues. However, it is difficult to identify the correct parameters for precise modeling. In this paper, the modeling of rolling friction based on a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) is proposed to precisely express the rolling friction characteristics. The initial value design of the RNN during supervised learning is also presented to achieve a better model. The effectiveness of the proposed approach is verified by comparison with conventional friction models using an actual experimental setup. © 2017 IEEE.


Zelenkauskaite A.,Indiana University Bloomington | Bessis N.,Distributed Intelligent Systems | Bessis N.,University of Bedfordshire | Sotiriadis S.,Distributed Intelligent Systems | Asimakopoulou E.,Distributed Intelligent Systems
Proceedings of the 2012 4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012 | Year: 2012

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objects - and thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain. © 2012 IEEE.


Marjovi A.,Distributed Intelligent Systems | Marques L.,University of Coimbra
IEEE Transactions on Cybernetics | Year: 2014

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots. © 2014 IEEE.


Smith A.,Distributed Intelligent Systems | Hill R.,Distributed Intelligent Systems
Proceedings - 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2011 | Year: 2011

In wireless ad-hoc networks where there is no continuous end-to-end path we move into the area of opportunistic networks. Forwarding messages via any encountered nodes, such as the mobile devices that many users already carry. Normally we are looking for the most efficient method of passing these messages across the network, but how do we evaluate the different methods. We propose to develop a framework that will allow us to evaluate how efficiently provisioning has been performed. This has been explored with the use of a case study and two benchmark protocols, Epidemic and PRoPHET. We present the results of this analysis and describe an approach to the validation of this through simulation. © 2011 IEEE.


Mahmood Z.,Distributed Intelligent Systems
Proceedings - 2011 International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2011 | Year: 2011

Cloud Computing is a generic term for delivering hosted services over the Internet. It follows a pay-as-you-go approach. Cloud Computing offers numerous benefits for the enterprises, however, there are also many issues, as with any new paradigm or technology. One of the main issues relate to the security and confidentiality of customer data in terms of its location, relocation, availability and security. This paper outlines the Cloud benefits, briefly explains the delivery and deployment models and discusses in detail the issues relating to data in the Cloud. The aim is to provide some useful background information for organizations preparing to migrate to the Cloud to take advantage of this latest computing paradigm. © 2011 IEEE.


Zhao Y.,Distributed Intelligent Systems | Patwari N.,University of Utah
IEEE Transactions on Mobile Computing | Year: 2015

Device-free localization systems, such as variance-based radio tomographic imaging (VRTI), use received signal strength (RSS) variations caused by human motion in a static wireless network to locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind or rotating or vibrating machinery, also causes RSS variations which degrade the performance of a localization system. In this paper, we propose a new estimator, least squares variance-based radio tomography (LSVRT), which reduces the impact of the variations caused by intrinsic motion. We compare the novel method to subspace variance-based radio tomography (SubVRT) and VRTI. SubVRT also reduces intrinsic noise compared to VRTI, but LSVRT achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI. We also propose and test an online calibration method so that LSVRT and SubVRT do not require 'empty-area' calibration and thus can be used in emergency situations. Experimental results from five data sets collected during three experimental deployments show that both estimators, using online calibration, can reduce localization root mean squared error by more than 40 percent compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97th percentile error of 1.3 m, a 60 percent reduction compared to VRTI. © 2002-2012 IEEE.


Zhao Y.,Distributed Intelligent Systems
Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM | Year: 2016

This work proposes a novel respiration monitoring system using Doppler signal from a low-cost motion sensor and received signal strength (RSS) measurements from a wireless network. We present the ambiguity problem of the Doppler monitoring system. We find that RSS from a wireless network is complimentary to Doppler signal, and we propose to integrate Doppler with RSS to make respiration monitoring more robust. Our experimental results show that the respiration rate estimation is more accurate by sensor fusion of these two radio frequency (RF) sensing modalities. © 2016 Copyright held by the owner/author(s).


Hill R.,Distributed Intelligent Systems
Proceedings - 2nd International Conference on Intelligent Networking and Collaborative Systems, INCOS 2010 | Year: 2010

This article re-visits the foundation concepts of Computational Intelligence in relation to the established field of Artificial Intelligence and describes an emerging data technology, Formal Concept Analysis, that can contribute to the topic. Technologies from Artificial Intelligence are briefly considered in relation to the need for improved knowledge representation and accessibility rather than more refined reasoning algorithms that operate in restricted domains. © 2010 IEEE.

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