Distributed Intelligent Systems

Calhoun, GA, United States

Distributed Intelligent Systems

Calhoun, GA, United States

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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.

Mauledoux M.,Distributed Intelligent Systems | Shkodyrev V.,Distributed Intelligent Systems
2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 | Year: 2010

The work is devoted to solve allocation task problem in the distributed way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for save time in the search a expand the solution of the optimal space. ©2010 IEEE.

Alalawi K.,Distributed Intelligent Systems | Al-Aqrabi H.,Distributed Intelligent Systems
2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015 | Year: 2015

The Voice over IP (VoIP) service demands high priority over other services and applications. Some constrains are associated with this real-time service, such as delay and throughput which need to be addressed before delivering to the customer. The mobility in IP networks is a demand that facilitates IP applications and services, especially in wireless networks. This paper demonstrates the performance of Voice over IP (VoIP) in 802.11 wireless networks and elaborates on the evaluation of voice packet end-to-end delay and throughput. Employing literature reviews and an experimental model created on OPNET that is simulated to assess the quality of service (QoS) of VoIP in 802.11g legacy and 802.11e wireless network; shows the enhancement of 802.11 reflects as enhancement in the quality of the VoIP service. The simulation results have indicated that the quality of VoIP service is influenced by the quality of the carrier which is IEEE 802.11 network. Therefore, the voice service over wireless network can be improved significantly by developing a quality of service policy that prioritizes the packet transmission based on the controlled access mechanisms. Eventually, the number of VoIP calls could be increased using the enhanced 802.11e standard rather than 802.11 standard. © 2015 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.

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

The capture and analysis of requirements for virtual campus systems that employ emerging technologies is complex. Whilst the agent paradigm can assist the modelling of such systems, the variety, scope and volume of network interactions creates a scenario that is difficult to comprehend and analyse. Transaction Agent Modelling (TrAM) is an approach to early requirements capture that is particularly suited for convoluted, complex domains. It incorporates the use of Conceptual Graphs and an Economic Accouting model to capture and represent the qualitative dimensions inherent in real-world situations, and also the use of Peirce Logic to test the models derived. Through an exemplar in the mobile learning (m-learning) domain, TrAM is demonstrated and explicated to illustrate its use. © 2010 IEEE.

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