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Katia Obraczka - One of the best experts on this subject based on the ideXlab platform.

  • SECON - On the Heavy Tail Properties of Spatial Node Density for Realistic Mobility Modeling
    2014 Eleventh Annual IEEE International Conference on Sensing Communication and Networking (SECON), 2014
    Co-Authors: Danielle Lopes Ferreira, Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    In this paper, we show empirically that the spatial Node Density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behav- ior. We develop a stochastic model that confirms our empirical observations by showing that Node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observa- tions, we showcase another application of our model by using it to generate mobility regimes whose spatial Node Density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.

  • On the heavy tail properties of spatial Node Density for realistic mobility modeling
    2014 Eleventh Annual IEEE International Conference on Sensing Communication and Networking (SECON), 2014
    Co-Authors: Danielle Lopes Ferreira, Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    In this paper, we show empirically that the spatial Node Density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behavior. We develop a stochastic model that confirms our empirical observations by showing that Node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observations, we showcase another application of our model by using it to generate mobility regimes whose spatial Node Density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.

  • A framework for modeling spatial Node Density in waypoint-based mobility
    Wireless Networks, 2013
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    User mobility is of critical importance when designing mobile networks. In particular, "waypoint" mobility has been widely used as a simple way to describe how humans move. This paper introduces the first modeling framework to model waypoint-based mobility. The proposed framework is simple, yet general enough to model any waypoint-based mobility regimes. It employs first order ordinary differential equations to model the spatial Density of participating Nodes as a function of (1) the probability of moving between two locations within the geographic region under consideration, and (2) the rate at which Nodes leave their current location. We validate our model against real user mobility recorded in GPS traces collected in three different scenarios. Moreover, we show that our modeling framework can be used to analyze the steady-state behavior of spatial Node Density resulting from a number of synthetic waypoint-based mobility regimes, including the widely used Random Waypoint model. Another contribution of the proposed framework is to show that using the well-known preferential attachment principle to model human mobility exhibits behavior similar to random mobility, where the original spatial Node Density distribution is not preserved. Finally, as an example application of our framework, we discuss using it to generate steady-state Node Density distributions to prime mobile network simulations.

  • MASS - Modeling spatial Node Density in waypoint mobility
    2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), 2012
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    This paper introduces a modeling framework to analyze spatial Node Density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a Node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first Node Density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

  • Modeling spatial Node Density in waypoint mobility
    2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), 2012
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    This paper introduces a modeling framework to analyze spatial Node Density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a Node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first Node Density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

Seungho Choi - One of the best experts on this subject based on the ideXlab platform.

  • lymph Node Density as an independent predictor of cancer specific mortality in patients with lymph Node positive laryngeal squamous cell carcinoma after laryngectomy
    Head and Neck-journal for The Sciences and Specialties of The Head and Neck, 2015
    Co-Authors: Seungho Choi
    Abstract:

    BACKGROUND: We examined the prognostic value of lymph Node Density in predicting cancer-specific mortality (CSM) for patients with lymph Nodes positive (pN+) laryngeal squamous cell carcinoma (SCC) after laryngectomy. METHODS: The records of 156 patients with laryngeal SCC who initially underwent curative resection of the primary tumor combined with neck dissection were reviewed. RESULTS: The 5-year cumulative incidence of CSM was 20.4%. N classification and extralaryngeal spread (ELS) were independent variables for CSM in all patients. Univariate analyses in 71 pN+ patients showed that ELS, number of positive lymph Nodes >4, and lymph Node Density >0.044 were significantly associated with increased CSM, whereas pN classification was not (p = .218). On multivariate analysis, lymph Node Density ≥0.044 remained an independent predictor of CSM (p = .001). CONCLUSION: Among the pN+ patients with laryngeal SCC, no pN classification but lymph Node Density was noted to have an independent impact on CSM.

  • prognostic value of lymph Node Density in Node positive patients with oral squamous cell carcinoma
    Annals of Surgical Oncology, 2011
    Co-Authors: Seungho Choi
    Abstract:

    Background Lymph Node Density (LND) is superior to TNM nodal status in predicting survival after surgery for bladder and other cancers. Little is known, however, about whether LND can predict survival in patients with oral squamous cell carcinoma (OSCC). We therefore evaluated the utility of LND for predicting survival for patients with OSCC and positive Nodes (pN+).

Wassim Kassouf - One of the best experts on this subject based on the ideXlab platform.

  • critical analysis and validation of lymph Node Density as prognostic variable in urothelial carcinoma of bladder
    Urologic Oncology-seminars and Original Investigations, 2013
    Co-Authors: Wassim Kassouf, Robert S Svatek, Shahrokh F Shariat, Giacomo Novara, Seth P Lerner, Yves Fradet, Patrick J Bastian, Armen Aprikian
    Abstract:

    Abstract Objective To validate the prognostic relevance of lymph Node Density (LND) and identify its optimal cut-points in a large international multicenter series of patients treated with radical cystectomy (RC) for invasive bladder cancer. Methods From 1993 to 2005, 4,430 bladder cancer patients who underwent RC without neoadjuvant chemotherapy were reviewed; of these, 1,038 were pN+M0 disease and form the basis of this report. Results Median age of patients was 67 years with median follow-up in survivors of 33 months. Overall, 5-year DSS estimate was 36%. Median number of lymph Nodes removed was 18 (IQR, 11–32), median number of positive lymph Nodes was 2 (IQR, 1–5), and median LND was 14.3% (IQR, 6.67–33.3%). LND as continuous variable was a stronger prognostic factor for DSS in patients that underwent a more extensive PLND (P 41% with cumulative 5-year DSS of 47%, 36%, and 21%, respectively (P Conclusion Lymph Node Density is prognostic in bladder cancer patients who undergo a more extensive PLND and remains prognostic even when adjuvant chemotherapy is used. Prognostic value of LND is best represented as a continuum of risk and LND

  • evaluation of the relevance of lymph Node Density in a contemporary series of patients undergoing radical cystectomy
    The Journal of Urology, 2006
    Co-Authors: Wassim Kassouf, Mark F Munsell, Dan Leibovici, Colin P Dinney, Barton H Grossman, Ashish M Kamat
    Abstract:

    Purpose: Lymph Node Density, that is the ratio of positive Nodes to the total number of Nodes excised, has been suggested to better stratify patients with bladder cancer who have nodal metastasis. We evaluated its relevance in a contemporary series of patients treated with radical cystectomy and in the context of adjuvant chemotherapy.Materials and Methods: From 1993 to 2003, 150 patients had pN+M0 disease at cystectomy, of whom 108 who did not receive neoadjuvant chemotherapy form the basis of this report. Statistical analyses were performed using standard methodology.Results: Five-year overall, disease specific and recurrence-free survival rates were 30.9%, 45.5% and 29.7%, respectively. The median number of lymph Nodes removed was 12 and the median number of positive Nodes was 2. Of the patients 70% received adjuvant chemotherapy. Patients with a lymph Node Density of 25% or less had 5-year overall and recurrence-free survival rates of 37.3% and 38.1% compared with 18.7% and 10.6%, respectively in thos...

Bruno A. A. Nunes - One of the best experts on this subject based on the ideXlab platform.

  • SECON - On the Heavy Tail Properties of Spatial Node Density for Realistic Mobility Modeling
    2014 Eleventh Annual IEEE International Conference on Sensing Communication and Networking (SECON), 2014
    Co-Authors: Danielle Lopes Ferreira, Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    In this paper, we show empirically that the spatial Node Density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behav- ior. We develop a stochastic model that confirms our empirical observations by showing that Node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observa- tions, we showcase another application of our model by using it to generate mobility regimes whose spatial Node Density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.

  • On the heavy tail properties of spatial Node Density for realistic mobility modeling
    2014 Eleventh Annual IEEE International Conference on Sensing Communication and Networking (SECON), 2014
    Co-Authors: Danielle Lopes Ferreira, Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    In this paper, we show empirically that the spatial Node Density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behavior. We develop a stochastic model that confirms our empirical observations by showing that Node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observations, we showcase another application of our model by using it to generate mobility regimes whose spatial Node Density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.

  • A framework for modeling spatial Node Density in waypoint-based mobility
    Wireless Networks, 2013
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    User mobility is of critical importance when designing mobile networks. In particular, "waypoint" mobility has been widely used as a simple way to describe how humans move. This paper introduces the first modeling framework to model waypoint-based mobility. The proposed framework is simple, yet general enough to model any waypoint-based mobility regimes. It employs first order ordinary differential equations to model the spatial Density of participating Nodes as a function of (1) the probability of moving between two locations within the geographic region under consideration, and (2) the rate at which Nodes leave their current location. We validate our model against real user mobility recorded in GPS traces collected in three different scenarios. Moreover, we show that our modeling framework can be used to analyze the steady-state behavior of spatial Node Density resulting from a number of synthetic waypoint-based mobility regimes, including the widely used Random Waypoint model. Another contribution of the proposed framework is to show that using the well-known preferential attachment principle to model human mobility exhibits behavior similar to random mobility, where the original spatial Node Density distribution is not preserved. Finally, as an example application of our framework, we discuss using it to generate steady-state Node Density distributions to prime mobile network simulations.

  • MASS - Modeling spatial Node Density in waypoint mobility
    2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), 2012
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    This paper introduces a modeling framework to analyze spatial Node Density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a Node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first Node Density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

  • Modeling spatial Node Density in waypoint mobility
    2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), 2012
    Co-Authors: Bruno A. A. Nunes, Katia Obraczka
    Abstract:

    This paper introduces a modeling framework to analyze spatial Node Density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a Node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first Node Density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

Dharma P. Agrawal - One of the best experts on this subject based on the ideXlab platform.

  • MASS - Lifetime Enhancement of Wireless Sensor Networks by Differentiable Node Density Deployment
    2006 IEEE International Conference on Mobile Ad Hoc and Sensor Sysetems, 2006
    Co-Authors: Demin Wang, Yi Cheng, Yun Wang, Dharma P. Agrawal
    Abstract:

    A wireless sensor network (WSN) is composed of wireless sensors using batteries with energy constraints, which limits the network life-time. How to maximize the network lifetime is an important issue in the design of WSNs. We establish a relationship between Node Density and network lifetime in a periodically data delivery scenario of WSNs with sensors transmitting data to the sink Node periodically. Based on our analysis, we propose a sensor Node Density deployment method that could implement a differential Node Density in WSNs so that the lifetime of the network could be maximized. Both theoretic analysis and simulation results show that our method outperforms the uniform Node distribution method in terms of the network lifetime.

  • Lifetime Enhancement of Wireless Sensor Networks by Differentiable Node Density Deployment
    2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2006
    Co-Authors: Demin Wang, Yi Cheng, Yun Wang, Dharma P. Agrawal
    Abstract:

    A wireless sensor network (WSN) is composed of wireless sensors using batteries with energy constraints, which limits the network lifetime. How to maximize the network lifetime is an important issue in the design of WSNs. We establish a relationship between Node Density and network lifetime in a periodically data delivery scenario of WSNs with sensors transmitting data to the sink Node periodically. Based on our analysis, we propose a sensor Node Density deployment method that could implement a differential Node Density in WSNs so that the lifetime of the network could be maximized. Both theoretic analysis and simulation results show that our method outperforms the uniform Node distribution method in terms of the network lifetime