GFDA: Route Discovery Algorithms for On-demand Mobile Ad Hoc Routing Protocols

Alhosban, Amal; Ababneh, Ismail; Malik, Zaki

doi:10.1016/j.procs.2012.06.013

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…A reactive AODV ( ad hoc on-demand vector) protocol is used to determine routing and anomaly conditions. The AODV algorithm determines a destination sequence number to evaluate a destination route [ 24 ]. The SMTRPM was tested with five patient nodes per room.…”

Section: Smtrpm Designmentioning

confidence: 99%

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

González

Villegas

Torres-Ramírez

et al. 2014

Sensors

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Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

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Section: Smtrpm Designmentioning

confidence: 99%

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

González

Villegas

Torres-Ramírez

et al. 2014

Sensors

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“…Amal Alhosban et al [12], proposed that a node forwards the packets on the basis of the degree of its neighbor nodes. The degree of nodes is defined as the number of its neighbors.…”

Section: Related Workmentioning

confidence: 99%

Route discovery in Mobile Ad hoc Network using an optimum angle selection based approach

Ranjan

Velusamy

2014

2014 International Conference on Computer and Communication Technology (ICCCT)

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In Mobile Ad hoc Networks, routes are drastically discovered by broadcasting the control packets. Broadcasting of these packets is useful, but results in high routing packet overhead. In this paper, a route discovery mechanism using an optimum angle selection based approach is proposed in order to improve the network performance by minimizing the flooding of control packets, result in less routing overhead. The proposed solution selectively floods the control packets through a limited set of nodes in the network. The limited set of nodes is chosen based on their position and angle they are forming with respect to the previous node along the path. The proposed technique is termed as OAS-AODV (Optimum Angle Selection -AODV) which tested using Ns-2 simulation. The performance of the proposed algorithm is evaluated using various performance parameters. The simulation results show that the proposed technique drastically reduces the routing packet overhead and improve the routing performance.

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“…Perhaps as a reflection of the growth of the ridesharing industry and of the aforementioned challenges, empirical research in this area is also experiencing a surge, exemplified by a growing number of journal publications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] that explore the multidimensional challenges and opportunities produced by the widespread adoption of ridesharing services.…”

Section: Introductionmentioning

confidence: 99%

“…There have been a number of studies in the last few years taking aim at understanding ride-sharing services and carpooling schemes -each of which takes a different investigative position on the challenges faced by resource pooling services as a whole [1,22], while some consider some facet of modeling behaviors [3] using agent-based approaches. The majority of papers reviewed were of modeling carpooling decisions as an optimization problem [7,11,12] and finally, some approaches intended to make early-stage predictions about carpooling and ridesharing trends [6,9] were pre-existent in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…al. [3] provided a full description of a hypothetical agentbased model for a carpooling application without offering an actual build of the model hypothesized. The authors focused on the systemic theoretical structure of the proposed model, the mathematics and optimization techniques that would be used and the general form of social network types that could be used between the driver agents of said model.…”

Section: Introductionmentioning

confidence: 99%

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Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

Shaheen

2019

Understanding Complex Systems

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The ridesharing economy is experiencing rapid growth and innovation. Companies such as Uber and Lyft are continuing to grow at a considerable pace while providing their platform as an organizing medium for ridesharing services, increasing consumer utility as well as employing thousands in part-time positions. However, many challenges remain in the modeling of ridesharing services, many of which are not currently under wide consideration. In this paper, an agent-based model is developed to simulate a ridesharing service in the Washington D.C. metropolitan region. The model is used to examine levels of utility gained for both riders (customers) and drivers (service providers) of a generic ridesharing service. A description of the Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided, as well as a description of a typical simulation run. We investigate the financial gains of drivers for a 24-hour period under two scenarios and two spatial movement behaviors. The two spatial behaviors were random movement and Voronoi movement, which we describe. Both movement behaviors were tested under a stationary run conditions scenario and a variable run conditions scenario. We find that Voronoi movement increased drivers' utility gained but that emergence of this system property was only viable under variable scenario conditions. This result provides two important insights: The first is that driver movement decisions prior to passenger pickup can impact financial gain for the service and drivers, and consequently, rate of successful pickup for riders. The second is that this phenomenon is only evident under experimentation conditions where variability in passenger and driver arrival rates are administered.

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GFDA: Route Discovery Algorithms for On-demand Mobile Ad Hoc Routing Protocols

Cited by 3 publications

References 20 publications

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

Route discovery in Mobile Ad hoc Network using an optimum angle selection based approach

Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

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