An intelligent model to control preemption rate of instantaneous request calls in networks with book- ahead reservation
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush , Islam, Farzana
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 Australasian Telecommunication Networks and Applications Conference, ATNAC, Adelaide 2008. Published in Proceedings of Australasian Telecommunication Networks and Applications conference , IEEE (pp.344-34)
- Full Text: false
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- Description: Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rat
Rerouting in advance for preempted IR calls in QoS-enabled networks
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
- Date: 2008
- Type: Text , Journal article
- Relation: Computer Communications Vol. 31, no. 17 (2008), p. 3922-3928
- Full Text: false
- Reviewed:
- Description: When network resources are shared between Instantaneous Request (IR) and Book-Ahead (BA) connections, activation of future BA connections may cause preemption of on-going IR connections due to resource scarcity. Rerouting of preempted calls via alternative feasible paths is often considered as the final option to restore and maintain service continuity. Existing rerouting techniques, however, do not ensure acceptably low service disruption time and suffer from high failure rate and low network utilization. In this work, a new rerouting strategy is proposed that estimates the future resource scarcity, identifies the probable candidate connections for preemption and initiates the rerouting process in advance for those connections. Simulations on a widely used network topology suggest that the proposed rerouting scheme achieves a higher successful rerouting rate with lower service disruption time, while not compromising other network performance metrics like utilization and call blocking rate.
Application of artificial intelligence to improve quality of service in computer networks
- Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
- Date: 2012
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol. 21, no. 1 (2012), p. 81-90
- Full Text: false
- Reviewed:
- Description: Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.
Green underwater wireless communications using hybrid optical-acoustic technologies
- Authors: Islam, Kazi , Ahmad, Iftekhar , Habibi, Daryoush , Zahed, M. , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 85109-85123
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- Description: Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions. © 2013 IEEE.